E. coli suspension (E. coli group) or saline (CON group) was injected into the uterine horns. Eight days later, severe acute endometritis developed in the E. coli group. Endometrial explants were incubated with noradrenaline and/or α1-, α2- and β-AR antagonists. In the CON group, noradrenaline did not significantly change PTGS-2 and mPTGES-1 protein expression and increased PGE2 secretion compared to the control values (untreated tissue). In the E. coli group, both enzyme expression and PGE2 release were stimulated by noradrenaline, and these values were higher versus the CON group. The antagonists of α1- and α2-AR isoforms and β-AR subtypes do not significantly alter the noradrenaline effect on PTGS-2 and mPTGES-1 protein levels in the CON group, compared to noradrenaline action alone. In this group, α1A-, α2B- and β2-AR antagonists partly eliminated noradrenaline-stimulated PGE2 release. Compared to the noradrenaline effect alone, α1A-, α1B-, α2A-, α2B-, β1-, β2- and β3-AR antagonists together with noradrenaline reduced PTGS-2 protein expression in the E. coli group. Such effects were also exerted in this group by α1A-, α1D-, α2A-, β2- and β3-AR antagonists with noradrenaline on mPTGES-1 protein levels. In the E. coli group, the antagonists of all isoforms of α1-ARs and subtypes of β-ARs as well as α2A-ARs together with noradrenaline decreased PGE2 secretion versus noradrenaline action alone. Summarizing, in the inflamed pig endometrium, α1-, α2- and β-ARs mediate the noradrenaline stimulatory effect on PTGE-2 protein expression, while noradrenaline via α1-, α2A- and β-ARs increases mPTGES-1 protein expression and α1-, α2A- and β-ARs are involved in PGE2 release. Data suggest that noradrenaline may indirectly affect the processes regulated by PGE2 by influencing its production. Pharmacological modulation of particular AR isoforms/subtypes can be used to change PGE2 synthesis/secretion to alleviate inflammation and improve uterine function.In the inflamed uterus, the production and secretion of prostaglandins (PGs) and noradrenergic innervation pattern are changed. Receptor-based control of prostaglandin E2 (PGE2) production and secretion by noradrenaline during uterine inflammation is unknown. The aim of this study was to determine the role of α1-, α2- and β-adrenoreceptors (ARs) in noradrenaline-influenced PG-endoperoxidase synthase-2 (PTGS-2) and microsomal PTGE synthase-1 (mPTGES-1) protein levels in the inflamed pig endometrium, and in the secretion of PGE2 from this tissue.  Endometritis and metritis are common and serious diseases in animals which lead to disturbances in reproductive processes and economic losses ,2. BacteUterine inflammation causes considerable changes in the rate of synthesis and secretion of many inflammatory mediators, including prostaglandins (PGs). In cows, high contents of PGE2 next to PGF2α were revealed in the uterine fluid during pyometra  and of PCatecholamines, including noradrenaline and adrenaline, act through adrenoreceptors (ARs) belonging to the seven transmembrane G protein-coupled receptor superfamily. There are two classes of AR: α and β. The α-AR class is divided into two subtypes: α1 and α2. Among these receptor subtypes, the following isoforms are identified: α1 A, B, D and α2 A, B, C. The β-AR class consists of three subtypes: 1, 2 and 3 . The mosIn the scope of the inflamed uterus, it is known that endometritis in pigs caused marked alterations in the uterine noradrenergic innervation pattern ,33 and ATherefore, the objectives of this study was to determine the influence of noradrenaline alone or together with antagonists of α1-, α2- and β-ARs on 1) the PTGS-2 and mPTGES-1 protein levels in the inflamed pig endometrium and 2) the secretion of PGE2 from this tissue.Escherichia coli (E. coli) group (gilts with E. coli injections into the uterus), noradrenaline increased the enzyme level, and it was higher versus the control value (p < 0.01), after using AR antagonists for: α1A (p < 0.001), α1B (p < 0.05), α1D (p < 0.001), α1ABD, α2A (p < 0.01), α2B, α1C, α2ABC (p < 0.001), β1 (p < 0.01), β2 (p < 0.05) and β3 (p < 0.01). In this group, the PTGS-2 levels were elevated in the untreated tissue (p < 0.05), in response to noradrenaline (p < 0.001), α1ABD-, α2A-, α2B- and β1-AR antagonists (p < 0.05) compared to the CON group.In the endometrium of the control (CON) group , the PTGS-2 protein levels did not differ significantly between the control value (untreated tissue) and in response to noradrenaline and all AR antagonists that were tested . In the p < 0.001) in the E. coli group (p < 0.05) and after using noradrenaline (p < 0.001) was higher than in the CON group.Noradrenaline: Compared to the control values, noradrenaline did not significantly change the endometrial PTGS-2 protein level in the CON group, while it increased  and α1ABD (p < 0.01)-AR antagonists with noradrenaline than following added noradrenaline and α1D-AR antagonist with noradrenaline. Under the influence of α1ABD-AR antagonist with noradrenaline, the PTGS-2 expression was higher (p < 0.05) versus the control value and lower (p < 0.01) versus noradrenaline and α1D-AR antagonist with noradrenaline actions. Compared to the CON group, in the E. coli group endometrium, the PTGS-2 expression was increased after treatment with α1D- (p < 0.001) and α1ABD (p < 0.01)-AR antagonists with noradrenaline.α1-AR antagonists with noradrenaline: In the CON group, the use of α1-AR antagonists in combination with noradrenaline did not result in a significant change in PTGS-2 protein levels relative to the control value or the effect of noradrenaline .The valuE. coli group, the PTGS-2 amounts were decreased (p < 0.001) in relation to the effects of noradrenaline and α2B-AR antagonist with noradrenaline. The enzyme expression evoked by α2B-AR antagonist with noradrenaline was higher (p < 0.001) than the control value. In the E. coli group, compared to the CON group, the PTGS-2 levels were increased after using α2B- (p < 0.001) and α2ABC (p < 0.05)-AR antagonists with noradrenaline.α2-AR antagonists with noradrenaline: All applied α2-AR antagonists with noradrenaline did not significantly alter the PTGS-2 protein expression in the CON group versus the control value and noradrenaline action . After uE. coli group was reduced by β1- and β2-AR antagonists with noradrenaline compared to the effects of noradrenaline (p < 0.001) and β3-AR antagonist with noradrenaline (p < 0.01). This enzyme level was increased by β3-AR antagonist with noradrenaline versus the control value (p < 0.05) and decreased (p < 0.05) in relation to the noradrenaline effect. In the E. coli group, the PTGS-2 protein level was higher in response to β3-AR antagonist with noradrenaline (p < 0.01) than in the CON group.β-AR antagonists with noradrenaline: In the CON group, all used β-AR antagonists with noradrenaline did not significantly change the PTGS-2 protein levels versus the control value and noradrenaline influence . The PTGE. coli group, increased (p < 0.05) the mPTGES-1 expression compared to the control value and following the administration of all AR antagonists, except for α2A- and β2-ARs. The enzyme levels in the E. coli group were significantly higher in response to noradrenaline (p < 0.001) and α1ABD- and α2A-AR antagonists (p < 0.05).In the CON group, the endometrial mPGES-1 protein expression did not differ significantly between the control value and after noradrenaline, and all used AR antagonist applications . Noradrep < 0.001) this enzyme level in the E. coli group, and it was higher (p < 0.001) than in the CON group , α1D- and α1ABD (p < 0.01)-AR antagonists with noradrenaline reduced the enzyme expression compared to noradrenaline and α1B-AR antagonist with noradrenaline influences. α1B-AR antagonist with noradrenaline increased (p < 0.001) the mPTGES-1 level versus the control value. The amount of mPTGES-1 in the E. coli group increased (p < 0.001) in response to the α1B-AR antagonist with noradrenaline compared to the CON groupα1-AR antagonists with noradrenaline: In the CON group, all applied α1-AR antagonists with noradrenaline had no significant effect on the mPTGES-1 protein levels versus the control value and noradrenaline action . In the E. coli group, the mPTGES-1 protein levels were higher after using α2A- (p < 0.01) and α2ABC (p < 0.001)-AR antagonists with noradrenaline than in response to noradrenaline, α2B- and α2C-AR antagonists with noradrenaline. The mPTGES-1 expression was higher (p < 0.001) after using α2B- and α2C-AR antagonists with noradrenaline versus the control value. In this group, the mPTGES-1 amounts were increased (p < 0.001) by α2B- and α2C-AR antagonists with noradrenaline compared to the CON group.α2-AR antagonists with noradrenaline: In the CON group, all used α2-AR antagonists with noradrenaline did not induce significant changes in mPTGES-1 expression compared to the control value and noradrenaline effect . In the E. coli group were lowered (p < 0.001) by β2- and β3 -AR antagonists with noradrenaline versus noradrenaline and β1-AR antagonist with noradrenaline effects. Compared to the control value, the mPTGES-1 level was increased (p < 0.001) by β1-AR antagonist with noradrenaline. In the E. coli group, this enzyme expression was elevated by β1- (p < 0.001) and β2 (p < 0.05)-AR antagonists with noradrenaline versus the CON group.β-AR antagonists with noradrenaline: In the CON group, all applied β-AR antagonists with noradrenaline had no significant action on the mPTGES-1 protein levels versus the control value and noradrenaline action . This enp < 0.01), and after using AR antagonists for: α1A (p < 0.05), α1B, α1D, α1ABD (p < 0.01), α2B, α1C, α2ABC (p < 0.05), β1, β2 and β3 (p < 0.01)  and evoked by AR antagonists for: α1A, α1B (p < 0.001), α1D, α1ABD, α2A, α1C, α2ABC (p < 0.01), β1, β2 and β3 (p < 0.001). In this group, noradrenaline elevated the PGE2 release more (p < 0.01) than in the CON group.The CON group endometrium secreted more PGE2 in response to noradrenaline versus the control value ( < 0.01) . In the p < 0.01) and E. coli (p < 0.001) groups versus the control values (p < 0.01) than in the CON group.Noradrenaline: The neurotransmitter increased the PGE2 release in the CON  and α1ABD (p < 0.05)-AR antagonists with noradrenaline was lower versus the effects of NA, α1B- and α1D-AR antagonists with noradrenaline (p < 0.01) than the control value. In the E. coli group, in response to α1A-, α1B-, α1D- and 1ABD-AR antagonists with noradrenaline, the PGE2 secretion was lower (p < 0.01) versus noradrenaline effect, and higher (p < 0.05) than the control value. Compared to the CON group, the PGE2 release in the E. coli group was increased (p < 0.01) by α1A-AR antagonist with noradrenaline.α1-AR antagonist with noradrenaline: In the CON group, the PGE2 release by α1A-  by α2B- and α2ABC-AR antagonists with noradrenaline versus noradrenaline influence (p < 0.05) the PGE2 secretion compared to the control value. In the E. coli group, the PG release by α2A- and α2ABC-AR antagonists with noradrenaline was lower (p < 0.01) than noradrenaline action. The PGE2 secretion by α2A- (p < 0.01), α2B-, α2C- (p < 0.001) and α2ABC (p < 0.01)-AR antagonists with noradrenaline was greater compared to the control value. Larger (p < 0.01) PGE2 amounts were released in the E. coli group by α2B- and α2C-AR antagonists with noradrenaline versus the CON group.α2-AR antagonists with noradrenaline: In the CON group endometrium, the PGE2 secretion was reduced (nfluence . α2A-AR p < 0.01) PGE2 in response to β2-AR antagonist with noradrenaline versus noradrenaline and β1-AR antagonist with noradrenaline effects (p < 0.01) and β3 (p < 0.05)-AR antagonists with noradrenaline compared to the control value. The PGE2 release in the E. coli group after treatment with β1-, β2- (p < 0.001) and β3 (p < 0.01)-AR antagonists with noradrenaline was higher than noradrenaline action. The PG secretion by β3-AR antagonist with noradrenaline increased (p < 0.05) compared to the control value. In the E. coli group, the PGE2 release by β1-AR antagonist with noradrenaline was lower (p < 0.01) than in the CON group.β-AR antagonists with noradrenaline: The CON group endometrium released more  of PTGS-2 expression in the ier data ,15. Noraier data  and PTGSier data . Similarier data .E. coli group) and PG release (both groups) were lower than the control values and in response to AR antagonists alone, except for the mPTGES-1 expression in response to α2A- and β2-AR antagonists in the E. coli group and the PGE2 release by α2A-AR antagonist in the CON group. The lack of significant effect of AR antagonists alone on PG secretion was revealed earlier in the rabbit gastric epithelial [Entirely new are also the present indications about the importance of ARs in the noradrenaline influence on the synthesis and secretion of PGE2 from the healthy and inflamed uterus. To determine which kinds of ARs mediate noradrenaline effects on the endometrial PTGS-2 and mPTGES-1 protein expression and PGE2 release, the antagonists of particular isoforms of α1- and α2-ARs and subtypes of β-ARs were used. It was found that the application of the antagonists alone did not significantly change the values of examined enzyme expression and PG secretion in both groups versus the control values. In relation to noradrenaline action alone, the protein expression of the enzymes , we detected that the stimulation of β1-, β2- and β3-ARs by noradrenaline increased PTGE-2 protein expression and PGE2 secretion and β2- and β3-ARs enhanced the mPTGES-1 protein expression. These findings are generally in line with the above reports, indicating the additional role of β3-ARs. In the E. coli group, we observed activation of β1- and β3-ARs in addition to β2-ARs in response to noradrenaline, leading to an increase in PGE2 release. This is likely a result of elevated β1-AR mRNA and protein expression in the inflamed endometrium used in our study [Literature data describing the receptor mechanisms of adrenergic stimulation in PGE2 synthesis/secretion during pathological states show that the activation of β2-ARs increased the PTGE-2 and PTGES expression and PGE2 release from human ovarian cancer cells . The parur study , as wellE. coli group endometrium. More precisely, we revealed that the noradrenaline stimulation of PTGS-2 protein expression involves α1A-, α1B-, α2A- and α2B-ARs, while α1A- α1D- and α2A-ARs are important for mPTGES-1 protein expression. All α1-AR isoforms and α2A-ARs are activated by noradrenaline to affect the PGE2 secretion. We also demonstrated that the partially different contribution of particular α-AR isoforms in noradrenaline influences this PG secretion in both studied groups . It should be added that in the inflamed endometrium, the mRNA/protein expression of α1D- and α2A-ARs was enhanced versus the healthy tissue [E. coli group endometrial explants could be due to changes in blood flow. It should also be added that downstream mechanisms of AR activation are not fully recognized. It was reported that noradrenaline could activate β2-ARs and transcriptionally activate PTGS-2 and PTGES via nuclear factor-kappaB (NF-kappaB) to produce PGE2 in human ovarian cancer cells [Earlier, the lack of involvement of α-ARs in the LPS-induced PTGE-2 protein expression in rat primary microglial cell culture  and in ty tissue . As was y tissue ,47 can by tissue ,50, the er cells . The inver cells . FurtherE. coli-infected uteri [E. coli, PGE2 production was increased via PTGS-2 and mPTGES-1, which led to the expression and secretion of pro-inflammatory mediators and damage-associated molecular patterns [E. coli-treated cow uterine explants [It is known that stressful factors activate the sympathetic nervous system which is linked to impaired female reproductive processes. Stress-related augmentation of circulating catecholamines may be able to promote inflammatory events. In the uterus, it may be related to PGE2 whose synthesis and secretion from the inflamed endometrium are elevated by noradrenaline (the present study). As mentioned earlier, the production/release of PGE2 increased in bovine ,9,10,51 ed uteri . It is kpatterns ,18. Pro-explants . Our stuIt should be added that large amounts of PGE2 are associated also with LPS-induced pathological initiation of preterm labor ,54. MoreOn the other hand, it is known that during inflammatory conditions, PGE2 acting locally together with PGI2 affects vascular permeability and pain generation . Thus, aSummarizing, antagonists of AR isoforms/subtypes indicated in the present study could be used in practice, in addition to currently administered drugs. As a result, it may contribute to more favorable outcomes in the prevention and treatment of uterine diseases associated with inflammation, particularly during the postpartum period, in animals as well as humans.2) at a temperature of 18 ± 2 °C and the following light conditions: natural daylight—14.5 ± 1.5 h, night—9.5 ± 1.5 h. They received a commercial diet and had access to water.The Local Ethics Committee for Experiments on Animals  approved all study procedures (Consent no. 65/2015). The guidelines in EU Directive 2010/63/EU for animal experiments were taken into account. The experiment was carried out on gilts . A tester boar was used to detect behavioral estrus. The study gilts did not exhibit reproductive disorders . The animals were transported from a commercial farm  to the local animal house  three days prior to testing (acclimatization period). The animals stayed in individual pens (an area of about 5 mE. coli (gilts with E. coli injections into the uterus).After the acclimatization period, the gilts were divided randomly (day 3 of the second estrous cycle—day 0 of the experiment) into two groups (each group consisted of 5 gilts): the CON  and the E. coli group, 50 mL of E. coli suspension  were injected into each uterine horn. In the CON group, 50 mL of saline solution was injected. Bacterial suspension and saline solution were injected into each horn in 5 places (10 mL/each place) at a similar distance from each other. All animals were untreated from the time of surgery to euthanasia. After eight days (the expected day 11 of the estrous cycle), the gilts were euthanized , and the uteri were collected.All study procedures were reported earlier . Most im2. After a 1.5 h preincubation period, the endometrial explants were treated, for 16 h, with fresh (control) medium or with the addition of noradrenaline alone  or AR antagonists alone (each at a dose of 10−4 M) for: α1A- , α1B- , α1D-  and α1ABD -ARs, α2A- , α2B- , α2C-  and α2ABC -ARs, β1- , β2-  and β3 -ARs. The endometrial explants were also treated with noradrenaline (10−5 M) together with particular antagonists (each at a dose of 10−4 M). All antagonists were obtained from Tocris Bioscience. Initial dilutions of the used antagonists were done according to the manufacturer’s instructions , and then stored at −20 °C. The final solutions of these agents, as well as the solution of noradrenaline, were prepared using the same medium for tissue preincubation and incubation. All treatments were performed in triplicate for each of the five gilts in the individual groups. A nitric oxide (NO) donor  was used at 10−4 M as a positive control. The doses of noradrenaline, antagonists and NONOate, as well as the time of incubation, were determined in the preliminary studies or according to our previous research. After incubation, the endometrial fragments were blotted with a paper filter and then weighed and stored at −80 °C until analyzed for the expression of PTGS-2 and mPTGES-1 proteins. The medium was placed into tubes with 5% EDTA , 1% acetylsalicylic acid  solution (pH 7.4), and frozen at −20 °C for further determination of PGE2 concentration.To perform in vitro experiment, the uteri were transported on ice to the laboratory (within 20 min) and then washed twice with sterile phosphate-buffered saline . A fragment of the uterine wall was collected from the middle part of each horn, and the endometrium was then separated from the myometrium by careful scraping using a scalpel blade. Afterwards, the endometrial fragments were cut and sliced (60–70 mg) and washed with Medium 199 . Single explants of the endometrium were placed into glass vials containing 2 mL of Medium 199 supplemented with 0.1% BSA  and antibiotics . The tissue fragments were preincubated and incubated in a shaking water bath at 37 °C in a humidified atmosphere of 95% air and 5% COTo estimate the expression of PTGS-2 and mPTGES-1 proteins in the endometrial explants, equal amounts of tissue fractions (20 μg) were dissolved in sodium dodecyl sulphate , a gel-loading buffer, heated  and separated by  SDS-polyacrylamide gel electrophoresis. Separated proteins were then electroblotted onto 0.2 µm pore size nitrocellulose membranes  in a transfer buffer. The nonspecific binding sites were blocked by incubation with 5% fat-free dry milk  in Tris -buffered saline Tween 20  buffer . After blocking, the nitrocellulose membranes were incubated  with polyclonal rabbit antibodies for PTGS-2  and mPTGES-1 , both from Cayman Chemical Co. Subsequently, the nitrocellulose membranes for PTGS-2 study expression were incubated  with biotinylated goat anti-rabbit IgG , incubated for 1 h, at 21 °C. Visualization of antibody binding was performed by incubation with a freshly prepared mixture of 3, 3`-diaminobenzidine tetrahydrochloride  and H2O2 in Tris-buffered saline (pH 7.2), for 3–4 min. In relation to mPTGES-1 expression, the nitrocellulose membranes were incubated  with secondary alkaline phosphatase-conjugated goat anti-rabbit antibody . Immune complexes were visualized using a standard alkaline phosphatase visualization procedure . Each analysis was repeated three times. To demonstrate the specificity of rabbit anti-PTGS-2 and anti-mPTGES-1 antibodies, specific binding peptides were used  as a negative control. After the neutralization of the primary antibodies for PTGS-2 and mPTGES-1, the bands were not found. The data for PTGS-2 were normalized in relation to the expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) using primary polyclonal rabbit anti-GAPDH antibody , while data for mPTGES-1 were normalized in relation to the expression of β-actin (ACTB) using primary polyclonal rabbit anti-ACTB antibody . Images were gained and quantified by the use of a Quantity-One system .The PGE2 concentration in the incubation medium was measured using the ELISA kits , following the manufacturer’s protocols. The standard curve for PGE2 ranged from 7.8 to 1000 pg/mL. The intra- and interassay coefficients of variation were 4.7% and 6.2%, respectively.p > 0.05) was confirmed by Shapiro–Wilk test. A two-way ANOVA followed by the Bonferroni test  was applied to compare the mean (±sem) values. The data are shown as mean (±sem) with differences accepted as statistically significant when p < 0.05.Only the results from the incubation of tissues for which the release of PGF2α in response to NONOate was statistically significant were considered. Normal distribution of all data -, α2- and β-ARs mediate in the noradrenaline stimulatory influence on the PTGS-2 protein expression, while α1-, α2A- and β-ARs are involved in this transmitter action on the mPTGES-1 protein expression. In turn, α1-, α2A- and β-ARs are activated by noradrenaline for the increase in PGE2 secretion. The above-mentioned ARs may play a crucial role in the effects of catecholamines on PGE2 production and secretion during spontaneous endometritis and metritis, affecting the severity of inflammatory symptoms and uterine function. Our results provide deeper insight into the pathomechanisms of bacterial endometritis, paving the way for the development of novel methods for the prevention and treatment of inflammatory diseases of uterus in both animals and humans.


According to the World Health Organization (WHO), musculoskeletal conditions are among the most common health problems, affecting approximately 1.71 billion people worldwide [During the last few years, there has been a remarkable intake of technology in musculoskeletal rehabilitation, notably with the development of serious games and their transfer to the clinic. Serious games in the case of musculoskeletal rehabilitation generally consist of systems that measure how the patients move and provide them with guidance to execute specific movements in a playful manner . They asMovement measurement is the first key component of serious games. It provides an objective biomechanical description of the movements executed, both in terms of quality and quantity. Cameras, inertial measurement units (IMUs), as well as electromyographic (EMG) and force sensors are the most commonly used devices to measure human movement in serious games ,8. Once With serious games, the biofeedback is brought to another level due to extended reality (XR), the second major technological component of serious games. XR is a general term covering virtual reality (VR), augmented reality (AR), and mixed reality (MR). All these types of extended realities have been used in musculoskeletal rehabilitation, particularly to provide more advanced instructions and feedback to the patients and place them in immersive and interactive environments. XR plays an important role in the communication with the patients, particularly for complex exercises and movements that should be executed precisely. A variety of devices can be used to deliver XR. Nevertheless, a display in the form of a screen, tablet, or headset is often considered because of its intuitive nature . In thisGamification is the third fundamental technological component of serious games. It consists of adding game aspects into the rehabilitation process, therefore improving motivation and involvement, which are critical for treatment success ,17,18. GSerious games have been booming lately, with highly promising results for a variety of musculoskeletal conditions. For instance, they have been shown to be effective in joint arthroplasty rehabilitation ,21,22,23Following the engaging state-ot-the-art advancements presented above, a limitation, or rather a perspective, should be highlighted. While current serious games for musculoskeletal conditions can be adapted by clinicians to the individual needs of the patients, there is a lack of support in doing so. In fact, currently, the adaptations are mostly based on the clinicians’ expertise. Similarly to the trend observed in other medical applications ,34,35, aIn conclusion, serious games are extremely promising for the rehabilitation of musculoskeletal conditions and their value should significantly increase in the future with the incorporation of AI to suggest personalized therapeutic objectives and individualize the games. In view of the importance of musculoskeletal rehabilitation worldwide, serious games could play a major role in improving global health and lower the societal and economic burden of musculoskeletal disorders. Finally, it is worth mentioning that serious games can be deployed at home  and that


Background. Disability  might occur for the first time in a given patient after an acute clinical event. It is essential, whenever indicated, to undergo a Physical Medicine and Rehabilitation assessment to detect disability and any need for rehabilitation early. Although access to rehabilitation services varies from country to country, it should always be governed by a PRM prescription. Objective. The aim of the present observational retrospective study is to describe consultancy activity performed by PRM specialists in a university hospital in terms of requests’ typology, clinical questions, and rehabilitation setting assignment. Methods. Multiple parameters were analyzed  and a correlation analysis was performed between the analyzed characteristics and both the different clinical conditions and the assigned rehabilitation setting. Results. PRM evaluations of 583 patients from 1 May 2021 to 30 June 2022 were examined. Almost half of the total sample (47%) presented disability due to musculoskeletal conditions with a mean age of 76 years. The most frequently prescribed settings were home rehabilitation care, followed by intensive rehabilitation and long-term care rehabilitation. Conclusions. Our results suggest the high public health impact of musculoskeletal disorders, followed by neurological disorders. This is, however, without forgetting the importance of early rehabilitation to prevent other types of clinical conditions such as cardiovascular, respiratory, or internal diseases from leading to motor disability and increasing costs. Rehabilitation consists of “a set of measures that assist individuals, who experience, or are likely to experience, disability to achieve and maintain optimal functioning in interaction with their environments” .From a health system perspective, rehabilitation, together with disease prevention, health promotion, disease control, and supportive and palliative care, represents one pillar of health strategies .There is an increasing need for rehabilitation worldwide due to the epidemiological shift from communicable to noncommunicable diseases, rapid global population aging, the rise in physical and mental health challenges, injuries, and comorbidities, and new rehabilitation needs related to infectious diseases such as coronavirus disease. Nevertheless, rehabilitation represents a largely unmet need globally, considering that in many countries, more than 50% of people do not receive rehabilitation services .Although access to rehabilitation services varies from country to country and often within the same country, even by region, it should always be governed by a Physical Medicine and Rehabilitation (PRM) prescription . This isTo date, the literature on the most important rehabilitative prognostic factors to be searched in order to assess the proper rehabilitation setting is scant; the few available evidence comes from experts’ opinions derived from a Delphi consensus study ,7.The need to identify factors affecting access to different rehabilitation settings  has been the subject of several studies over the years.Ottenbacher et al. has already postulated the relevance of “non-clinical factors” in playing a key role in decision making for PAC access. In fact, they categorize barriers to access into four categories: financial, structural, personal and sociodemographic, and attitudinal . All fouMore recently, other authors have tried to identify clinical assessment schedules (CLAS) in which functioning data are collected to guide and inform the decision-making process in clinical practice and service provision . A studyIn Europe, other nations have drawn up national documents to define pathologies requiring rehabilitation and to regulate access to rehabilitation at a national level, as in the case of France .As regards Italy, appropriateness for rehabilitation should be regulated by Ministerial national guidelines which, however, are currently being tested nationwide . For thiIn order to ensure an effective continuity of the rehabilitation pathway to a patient with a disability susceptible to improvement, a connection between the acute, postacute, and territorial phases is essential. In Italy, inpatient rehabilitation is divided into two areas: intensive rehabilitation and the highly specialized intensive rehabilitation in its various fields . A systematic connection among the different care settings  guarantees a global intervention with a complete taking care of the patient, timeliness in rehabilitation treatment, and a greater appropriateness in resource allocation. The transfer from an acute care ward to a hospital or territorial rehabilitation unit must be planned through a joint assessment by the acute ward and PRM specialists; it is fundamental to perform a multidimensional evaluation based on clinical conditions, the level of disability, and relying on validated assessment scales .Furthermore, to maximize the effectiveness of PRM evaluations in acute wards and to ensure early taking care of the patient, it is essential that this evaluation occurs as early as possible. Several studies in the literature have reported an association between early admission to rehabilitation care and improved functional outcomes ,16,17.The delay in PRM consultation may be due to multiple factors. Among them could be considered the acute ward specialist’s difficulty in timely intercepting the patient’s rehabilitation needs, the high demand, and the difficulty to provide all evaluations in a brief time.The aim of the present observational retrospective study is to describe consultancy activity performed by PRM specialists in a university hospital, in terms of requests’ typology, clinical questions, and the final decision about rehabilitation setting assignment after an acute ward to better improve our healthcare system in terms of rehabilitation services. The analysis of the main parameters during PRM evaluation is aimed at standardizing as much as possible the activity carried out in view of a correct rehabilitation setting assignment.The present work is an observational retrospective study. It was conducted at the “Policlinico Umberto I” of Rome, the third largest Italian hospital with 1235 beds and approximately 41,000 hospitalizations per year. In our hospital, patients—hospitalized in acute wards—requiring rehabilitation are reported to our department through a request for PRM assessment, which is performed at acute wards by a PRM physician who provides a report on the patient’s medical record.The study was conducted in accordance with the Helsinki Declaration (in its latest version).We examined PRM assessments performed over a period of time ranging from 1 May 2021 to 30 June 2022 by analyzing anamnestic data, clinical and social/housing conditions, the scores of specific assessment scales, the Barthel Index ,19,20 anThe number of PRM evaluation requests received by our department may vary according to the surgical activity of each department  and it may undergo periodic variations (fewer requests in July–August), reaching an average of about 60 requests per month. Over the indicated time frame, approximately 780 PRM consultancies were requested, corresponding to 1 PRM evaluation for every 70 hospitalized patients . Departments requiring the greatest number of PRM evaluations were Neurology and Orthopedics. In our hospital, the Orthopedics department has 30 beds. As regards Neurology, however, the Nervous Diseases department is divided into different wards: Cognitive Neurorehabilitation with 8 beds, Sub-Intensive Neurology with 7 beds, Neurology with 16 beds, and the Stroke unit with 8 beds.Each PRM evaluation request received by our department was considered eligible. No selection criteria were applied.The PRM physician team included medical specialists and PRM postgraduates of “Sapienza” University of Rome.Multiple parameters that might affect the rehabilitation setting choice were analyzed, such as the clinical condition underlying acute hospitalization, patient’s socio-family background, and rehabilitation assessment scale scores. Subsequently, a correlation analysis was performed between the analyzed characteristics and both the different clinical conditions and the assigned rehabilitation setting.Specifically, we investigated how assessment scales scores vary after acute events such as orthopedic intervention, stroke, exacerbation of an internal disease, and their distribution according to the assigned rehabilitation setting.In addition, for each PRM evaluation, the time—in terms of number of days—elapsed between the patient’s hospitalization and the request for evaluation was assessed to analyze the priority for rehabilitation treatment in relation to the clinical condition underlying hospitalization.As variability of judgment among the physicians might have arisen, PRM physicians were asked to try to minimize any observer-related bias, keeping a uniform standard of judgment. The purpose of this data collection was to precisely identify the parameters required to standardize PRM evaluations as much as possible, thereby avoiding evaluation bias.Data derived from PRM evaluations were collected in a database. An observational analysis was carried out to evaluate the distribution of different characteristics  according to patients’ diagnosis  and rehabilitation setting. The time elapsed between the day of admission to the ward and PRM evaluation request was analyzed using the median statistical index. We analyzed data of 583 consultations performed over a period ranging from 1 May 2021 to 30 June 2022. Currently, further data are being gathered. We initially collected PRM evaluations carried out in “clinical condition” groups according to the Italian classification of Major Diagnostic Categories (MDC). We then analyzed the distribution of the characteristics collected in patients with disabilities of musculoskeletal (MSK), neurological, respiratory, and cardiologic origin, merging data relating to other patients suffering from internal diseases into a single category under the name of “Other” conditions.Parameters such as the number of cases, sex, mean age, Barthel Index, and CIRS were related to the underlying clinical condition.We analyzed 583 PRM evaluations in total  with a female mean age of 76 years and a male mean age of 69. The mean BI was 20 for female assessments and 19 for male assessments, while CIRS was ≥3 in 48%  and 41% . In Patients with MSK conditions exhibited a mean age of 76 years and a mean BI of 21, and 47% of them reported a CIRS score ≥ 3. Patients with neurological disabilities showed a mean age of 67 years and a mean BI of 15, and 37% of cases reported a CIRS score ≥ 3. Patients with respiratory disabilities displayed a mean age of 75 years with a mean BI of 25, and in 38% of cases, they reported a CIRS score ≥ 3. Patients with cardiologic disabilities presented a mean age of 79 years, a mean BI equal to 23, and a CIRS score ≥ 3 in 66% of cases. Patients suffering from internal diseases included in the “Other” category showed a mean age of 72 years, with a mean BI equal to 22 and a CIRS score ≥ 3 in 48% of cases.Next, as reported in We then analyzed the most frequently occurring diseases (according to ICD-9) during PRM evaluation activity, which are shown in order of frequency in Patients with femoral neck fractures  presenteThis analysis was carried out only for the most frequently occurring clinical conditions (>5 consultations); therefore, the cases shown in The proposed rehabilitation setting in relation to each clinical condition underlying hospitalization is depicted in The most frequently assigned rehabilitation setting  was homeIn The median number of days elapsed between the admission of each patient to the acute ward and the request for PRM evaluation is shown in It can be noticed that the time was longer in the category of patients with cardiovascular disabilities (22.5 days), followed by patients with respiratory diseases (20.5 days). On the other hand, it was shorter for patients with MSK diseases (6 days), neurological patients (12 days), and for the ones in the “Other” category (18 days). Since it was not possible to obtain these data for all the sampled patients, the total number of cases reported in the table below is lower than the total number of patients included in the present study (516 vs. 583). In order to assess quality levels, the timeliness of the rehabilitation treatments, and the appropriateness of access for various conditions, we analyzed consultation requests received by the PRM department of “Policlinico Umberto I” Hospital.Data related to 583 PRM evaluations were collected and analyzed retrospectively, over a timeframe ranging from 1 May 2021 to 30 June 2022.It should be emphasized that this study took place during the pandemic period caused by COVID-19, which had repercussions on acute care activity and, consequently, on rehabilitation ,28. Due Most of the PRM evaluations were carried out on patients with disabilities of MSK origin, followed by those with disabilities of neurological origin; this suggests that these clinical conditions are still among those of greatest interest in rehabilitation. PRM evaluations have been carried out in multiple internal medicine departments and in intensive care units ,30. The The analysis of the collected parameters—including the number of cases, average age, BI, and CIRS by clinical condition—shows that the largest patient group was composed of subjects suffering from MSK conditions, with a mean age of 76 years. These patients presented, on average, a lower BI compared to patients with cardiovascular and respiratory diseases. Conversely, patients with neurological disorders had a lower mean age and, compatibly with the consequences that may result from an event such as a stroke or a brain trauma, they showed a mean BI lower than other categories, scoring a BI between 0 and 24 in almost 75% of cases. Although the epidemiology of brain ischemia and cardiovascular diseases is comparable, it has to be noted that the “neurological disorders” category also includes head and spinal traumas, which generally tend to affect young subjects.We then analyzed the single clinical conditions most frequently encountered during the PRM consultations. MSK and neurological disorders were the most frequent, with femoral neck fractures  being thAs regards the settings, the most frequently proposed were home rehabilitation in 168 cases, intensive rehabilitation in 141 cases, and long-term care in 130 cases. Patients assigned to intensive rehabilitation presented a lower age than patients assigned to home rehabilitation  or long-It is worth presenting the data gathered on the time elapsed between the hospitalization in ward and the request for PRM consultation . RemarkaThe overall findings of the present report firstly indicate that PRM counseling is crucial to ensure the appropriateness of rehabilitation programs. Secondly, it emerges that the PRM specialist is the only medical specialist able to globally take care of the patient with disabling outcomes of any clinical condition and along the entire course of the disease, from the acute phase  to the fIn fact, the PRM specialist provides consultation as early as possible from the acute event and, if there are indications for rehabilitation treatment, performs diagnoses and defines rehabilitation programs. Additionally, the PRM specialist globally evaluates the patient’s clinical, functional, and social conditions and the degree of compliance with rehabilitative treatment, identifying the most suitable rehabilitation pathway and the most appropriate settings, taking into account the rehabilitation intensity and the healthcare support needed by the patient. Hence, the patient undergoes a holistic examination, no longer focusing only on the acute problem leading to hospitalization but also deeply evaluating the social and environmental factors, in order to prevent hospitalization and to promote home stay .A reduction in the time between hospitalization and PRM consultation request may be highly advantageous in order to ensure high quality and timeliness in their rehabilitation treatment and to minimize the rate of inappropriate access ,39. IndeThe definition of the rehabilitation process must consider different intervention settings  based on the assessment of the patient’s overall function and needs. From this perspective, the design and implementation of the proposed rehabilitation interventions are not only based on the clinical condition; rather, they need to be shaped according to the individuality of patients and to the contexts in which they express their social functioning. In this regard, one of the strategies to be mentioned is telerehabilitation, an innovative approach which ensures rehabilitative intervention even in home settings, especially in the pandemic period .From the analysis carried out, it emerged that the most frequently proposed setting for orthopedic and neurological rehabilitation was intensive rehabilitation, regardless of the clinical condition causing the disabling outcomes. In contrast, with regard to internal disease and respiratory or cardiological diseases, the assigned setting was home rehabilitation.The strengths of the present study are that it is original, innovative, and it develops a topic not previously analyzed in the literature. It presents a large and heterogeneous sample of patients, with a relevant variety of clinical conditions included.Some limitations must be described. Being a retrospective observational study, it analyzes the entire sample without applying selection criteria to the patient pool. It is not a multicentric study. The results have not been compared with other hospitals’ data; nevertheless, it has to be considered that “Policlinico Umberto I” Hospital of Rome is a reference center, presenting a wide spectrum of clinical conditions to be analyzed. Another limitation of this study is the lack of data on the patients’ follow-up for evaluating the effectiveness of the proposed settings. However, although it is a local study, it clearly shows that in the rehabilitation field, the treatment pathway and the individual rehabilitation project are not defined based only on the impairment; rather, they are designed according to the rehabilitation diagnosis related to the patient’s function and needs.This is the first Italian study conducted on a large scale in relation to physiatric medical consultations. Having been conducted in one of the largest hospitals in Italy, it is likely to expect that these results could be useful at an international level to better understand the type of patients who need physiatric consultation and what are the specific characteristics that guide the physiatrist in expressing the rehabilitative diagnosis and prognosis. The present research could also provide general indications for the purpose of resource planning and allocation, to be defined on the basis of the analysis of epidemiological data of patients admitted to the structures of interest.PRM assessments performed for acute care inpatients are very important in several respects. Firstly, it should be remembered that rehabilitation diagnosis is pivotal for determining the patient’s rehabilitation treatment pathway, starting from the very first stages of the clinical condition for which the patient is hospitalized. Secondly, early definition of the individual rehabilitation project allows one to quickly identify the most appropriate rehabilitation setting for each patient from the time of discharge from the hospital ward. It is crucial to ensure the timely global taking care of the patient, through an early collaboration between the acute ward and the PRM specialists, to improve the outcome and reduce disability.There are no patents resulting from the work reported in this manuscript.


To address the rehabilitation needs of upper limb hemiplegic patients in various stages of recovery, streamline the workload of rehabilitation professionals, and provide data visualization, our research team designed a six-degree-of-freedom upper limb exoskeleton rehabilitation robot inspired by the human upper limb’s structure. We also developed an eight-channel synchronized signal acquisition system for capturing surface electromyography (sEMG) signals and elbow joint angle data. Utilizing Solidworks, we modeled the robot with a focus on modularity, and conducted structural and kinematic analyses. To predict the elbow joint angles, we employed a back propagation neural network (BPNN). We introduced three training modes: a PID control, bilateral control, and active control, each tailored to different phases of the rehabilitation process. Our experimental results demonstrated a strong linear regression relationship between the predicted reference values and the actual elbow joint angles, with an R-squared value of 94.41% and an average error of four degrees. Furthermore, these results validated the increased stability of our model and addressed issues related to the size and single-mode limitations of upper limb rehabilitation robots. This work lays the theoretical foundation for future model enhancements and further research in the field of rehabilitation. The goal of rehabilitation is to improve deficits in motor control in patients through strengthening exercises for the affected limb. Traditional rehabilitation therapy relies on one-on-one training with a rehabilitator focusing on the relearning of lost or weakened functional movements , which cThe end-effector type rehabilitation training robot predominantly employs a series of mechanical structures or a linkage mechanism as its core framework. This system is securely affixed to the human arm through the end-effector. Furthermore, it exerts traction force on the extremities of the patient’s limbs, facilitating upper limb rehabilitation training ,5,6,7,8.Exoskeleton rehabilitation robots are wearable devices with mechanical structures that correspond to human joint movements. Unlike end-effector type upper limb rehabilitation robots , which hThe parameters of traditional controllers are fixed and cannot be adapted to changes in the environment or tailored to the patient’s condition. In their study, Islam et al. introduced a seven-degree-of-freedom rigid upper limb exoskeleton robot that effectively stabilized the patient’s rehabilitation movements . HoweverGiven the variations in the structure, control mechanisms, and specific treatment approaches employed in upper limb rehabilitation robots, the accurate provision of torque tailored to individual patient movements has emerged as a focal point in contemporary rehabilitation robotics research. The response of users to additional torque necessitates the precise implementation of algorithms. Achieving precise torque delivery requires the incorporation of key parameters, such as joint positions, velocities, accelerations, and kinetic modeling of the impaired limb, into the control system. Presently, upper limb exoskeleton rehabilitation robots are directed towards addressing the following challenges.Enhancing patient comfort and safety: Traditional gear-driven structures tend to be bulky. The implementation of wearable rehabilitation robots employing flexible and elastic materials can reduce the weight of upper limb devices, thereby improving patient comfort and safety.Diversifying rehabilitation training: Conventional systems often offer a limited range of motion, primarily focused on anterior movements, which might provide inadequate stimulation for central nervous system rehabilitation. A dynamic robot controller, adaptable to the patient’s specific rehabilitation needs and progress, along with the design of effective training modes and their corresponding hardware implementations, are critical components of robot-assisted rehabilitation.Addressing joint movement uncertainty: Controlling the movement of specific muscles during the joint movement process of the affected limb can be challenging. When patients experience reduced grip strength, it’s essential to select a structural model that is better suited for the arm and hand.Enhancing portability and versatility: Many exoskeleton rehabilitation robots are hindered by complex structures and limited portability, primarily due to the reliance on motor technology with inherent power constraints.To address this, portable multifunctional rehabilitation robots should aim to combine the advantages of both traditional and portable systems, all while ensuring a proper fit with the human body’s joint axes. An example of innovation in this direction is showcased in Harvard University’s work , which iThe upper limb rehabilitation robot in this study effectively addressed several key limitations, as follows.Enhancing patient safety by positioning the motor without direct contact with the body, eliminating the need for a traditional gear-driven structure and thereby reducing the weight of the upper limb device.Implementing multimodal control to better cater to the rehabilitation requirements of hemiplegic patients at various stages, ultimately reducing the workload of rehabilitators while enabling easier data acquisition and visualization.Mitigating joint movement uncertainty by implementing multiple safety measures throughout the entire system, ensuring the patient’s safety from software to hardware levels.Embracing a modular design for simplified disassembly and maintenance, with 3D printed components to enhance portability and provide a foundation for future personalized design enhancements.The rehabilitation process can be categorized into three primary phases, which depend on the time elapsed since the traumatic event. These phases include the acute, subacute, and chronic phases. During each of these phases, rehabilitation therapy should facilitate the gradual recovery of the range of motion and muscle strength in the injured limb. The control strategy employed by the robotic system should be adapted to the specific needs of each phase. In the initial phase, for instance, when the patient has lost a significant portion of arm function, the robot should assist the patient in following a predetermined trajectory to enhance limb mobility and mitigate muscle atrophy or tendon retraction.Traditional rehabilitation strategies have typically prioritized proximal limb joint rehabilitation, as nerve recovery often progresses from the proximal limb segment to the distal limb segment. However, this approach may limit opportunities for distal limb control using synapses. Bilateral coordinated movement of the upper limb is an effective neural remodeling method for stroke patients who generally suffer from unilateral hemiparesis. This method involves using the healthy limb to guide the rehabilitation training of the affected limb. When training hemiplegic patients, it is important to increase human–computer interaction as much as possible. This can enhance the motivation for training and visualize the training process.Unlike passive robots that constantly direct the movement of the affected limb, assistive robots provide assistance only when the patient intends to initiate the movement. Both passive and assistive modes are designed for the initial stages of stroke rehabilitation, when the patient may lack the necessary strength to independently move the affected limb. On the other hand, active and resistive modes are employed in the later phases of rehabilitation as they necessitate active movement from the patient.Facilitating a seamless human–robot interaction (HRI) that feels natural presents a significant challenge in the development of robotic rehabilitation systems. The chosen approach for detecting user intentions plays a pivotal role for ensuring a transparent and user-friendly HRI. Certain robotic rehabilitation platforms adopt bio-signals as a source for recognizing intentions ,27,28,29Despite the development of numerous prototypes, research on robotic devices for upper extremity rehabilitation remains a growing field and requires new approaches to address key limitations in hardware design. In this study, we have designed an upper extremity exoskeleton robot to tackle some of the key limitations in existing exoskeletons. We have also developed a myoelectric angle synchronization acquisition system to enable more accurate myoelectric control of joint movements. The main contributions of this study are as follows.The aim of this study was to conceptualize, analyze, construct, and validate a six-degree-of-freedom upper limb rehabilitation robot that seamlessly integrates EMG signal monitoring, PID control, and Unity3D simulation games. This comprehensive approach set a notable precedent for similar inquiries in the field of rehabilitation research.This paper devised an eight-channel sEMG acquisition system to facilitate passive control of the affected side using an elbow rehabilitator. The feasibility of sEMG-based elbow angle prediction was validated using a back propagation neural network (BPNN).In this work the interrelationships between bones, joints, muscles and other tissues of the human upper limb were analyzed. Based on this analysis and relevant clinical requirements, this paper proposed an overall design scheme for the mechanical structure of an upper limb hemiplegic rehabilitation training robot.The human upper extremity includes the shoulder, upper arm, forearm, and hand. The main joints of the upper limbs are the sternoclavicular joint, the shoulder joint, the elbow joint, and the hand joint. The palm of the hand has a complex structure and flexible movements and is often analyzed and designed separately for rehabilitation training systems. However, the research in this paper focused on the shoulder, elbow, and wrist joints of the human upper extremity. The seven degrees of freedom of movement of the human upper limb, including the shoulder, elbow, and wrist joints, involve different muscle groups and joint mobility . The relIt has been noted that the brain plasticity theory highlights the significance of stimulating compound movements of multiple joints and muscle groups for neural recovery. Additionally, the correction of abnormal movement patterns after hemiplegia necessitates the coordination of movements among joints. As a result, single-joint or few-joint motor training alone may not be sufficient for rehabilitation. Therefore, the robot design aimed to facilitate the participation of multiple joints of the upper limb in the training simultaneously. By doing so, better rehabilitation outcomes could be achieved.The scheme of the exoskeleton was designed by first making certain simplifications to the models of the joints.The shoulder joint was simplified as a ball-and-socket joint with a fixed center of rotation.The elbow joint was simplified as a hinge joint rotating around a coronal axis, providing one degree of freedom for flexion/extension.The forearm joint was simplified as a carpel joint with one degree of freedom, which allows for inward/outward rotation around the central axis of the forearm.Similarly, the wrist joint was simplified as an elliptical joint with two degrees of freedom: flexion/extension and retraction in the frontal and sagittal planes, respectively.The degrees of freedom corresponding to each joint of the human upper limb are shown in Flexion/extension of the shoulder joint (J2); internal/external rotation (J1).Flexion/extension of the elbow joint (J4).Internal/external of the forearm (J5/J3/J2′).Flexion/extension of the wrist joint (J6); adduction/abduction (J6′).In addition, the sizes of the specific parts of the mechanism are required to be adjustable to accommodate different sizes of the affected limb.The subsequent subsections provide comprehensive insights into the design and development particulars of the upper extremity rehabilitation robot outlined in this paper. Furthermore, During the prototype design phase, lightweight materials that were safe and reliable in terms of strength were required for the exoskeleton robot. In order to select the appropriate materials, a stress check was performed on the non-standard parts, and the maximum stress was calculated as the basis for the material selection. The torque required for shoulder joint movement was large to prevent shaking of the base during training, and the material selected for both the base and robot arm was chosen with patient safety and comfort in mind. Alloy steel was chosen for the base material, while 3D printing technology was used for the robot arm; specifically photosensitive resin and PLA were selected for their light weight and high strength. The robot arm cross-section was rounded to better fit the patient’s upper limb and increase wearing comfort.The rehabilitation robot system is depicted in For the shoulder module, to achieve both inward/outward shoulder rotation and flexion/extension, the robot’s shoulder structures were interconnected through a tandem configuration, as depicted in In this study, the rehabilitation robot was equipped with 140 mm and 80 mm rotating sleeve assemblies on the large and small arms, respectively. Both assemblies shared the same parameters except for the varying inner and outer diameters. The inner sleeve functioned as a rotary axis, with the bearing design serving as a reference in the design. The user sleeve was divided into upper and lower sections, with a middle groove that fit into the inner convex groove of the outer sleeve band, incorporating a ball in the middle to reduce friction. M3 screws were used to join the three parts into a single unit, with a total height of 42 mm. The slide groove had a width of 5.5 mm, the convex groove was 5 mm wide, and both sides featured 1 mm rounded corners, with the ball measuring 3 mm in diameter. The entire assembly was constructed from PLA material, with a weight of 220 g for the large arm cuff assembly and 180 g for the small arm cuff. A pressure sensor was inserted between the outer cuff and the user cuff, positioned at the uppermost part of the rotating cuff, primarily intended for measuring the force of the upper limb movement along the anterior sagittal plane.The wrist joint module of the robot consisted of two rotary joints for radial offset and flexion/extension of the wrist joint, as depicted in In the hand module, we distinguished three primary components: the wiring box, the grip, and the base. The wiring box housed the MPU6050 posture sensor. Since there were no holes in the lid, the circuitry extended from the bottom of the box to the base of the grip. Base 1 was affixed to Base 2, and Base 2 was securely connected to Wrist link 2 using M3 screws.In this paper, we utilized motor drives as our method of choice. To minimize any burden on the patient, the entire weight of the robot arm was attached to the shoulder module, and the base was connected to the robot arm to counteract the effect of gravity on the patient. The base’s underside featured a power on/off button, a charger port, and a network cable port. Additionally, to accommodate patients of varying heights, the base was equipped with a height-adjustable mechanism that could be modified using the knob. In order to ensure the safety of upper limb training, the motor and the corresponding upper limb mechanical joint were affixed to the movable joint through an intermediary sleeve.In the software of the upper limb rehabilitation robot, users could select between active and passive modes and adjust the angular speed of movement, movement time, and movement angle using the interface buttons.The mass, center of gravity, and inertia tensor for each component of the proposed exoskeleton robot were calculated using the Solidworks software (SolidWorks 2021). The results are presented in Safety is of paramount importance in the design of upper limb exoskeleton robots, as they operate in close proximity to the wearer. Human–robot interactions should be meticulously engineered to ensure safe operation. Achieving this safety entails the incorporation of safeguards at multiple levels, including mechanical, electronic, and control designs, to guarantee the secure usage of the robot.On a mechanical level, safety can be established by integrating physical blocking devices within the robot’s structure. These devices serve to restrict the robot from surpassing predetermined ranges of motion (ROM). Additionally, the design of links and robot components should be such that adjacent links naturally act as physical blocking devices within the defined ROM. Furthermore, it’s crucial to regulate the robot joints by setting appropriate current and voltage limits to ensure they remain within the permissible ROM.In terms of the electronic design, controls can be established to limit torque, force, velocity, and position through saturation mechanisms. This ensures the wearer’s safety in the event of a robot malfunction. The control algorithm employed in the rehabilitation robot discussed in this paper includes predefined thresholds for the ROM, velocity, force, and torque. These thresholds can be easily adjusted and set by users via the graphical user interface embedded in the onboard software. Additionally, in the event of an emergency or system failure, the onboard software and power button allow for a quick shutdown with a single click. By implementing these safety measures across the mechanical, electronic, and control domains, the safety of human–robot interactions is enhanced.For serial actuators, the commonly used approach by researchers is the Denavit–Hartenberg (D–H) parametric method. This method is favored due to its simplicity and ease of application in various scenarios, such as the development of forward kinematics, inverse kinematics, Jacobian, and dynamic models.0Y0Z0, which includes the coordinate system of joint one, followed by the coordinate systems of joints two to six. The coordinate system of the end-effector of the robot arm is represented by X6Y6Z6.As shown in θ, link length d, link offset a, and twist angle α.After establishing the joint coordinate system, the D–H parameter  of each i−1 with respect to coordinate system i, is as follows.R and R are the basic rotation transformation matrices around the z-axis and x-axis, respectively, and Td and T are the basic translation transformation matrices along the z-axis and x-axis, respectively. The four basic transformation matrices are all invertible matrices, so iA is also invertible. iA can be further expressed as follows.The general formula for the transformation between adjacent coordinates in the D–H method, which describes the transformation of coordinate system The D–H parameters can be substituted into Equation (2), as follows.The matrix representation of the robot’s end position in the base coordinate system (kinematic equations of the robot) is given by the following.The end position matrix of the robot is as follows.In Equation (3),6-axis of the robot’s end (handle) coordinate system in the base coordinate system.6-axis of the robot’s end (handle) coordinate system in the base coordinate system.6-axis of the robot’s end (handle) coordinate system in the base coordinate system.i stands for sin i,θ ci stands for cos iθ, d1 = 80 mm, d3 = 330 mm, d6 = 4~20 mm, and a4 = 400 mm.In this paper, the linear velocity vector of the upper limb rehabilitation robot consisted of velocities along the three Cartesian axes, and the rotational velocity vector included the angular velocities around the Cartesian axes. In Equation (5), M, V, G, and F matrices were computationally intensive and, therefore, are not explained in the article.The dynamic equations of the robot were derived from the Newton–Euler iterative formulation as follows.sEMG not only reflects the anatomical structure and physiological properties of the muscles being monitored, but also contains information about their movement, such as the muscle moment, movement speed, and movement angle. Since sEMG is non-invasive, rapid, and convenient, it is commonly used in rehabilitation medicine to evaluate muscle function recovery. The field of prosthetic control is also based on sEMG, which is used for motion recognition and classification to control the movement of prostheses according to the patient’s intention ,34,35.In order to establish the relationship between sEMG and the elbow joint angle, it was necessary to collect joint angle data during the flexion and extension movements of the elbow joint. This information served two purposes: first, as a target value for model training and ongoing evaluation; and second, as a metric to validate the model performance and prediction accuracy. The synchronized signal acquisition system  consisteThe small arm and large arm of the rehabilitation device were encompassed by two modules, each equipped with six sEMG sensors. Data acquisition was facilitated through the use of an existing data acquisition card, which performed the essential tasks of EMG signal capture and digital-to-analog conversion at a sampling frequency of 650 Hz.To operate the device, the white switch located at the junction of the large arm and the small arm was pressed. Upon doing so, a green light in a slow-flashing state became visible. Next, the device was brought into proximity with the matching dongle to initiate the Bluetooth connection. Once the connection was successfully established, the indicator light transitioned from a slow flash to a steady, normal light, signifying that the Bluetooth pairing process was complete.The upper computer interface, as depicted in The central operational button held the key functions for initiating data acquisition and saving for both sEMG and the joint angle data. Prior to commencing the data acquisition, this button was displayed as the “save” button. By clicking the “save” button, the data acquisition began instantaneously, and the button transformed into a “stop” button. When the required data amount was collected, the “stop” button was clicked to halt the data acquisition, and the generated data file was saved in bin format on the computer.In order to establish a quantitative relationship between sEMG and the elbow joint angle, it was essential to collect joint angle data during elbow flexion and extension movements. This data served two purposes: one was to be used as the target value for model training and continuous optimization, and the other was to be used as an index for evaluating the model’s performance and verifying its prediction effect.Common joint angle measurement methods fall into two categories: direct and indirect. The advantage of the direct measurement method is its low cost and simplicity of operation. However, the device needs to be bound to the limb in direct contact, which may limit limb movement. For this study, the direct measurement method was adopted using an angle sensor . This sensor contained a circular rotating varistor that changed resistance value when the angle rotated, allowing for the rotation angle to be obtained by measuring the dividing voltage of one of the segments of the varistor.EMG signals are divided into two types based on the type of electrodes used for collection: nEMG and sEMG. For this study, the surface electrode acquisition method (sEMG) was used, where surface electrodes were attached to the skin surface to pick up the superposition of the action potentials from different motor units in time and space. This method has the advantage of being non-invasive and simple to operate, and the measured EMG changes can reflect the functional changes of the whole muscle, making it suitable for sports training applications .For this experiment, the selected electrode patch was a single-use ECG electrode. The EMG sensor used in this paper integrated the filtering and amplification circuits. It amplified weak human sEMG signals within the range of ±1.5 mV by 1000 times and effectively suppressed noise, especially industrial frequency interference, through differential input and analog filtering circuits. The output signal was in analog form, with 1.5 V as the reference voltage, and the output range was 0~3.0 V. The signal amplification was consistent and unchanged for all the subjects in the experiments herein.The acquired sEMG and joint angle signals were susceptible to noise and required preprocessing. sEMG, like other physiological electrical signals, has a low amplitude and demands precise experimental equipment and controlled environmental conditions. In this chapter, we employed the Fourier transform for sEMG noise reduction and wavelet transform for the joint angle signal noise reduction. We also analyzed the process and outcomes of signal preprocessing.The Fourier transform noise reduction process began with transforming the time domain signal using the Fourier analysis. The transformed signal was then filtered to eliminate noise frequencies. Finally, the filtered signal underwent Fourier inversion to yield a noise-reduced signal. In the case of sEMG acquisition, the noise primarily arose from industrial frequency interference related to the equipment and inherent cardiac interference from the human body. A breakdown of the primary sources and their associated frequency ranges of noise can be found in As shown in the table, the noises mixed into the sEMG were primarily low-frequency noises with frequencies below 30 Hz, with the exception of the 50 Hz industrial frequency interference. The sampling frequency of the synchronous signal acquisition system was 650 Hz, which meant the acquired signal frequency range was 0–325 Hz.To filter out noise while retaining the useful information of the original signal, we designed both a 30–300 Hz bandpass filter and a 50 Hz IF trap using the MATLAB(MATLAB R2018b) toolbox Filter Design and Analysis Tool with the ‘filterDesigner’ command. The default setting for the filter was an infinite impulse response (IIR) filter.This paper selected the Butterworth filter for both the 30–300 Hz bandpass filter and the 50 Hz IIR trap. For the 30–300 Hz bandpass filter, we set the stopband cutoff frequencies to 10 Hz and 320 Hz, the passband cutoff frequencies to 30 Hz and 300 Hz, the stopband attenuation to 40 dB, and the passband attenuation to 1 dB. This resulted in a 10th order Butterworth bandpass filter.For the 50 Hz trap, we set the passband cutoff frequencies to 47 Hz and 53 Hz, the stopband cutoff frequencies to 49.5 Hz and 50.5 Hz, the two passband attenuations to 5 dB and 1 dB, respectively, and the stopband attenuation to 30 dB. This yielded a sixth order industrial frequency trap.Using the parameters described above, the signal was first bandpass filtered and then 50 Hz trap filtered. As seen in For preprocessing the joint angle signals, we employed wavelet transform denoising in MATLAB. While MATLAB offers a range of wavelet denoising functions, our paper utilized the one-dimensional automatic noise reduction function, ‘wden’. To determine the threshold value and choose the appropriate threshold function, we made the following settings. We opted for the heuristic threshold calculation method to compute the threshold value and selected the soft threshold function for filtering the wavelet system.In order to achieve optimal denoising results, we assessed the denoising effect using the ‘smoothness r’ index. This index, set at a decomposition level of four layers, was based on the Coiflet-4 (coif4) wavelet basis. The results of this denoising process are presented in First, the acquired sEMG and joint angle signals underwent preprocessing to cancel out any noise. Since most of the noise frequencies in the sEMG were below 30 Hz, the Fourier transform was utilized for denoising, using a bandpass filter with a range of 30–300 Hz and an industrial frequency trap of 50 Hz to effectively filter out the noise in the sEMG.For the joint angle signal, wavelet threshold denoising was employed. The number of wavelet decomposition layers was set to four, and the coif4 function was used as the wavelet basis. This approach successfully eliminated noise in the joint angle signal using the one-dimensional automatic noise cancellation function “wden”.Furthermore, taking advantage of the synchronous acquisition of the sEMG and joint angle signals, the starting and ending points of the muscle activity segments were detected based on the angle signal waveforms. This step laid the foundation for the subsequent extraction of the characteristics of the muscle activity segments.The enrolled participants in the experiment were required to be in good health, without any neuromuscular diseases, and possess flexible and obstacle-free movement in all their joints. To ensure the validity of the experiment, the participants were not allowed to engage in strenuous exercise within 24 h before the data collection began, ensuring that their upper limbs were in a normal condition during the experiment. There were six enrolled participants in this study, comprising four males (coded as M1~M4) and two females (coded as W1~W2). The basic information about the enrolled participants is presented in In this paper, the upper limb muscles associated with elbow flexion/extension that were measured included the triceps brachii, biceps brachii, elbow, and brachioradialis muscles. To collect the EMG signals and angle signals during elbow flexion and extension, three surface electrode patches were affixed to each muscle: positive, negative, and reference electrodes.Before placing the electrodes, it was important to enhance the skin’s adherence to the electrodes by preparing the measurement area. This involved cleaning the area with mild soap and water to eliminate dirt, sweat, sebum, and other impurities. If the measurement area was covered by thick hair, it was advisable to trim or shave the hair. Long hairs could obstruct the electrodes from making close contact with the skin, potentially affecting the signal quality. Since the surface electrode patch primarily comprised a non-woven backing, pressure-sensitive adhesive, electrode core, and conductive adhesive, no additional skin preparations were required. Once the measurement area was completely dry, the electrodes were attached to the skin, as illustrated in The preparatory posture at the beginning of each experiment was set as follows. The subject stood in an upright position, with arms naturally hanging down, the forearm and upper arm forming a straight line perpendicular to the horizontal plane, and the palm of the hand clenched in a fist with the palm facing upward. The synchronized signal acquisition system was worn on the subject’s right arm, as shown in The angle data collected during the elbow flexion/extension movement was referred to as the “original angle”. To obtain the actual angle during the elbow flexion/extension movement, the baseline angle was subtracted from the original angle data. This process enabled the determination of the real angle during the elbow flexion/extension movement for further analysis.The purpose of this chapter was to extract suitable EMG signal eigenvalues to lay the foundation for the subsequent modeling. There were two evaluation indices for the prediction effect of the model. One was the mean square error (MSE) between the predicted value and the real value. The smaller the MSE value, the better the prediction effect, as shown in Equation (8).Another indicator is the regression coefficient (R), as shown in Equation (9). R represents the correlation between the predicted and actual values. An R value of 1 indicates a strong relationship, while an R value of 0 indicates a random relationship .In summary, a smaller MSE value and a larger R value indicated a better prediction effect, as described in Equation (9). Where By comparing the root mean square (RMS), mean absolute value (MAV), integral absolute value (IAV), wave length (WL), Wilson amplitude (WAMP), zero crossing (ZC), slope sign change (SSC), mean power frequency (MPF), peak frequency (PKF), median frequency (MF), total power (TTP), spectral moments (SM), auto regressive (AR), and SampEn eigenvalues of the model prediction effect, we finally selected the fifth order AR model coefficients as the input values of the BP neural network. The prediction merits of the elbow joint angle were then analyzed using MATLAB with error back propagation neural network (BPNN), generalized regression neural network (GRNN), support vector machine regression (SVR), regression tree (RT), and ensemble regression learning (ERL) regression algorithms.The chosen window width for the superimposed window in this paper was 130 signal points. The results are depicted in An artificial neural network (ANN) is an algorithm designed and developed to simulate the structure and function of the human brain’s neural network. It consists of neurons connected by adjustable weights and exhibits a good adaptive learning ability. Among various neural network algorithms, the back propagation neural network (BPNN) is the most widely applied. Its structure is illustrated in According to the grading of muscle strength, limbs with a muscle strength below grade two are unable to resist gravity, especially in the initial stage after a stroke where the patient’s upper limb nerves and muscles may be damaged, resulting in a lack of active power. In this early rehabilitation stage, passive training is prioritized to improve muscle tone, reduce muscle spasms, and guide the affected limb towards active rehabilitation. The process of passive control involves operating the system as planned, processing the output information, and solving any deviations that may occur.As the stroke progresses, the upper limb may regain some motor ability, and the upper limb rehabilitation robot transitions from leading the movements to following the patient’s movements. The focus then shifts to active rehabilitation, with an emphasis on human–machine interaction. Establishing a closed-loop pathway between the human and the machine can improve the effectiveness of rehabilitation in the later stages and restore normal physiological activities of the upper limb.Based on the above rehabilitation perspective, this paper proposed three control modes, as depicted in the Passive mode controlled by the PID  to guide the initial rehabilitation process.Active rehabilitation, where the affected side was driven by the healthy side, allowing for coordinated movement between both sides.Interactive control, enabling the patient to move autonomously, fostering more independent and self-driven rehabilitation activities.The PID control is known for its simplicity in structure, stable operation, and widespread application. It has been extensively used in the fields of rehabilitation robotics and industrial control and remains one of the earliest and most popular control methods ,39,40.One of the advantages of PID control is that it does not heavily rely on the model of the control object. Satisfactory results can often be achieved by tuning the system parameters appropriately. In this study, a simple PID controller based on state feedback, as shown in Equation (10), was implemented to test the desired functionality of the developed exoskeleton robot. The trajectory of motion was controlled by manipulating the joint torques calculated by the PID controller. The PID controller effectively regulated the robot’s movements and ensured its desired performance during the rehabilitation exercises.KP, KI, and KV are the diagonal matrices for the proportional, integral gains, and derivative, respectively.KP), derivative gains (KV), and integral (KI). In this paper, the PID gains were set for the controller. The gains chosen for the experiments were KP = diag , KI = diag , and KV = diag .The reliability of such a controller depends on the proper selection of the proportional , with an internal clock (INT) of 72 MHz, resulting in a counting time of 0.1 ms. The auto-load register (ARR) value was configured as (200−1), generating a counting interrupt period of 20 ms, which aligned with the required period for the servo drive. By using the output comparison function in the PWM mode, the duty cycle could be adjusted by modifying the value of the condition code register (CCR). The CCR value was derived from the Hall sensor through digital-to-analog conversion (ADC) using a specific formula transformation.In this study, a Hall sensor was employed to extract the angle information from the healthy side’s elbow joint  elbow angle and the reference  angle are depicted in 2.When compared with the other literature that reported angle prediction errors in the range of 5~8° [The linear regression relationship between the refence value and the actual value showed a high R value of 94.41%. Additionally, the average error between the two angles was approximately 4° with a MSE of 7.2° of 5~8° , the modThis experiment aimed to analyze the motion of a single joint, and in order to validate the experimental results, it was analyzed with respect to PID control and healthy side-driven affected side control. The robot kinematic model was established using the Link and Serial Link functions in the MATLAB Robotics Toolbox. The link parameters of the upper limb rehabilitation robot were input to obtain its kinematic space, as illustrated in For the PID control , the ploFor the shoulder flexion–extension movement, the joint was initially positioned at zero degrees and then extended to 85° before returning to 0°. The same movement was repeated once more. From the top curve shown in As shown in For the inward and outward shoulder rotation, the experiment began with all the joints in the zero position. The shoulder was then horizontally rotated outward to 45° and returned to 0°. This motion was repeated at the same speed. The experimental results are presented in For the elbow flexion–extension motion, the elbow joint was repeatedly extended to 95° and then returned to 0°. The experimental results are displayed in For simultaneous shoulder and elbow joint movements, the first movement replicated a forward-reaching motion, starting from the initial position , moving to the reaching position (the shoulder extended to 90° and the elbow extended to 90°), and then returning to the initial position. As shown in The diagonal catch movement consisted of flexion–extension and the inward and outward rotation of the shoulder joint, as well as flexion–extension of the elbow joint. The movement began with the elbow and shoulder joints internally and externally rotated at 0°. First, the shoulder joint was internally rotated to 45° and then was maintained at this angle until the end of the movement (located at Point 1). Subsequently, the shoulder joint was internally retracted from 90° to 0°, and the elbow joint was rotated from 0° to 90° before the movement concluded. The experimental results are shown in Due to the unilateral disability of hemiplegic patients, special consideration should be given to the mutual coordination of bilateral limbs in bilateral rehabilitation training. In this study, we chose to use the healthy upper limb to drive the affected side as a rehabilitation task to promote bilateral coordination. The goal of this task was to achieve better coordination by equalizing the angle of motion of the elbow joints of both upper limbs. However, for hemiplegic patients, accomplishing the equivalent output force of the affected elbow joint without any assistance or feedback was challenging.It was previously mentioned that there were still errors in angle prediction using BPNN, and there was some delay in the system. Therefore, sEMG was not introduced as a parameter for the healthy side to drive the affected side’s movement. It was more reliable to have the patient wear the elbow rehabilitator on the healthy side and use the Hall sensor to obtain the joint angle. To demonstrate the stability of this approach, the subjects were asked to complete a single elbow joint exercise repeatedly for 1000 s. The results of the experiment are shown in Through research on rehabilitation robots in recent years, significant breakthroughs have been made by research institutes and universities in the field of upper limb rehabilitation robots. The development trend has shifted from low degrees of freedom to high degrees of freedom, and from traditional control to artificial intelligence control. In designing upper limb rehabilitation robots, it is essential to consider the precise torque required for limb operation, taking into account the differences in the structure, control, and specific treatments.This study presented the development of a six-degree-of-freedom (6-DOF) upper limb rehabilitation robot with three control modes, an end-pose extraction module, and a mechanical joint capable of rotation around the center. A hybrid approach was utilized to derive the kinematic equations of the developed robot. The implementation of PID control algorithms allowed for passive control of the patient during the prehabilitation period.The experimental results demonstrated that the robot exhibited low tracking error in joint motion. For patient active rehabilitation in the middle stage, bilateral control was employed, enabling patients to actively participate and improve their training efficiency. Before this, an eight-channel elbow angle prediction system was developed, and the experiments confirmed the effectiveness of using BPNN for prediction. The patients wore an elbow rehabilitator on the healthy side and the upper extremity rehabilitation robot on the affected side, utilizing the healthy side to drive the affected side. During repetitive tracking training, the affected side’s upper limb was able to achieve basic following, although a slight delay was observed in the data transmission from the Hall sensor to the register.For the interaction control, a Unity3D-based game system was developed, enabling the patients to participate in the game based on the position of their end arm. This approach increased the patients’ interest in rehabilitation and allowed for a better visualization of the rehabilitation training data. Future work may involve the experimental validation of active rehabilitation and the incorporation of impedance in the interaction control.To address the issue of delayed data transmission, several strategies can be employed. Firstly, elevating the sampling rate can enhance the data density, yielding finer and more detailed information, consequently bolstering the temporal resolution of events. Secondly, data interpolation and filtering can be employed. While increasing the sampling rate may augment the data volume, filtering out high-frequency noise and erratic data can alleviate delays. Thirdly, advanced control algorithms, such as ahead-lag control or model predictive control, can be implemented. These algorithms, by predicting future events, enable proactive system responses, reducing reliance on real-time data and mitigating data transmission delays.In the future, our research will emphasize the comprehensive evaluation of the rehabilitation robot efficacy on hemiplegic patients, both in the short-term and long-term rehabilitation phases. We aim to investigate the effectiveness of upper limb rehabilitation robots combined with multimodal control for patients across various life stages. Our focus will also extend to enhancing the interaction design for Unity3D, with the intention of creating games grounded in the patients’ daily activities, allowing us to assess their impact on the rehabilitation process and overall effectiveness.