A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> Elevated stump skin temperatures and the accompanying thermal discomfort are side effects of prosthesis use that may reduce amputee quality of life, particularly in hot or humid surroundings. Lower skin temperatures might be achieved through more effective heat transfer in the prosthesis, a process governed in part by the thermal conductivity of the sock, liner, and socket layers. To assess the thermodynamic properties of currently available components, an instrument capable of measuring the heat flux across a regulated temperature differential was developed. Experimental results show that the thermal conductivity ranged from 0.085 - 0.266 W/m x K for liner materials and from 0.148 - 0.150 W/m x K for socket materials. The results of this study demonstrate that the prescription of typical multi-layer prostheses constructed with the higher thermal conductivity materials might reduce temperature-related discomfort in patients. <s> BIB001 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> The ability to mimic the tactile feedback exhibited by the human hand in an artificial limb is considered advantageous in the automatic control of new multifunctional prosthetic hands. The role of a slip sensor in this tactile feedback is to detect object slip and thus provide information to a controller, which automatically adjusts the grip force applied to a held object to prevent it from falling. This system reduces the cognitive load experienced by the user by not having to visually assess the stability of an object, as well as giving them the confidence not to apply unnecessarily excessive grip forces. A candidate for such a sensor is a thick-film piezoelectric sensor. The method of fabricating a thick-film piezoelectric slip sensor on a prototype fingertip is described. The construction of experimental apparatus to mimic slip has been designed and analyzed to allow the coefficient of friction between the fingertip and the material in contact with the fingertip to be calculated. Finally, results show that for a coefficient of friction between the fingertip and grade P100 sandpaper of approximately 0.3, an object velocity of 0.025plusmn0.008 ms-1 was reached before a slip signal from the piezoelectric sensor was able to be used to detect slip. It is anticipated that this limiting velocity will be lowered (improved) in the intended application where the sensor electronics will be powered from a battery, connections will be appropriately screened, and if necessary a filter employed. This will remove mains interference and reduce other extraneous noise sources with the consequence of an improved signal-to-noise ratio, allowing lower threshold values to be used in the detection software <s> BIB002 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> Controlling grip force in a prosthetic or robotic hand requires detailed sensory feedback information about microslips between the artificial fingertips and the object. In the biological hand this is accomplished with neural transducers capable of measuring micro-vibrations in the skin due to sliding friction. For prosthetic tactile sensors, emulating these biological transducers is a difficult challenge due to the fragility associated with highly sensitive devices. Incorporating a pressure sensor into a fluid-filled fingertip provides a novel solution to this problem by effectively creating a device similar to a hydrophone, capable of recording vibrations from lateral movements. The fluid conducts these acoustic signals well and with little attenuation, permitting the pressure sensing elements to be located in a protected region inside the core of the sensor and removing them from harmpsilas way. Preliminary studies demonstrate that high frequency vibrations (50-400 Hz) can be readily detected when such a fingertip slides across a ridged surface. <s> BIB003 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> OBJECTIVE ::: This article provides an overview of tactile displays. Its goal is to assist human factors practitioners in deciding when and how to employ the sense of touch for the purpose of information representation. The article also identifies important research needs in this area. ::: ::: ::: BACKGROUND ::: First attempts to utilize the sense of touch as a medium for communication date back to the late 1950s. For the next 35 years progress in this area was relatively slow, but recent years have seen a surge in the interest and development of tactile displays and the integration of tactile signals in multimodal interfaces. A thorough understanding of the properties of this sensory channel and its interaction with other modalities is needed to ensure the effective and robust use of tactile displays. ::: ::: ::: METHODS ::: First, an overview of vibrotactile perception is provided. Next, the design of tactile displays is discussed with respect to available technologies. The potential benefit of including tactile cues in multimodal interfaces is discussed. Finally, research needs in the area of tactile information presentation are highlighted. ::: ::: ::: RESULTS ::: This review provides human factors researchers and interface designers with the requisite knowledge for creating effective tactile interfaces. It describes both potential benefits and limitations of this approach to information presentation. ::: ::: ::: CONCLUSION ::: The sense of touch represents a promising means of supporting communication and coordination in human-human and human-machine systems. ::: ::: ::: APPLICATION ::: Tactile interfaces can support numerous functions, including spatial orientation and guidance, attention management, and sensory substitution, in a wide range of domains. <s> BIB004 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> A biomimetic system for enhancing the control and reliability of grasping with prosthetic hands was designed and experimentally evaluated. Barometric pressure sensors as well as a force-sensitive resistor (FSR) were placed on a prosthetic hand to provide valuable tactile feedback. Contact and slip detection grip control algorithms were developed to interpret force signals for enhancing stable grasping. Recent advances in radio-frequency identification (RFID) technology enable the amputee to select between grip control strategies based on the desired object to be grasped. Experimental results indicate that the control algorithms are capable of utilizing real-time force responses to detect object contact as well as slip. By allowing the user to act as a high-level controller with RFID technology, a multi-faceted low-level controller that responds to tactile feedback can be developed for enhancing grasping functionality in prosthetic hands. <s> BIB005 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> Abstract We present a micro tactile information transceiver that recognizes user's fingertip motion (direction/selection) input and generates artificial texture output using frictional-force tunable slider-pad. The proposed micro tactile transceiver offers capacitive detection of human gesture input and simultaneous generation of frictional force and vibration by electrostatic attraction of slider-pad. In the experimental study, we verified the tactile input recognition performance by measuring the capacitance changes for the z -axis (selection) and x -/ y -axis (direction) movement, which were 0.146 ± 0.02 nF/40 μm and 0.03 ± 0.02 nF/250 μm, respectively. We also verified the artificial texture generation performance by measuring frictional force and lateral vibration by controlling the electrostatic force between slider-pad and electrode layer. The frictional force was measured in the range of 32–152 mN under 0–80 V DC input voltages, offering the lateral force on skin in the range of 121–580 mN combined with spring force. We applied AC voltage to induce fluctuating frictional force to generate lateral vibration on the skin, and obtained peak-to-peak fluctuation of 128.1 ± 0.8 mN under 3 Hz/60 V which is a 32% improved value compared to previous approaches. The proposed micro tactile information transceiver is capable of recognizing human gesture information (direction/selection) and generating the artificial texture information. <s> BIB006 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> Many upper limb amputees are faced with the difficult challenge of using a prosthesis that lacks tactile sensing. State of the art research caliber prosthetic hands are often equipped with sophisticated sensors that provide valuable information regarding the prosthesis and its surrounding environment. Unfortunately, most commercial prosthetic hands do not contain any tactile sensing capabilities. In this paper, a textile based tactile sensor system was designed, built, and evaluated for use with upper limb prosthetic devices. Despite its simplicity, we demonstrate the ability of the sensors to determine object contact and perturbations due to slip during a grasping task with a prosthetic hand. This suggests the use of low-cost, customizable, textile sensors as part of a closed-loop tactile feedback system for monitoring grasping forces specifically in an upper limb prosthetic device. <s> BIB007 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> Tactile or touch sensing can enable an object's surface texture and other properties to be perceived which can facilitate grasping and manipulating various objects. Prosthetic hand users and operators of tele-operated robot arms also need to perceive these tactile properties by some means to effectively manipulate objects and performed skilled work. This paper introduces a tactile sensing and feedback system that is based on detecting surface vibrations in an artificial finger, when contact or friction with a surface is made, and appropriately stimulating nerves in the user's skin with electro-tactile feedback. This feedback system has benefits over existing systems because it can deliver a wide variety of sensations to the user and is compact, non-mechanical, wireless and comfortable for the user to wear. Experimental results are provided which show the potential of our system at achieving remote tactile sensing and feedback of textured surfaces. <s> BIB008 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> Abstract Quantitative measurement of hand kinematics can help us to better understand pathophysiological aspects of finger neural control and quantify hand impairments. A sensor glove was developed based on resistive bend sensors and resistive force sensors to monitor finger joint angles and forces exerted to objects by fingers. The validity and reliability of the glove were evaluated. A novel method was proposed to visualize and quantify the abnormality of the inter-joint coordination. The validity test indicated that the accuracy error of measuring joint angles was approximately ±6° and that of measuring forces was approximately ±8 g. The reliability test yielded an intraclass correlation coefficient of 0.9876 ± 0.0058 for the force sensors and 0.9561 ± 0.0431 for the bend sensors. The stability, accuracy and reliability of measuring joint angles were comparable with previous studies. The involvement of force sensors and the capacity of reflecting inter-joint coordination make the glove more comprehensive for hand function assessment. <s> BIB009 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> Abstract This paper presents a multisensory glove to allow prosthetic and robotic hand to simultaneously feel the pressure, temperature, and humidity. The low-cost implementation of the flexible glove with multiple sensors presented here will enable gathering of tactile information from grasped objects. The off-the-shelf components such as pressure, temperature and humidity sensors have been mounted on a flexible printed circuit (FPC) board and attached the glove. During validation most of the sensors were in contact with the grasped objects and successfully measured various contact parameters. <s> BIB010 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> Lack of haptic feedback in prostheses is proving to be the bottleneck in the field. Closed loop prostheses should fill the gap for amputees to experience exteroceptive (environmental pressure) or proprioceptive (proper state or joint angles) by means of different sensory feedback. Detailed work is being done on the development of electrotactile and vi-brotactile stimulation system. The objective of this research is to generate vibrotactile feedback on a residual arm of an amputee. With this research, it is an endeavor for amputees to possess cognitive response while holding something amid environmental interactions with the help of convenient methodology and with suitable components in hand. To make this design pertinent to the objective, we have used coin shaped vibration motors, 0.5-inch diameter circular FSR provide haptic feedback from the tip of the finger. 3D printed prosthetic hand proved to be the best remedy for lowering the cost. The research was conducted on 6 healthy subjects and one amputee. Experimental results showed the effectiveness of proposed approach. <s> BIB011 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> Lack of sense of touch is a fundamental problem for today's robotic prostheses. Considering that touch feedback plays a significant role in identifying contacted objects, the aim of this study is to use acceleration signals, which occur due to physical contact of a prosthetic hand with objects, as sensory feedback. These contact signals are applied on the clavicle bone using a tactor. First, a library of accelerations occurring as a result of tapping on different materials is prepared. The effect of impact speed is studied and used as a scaler for real-time applications. A stochastic signal model is developed for the contact accelerations. Due to the distinct waveform characteristics of different materials, the rate of change of acceleration is used to identify hardness of the objects. In a psychophysical study, the whole procedure of recording, identifying, and replaying the signals is investigated. Results of this study showed the ability of the designed tactor to provide distinguishable hardness sensations of different materials in real time. Furthermore, considering the results of the position control test with and without vision, it can be concluded that the proposed contact sensory feedback method could be enough to substitute for vision during simple grasping tasks. <s> BIB012 </s> A Review of Non-Invasive Haptic Feedback stimulation Techniques for Upper Extremity Prostheses <s> Fig. 1 -Signal flow's block diagram of myoelectric prostheses. <s> A system that can recognise materials based on tactile sensing alone is presented.Identification achieved by analysis of thermal conductivity and surface texture.Evaluation of various machine learning algorithms and developed hybrid classifiers.The system outperforms humans in the task of material recognition. Identification of the material from which an object is made is of significant value for effective robotic grasping and manipulation. Characteristics of the material can be retrieved using different sensory modalities: vision based, tactile based or sound based. Compressibility, surface texture and thermal properties can each be retrieved from physical contact with an object using tactile sensors. This paper presents a method for collecting data using a biomimetic fingertip in contact with various materials and then using these data to classify the materials both individually and into groups of their type. Following acquisition of data, principal component analysis (PCA) is used to extract features. These features are used to train seven different classifiers and hybrid structures of these classifiers for comparison. For all materials, the artificial systems were evaluated against each other, compared with human performance and were all found to outperform human participants average performance. These results highlighted the sensitive nature of the BioTAC sensors and pave the way for research that requires a sensitive and accurate approach such as vital signs monitoring using robotic systems. <s> BIB013
On the other side, the importance of providing feeling from the tactile prosthetic hand to the patient is not only limited to transferring the feeling of the handgrip, but it also helps the user to control the applied contact force to prevent sliding object. In addition, the main important function of the haptic system is to enable the user to detect surface type, roughness, temperature, humidity, and rigidity, depending on the sensing types that used in the tactile system. Usually, the tactile sensors mount on one or more fingertip of the prosthetic hand or sometimes cover entire prosthetic hand. The main function of the tactile sensory system is to measure the environmental parameters and provide it to the microcontroller as analog signals, in order to process the data. The decision orders that output from the microcontroller using as a manipulating signal to drive the haptic feedback actuators, in order to excite the nervous system of the amputee's residual limb. Such excitation transfers the feeling to the patient's brain and enables him to recognize his environment through his prosthetic hand. For example, the main steps of the force-pressure detection in a tactile feedback stimulation system are presented in Fig. 2 . a. The grasp force is measured and transferred to a pressure stimulation on the forearm skin to excite the patient's brain. The haptic feedback stimulation system can be further classified into six displays, depending on how to stimulate the patient's skin and provide the information of the tactile sensory system to the amputee's brain. The six haptic feedback displays are the pressure feedback display, the vibration feedback display, the skin stretch feedback display, the squeeze feedback display, the electro feedback display, and the thermal feedback display, as shown in Fig. 2 . b. The combination of two or more types of haptic feedback displays leads to create an unusual multi-mode feedback technique, such a system is called the hybrid feedback display. In general, the haptic feedback stimulation system consists of three main parts: the tactile sensory system, the computer system, and the feedback stimulation system. The tactile sensory system is responsible for gathering the tactile information on the prosthetic hand and transferring it to measurable signals. The computer system is in charge of processing the tactile signals and manipulating the excitation signals to control the feedback actuators. Lastly, the feedback stimulation system consists of mechanical or electrical actuators which have the ability to excite the amputees' residual parts to stimulate the skin and convey the tactile information to the users' brain in a quiet manner. Based on previous studies, the tactile sensory system can be classified into five classes according to its function. The five classes are: pressure detection sensory system BIB009   , slippage detection sensory system BIB005 BIB007 , surface texture detection sensory system BIB006 BIB003 , material detection sensory system BIB013 BIB012 , and temperature detection sensory system BIB001  . The challenges on how to combine two or more types of tactile sensors to form a hybrid sensory systems which has the ability to measure multi types of tactile information simultaneously have been addressed in some studies BIB002 BIB010 . The general idea behind most of the tactile sensors that using with the prostheses is utilizing material, resistor, capacitor, conductive rubber, or other materials that have the ability to change the current pass through it and then change its output voltage when the sensory affected of an external disturbance like force, temperature, or vibration. For instance, the tactile glove equipped with multi Quantum Tunnelling Composite (QTC) pressure sensors has been developed by NASA/DARPA in order to modify autonomous grasping skills of a dexterous humanoid robot  . On the other hand, the force sensitive resistor sensor (FSR) was installed on the fingertip of a 3D printed prosthetic hand to detect the grasping contact force and enable the user to generate a suitable grasping force to carry the object without slipping BIB011 . The surface texture detection is available to the patient who uses the haptic artificial hand by attaching a vibration sensor on the fingertip of the prosthetic hand. When the finger slides over the surfaces, the vibration sensor provides wide variation of amplitude and frequency signals, depending on the hardness of the surfaces. These signals will be processed to generate a high wide range of feedback stimulation to the user and enable him to recognize the surface texture BIB004 BIB008 . The main objective of this study is to shed some light on the main techniques and the classification of the noninvasive haptic feedback stimulation system, which has been designed to help the patients who suffer from loss of their upper limb to recover the sensation through prosthetic hand. Furthermore, this literature aims to highlight the main types of actuators that commonly used to stimulate the patients' residual parts and the most common locations of installing these actuators. In addition, this paper presents a statistical analysis to the types of the prosthetic hand that been used in the experimental work in this field, the types and the number of the engaged volunteers, and the challenges that were faced in previous studies. When conducting a systematic search of the literature, the wearability, effectivity, comfort, and functionality criterions were placed on as the main standers to read, classify, and summarize the previous studies, as shown in Fig. 3 . The rest of paper is organized as follows: the main non-invasive feedback stimulation techniques that usually used with the prosthetic hand are listed Section 2. Section 3 lists the studies that combine between two or more types of noninvasive feedback stimulation, called as hybrid feedback stimulation system. Discussion and statistical analysis of previous research and its main techniques are presented in Section 4. Finally, some conclusions and future work regarding this work are addressed in Sections 5.