standing from a seated position and walking are basic components of many daily activities 
 and have been analyzed using various methods . the sit - to - walk ( stw ) task involves the 
 sequential motions occurring from standing to initiating gait . magnan1 
 and kerr2 , 3 examined the stw task using a three - dimensional motion analysis 
 system and ground reaction force plates . these authors analyzed the timing of defined events 
 such as task onset , seat off , and toe off in movements and in phases divided by these events 
 during the stw task while focusing on the velocity of the body s center of gravity and the 
 ground reaction force , and revealed that seat - off and gait initiation were executed 
 simultaneously in healthy participants . kouta4 showed that the forward and vertical speeds of the center of gravity 
 during the stw task were lower in elderly adults than in younger adults . similarly , most 
 other studies of the stw task have focused on the transition between standing and gait 
 initiation . dion5 defined this 
 transitional ability or strategy as  fluidity  or a  fluid strategy  , and developed a 
 fluidity assessment scale known as the fluidity index ( fi ) . fi was shown to associate with 
 general clinical measurements and decreasing fluidity among patients with hemiplegia5 . fi is calculated using the change in forward momentum of the center of 
 gravity , and thus can objectively and sensitively evaluate fluidity . however , large 
 equipment , such as a motion analysis system , is required to calculate fi ; therefore , the 
 fields in which fi can be used are limited . to address this problem , malouin7 developed the fluidity scale ( fs ) , in which 
 fluidity is assessed on a four - point graded ordinal scale . this grade is determined using 
 the timings of trunk extension completion and first toe off . these devices are sufficiently small 
 and lightweight to be carried by a subject without hindering motion . therefore , 
 accelerometers enable measurements not only in laboratory settings , but also in clinical 
 settings , which are not equipped with motion analysis systems . these devices have been 
 widely used in various contexts , including gait analysis8,9,10,11,12,13 and sit - to - stand 
 tasks14 , 15 . the timed up - and - go test , which includes elements of the stw task , 
 has been analyzed using accelerometers16,17,18,19 . weiss16 examined healthy participants and patients with parkinson s disease 
 and showed that an accelerometer - based assessment was better than an assessment based only 
 on a stopwatch . although it is feasible to use an accelerometer to evaluate fluidity in the 
 stw task , this application has not previously been reported . the aim of this study was to evaluate the possibility of an accelerometer - based fluidity 
 evaluation . we compared event timings in the stw task calculated with an accelerometer to 
 those calculated using a three - dimensional motion analysis system and foot pressure sensing 
 system . we have also discussed the possibility of estimating fi from the timing of these 
 events and the magnitude of acceleration . the participants were 16 healthy young males ( mean age : 23.7  2.2  years , mean height : 
 174.2  3.7  cm , mean weight : 67.5  8.1  kg ) without any disabilities that would restrict 
 performance of the stw task . the epidemiologic research ethics committee of gunma university 
 faculty of medicine approved this study ( no . the stw task was performed under conditions based on those reported by malouin et al7 . participants sat on a chair without a back 
 support or armrests and with a seat height standardized to 100% of the individual 
 participant s leg length . participants were instructed to look forward and , during the task , 
 to fold their arms on their chest . after the start of the data collection , participants 
 remained in a stationary position for 3  s , and upon hearing an auditory cue were required to 
 stand up and walk toward a target placed 2  m in front of the chair . participants practiced the task at each speed until 
 they could reproduce the movements smoothly and naturally . each trial was then recorded 
 simultaneously using a three - axial piezoelectric acceleration sensor ( ta-513 g , nihon khoden , 
 tokyo , japan ) , a motion capture system ( ma3000 , anima , tokyo , japan ) with six infrared 
 cameras , and a foot pressure sensing system ( walk way , anima ) . the sampling frequency was 
 100  hz . the acceleration sensor was directly attached to the skin between the l3 and l4 
 vertebrae13 , 18 , 19 and was orientated to 
 measure the sagittal , frontal ( medial - lateral ) , and vertical planes . in this study , only 
 sagittal data were analyzed . reflective markers were attached to the acromion , anterior 
 superior iliac spine , greater trochanter , knee joint , lateral malleolus , and head of the 
 fifth metatarsal on both sides of the body . acceleration and kinematic data were filtered 
 using a zero - phase low - pass filter with a cut - off frequency of 8  hz . a typical acceleration waveform and definitions of the events 1.a typical acceleration waveform and definitions of the events from acceleration and 
 reference dataa negative change indicates anterior trunk inclination or backward acceleration . a 
 positive change indicates posterior trunk inclination or forward acceleration . when 
 the trunk inclines , the accelerometer detects gravitational acceleration .. the onset of the stw task was defined as the point at which the acceleration 
 exceeded two standard deviations ( sd ) of the mean of data from the 3-s stationary period this was compared to the onset time of the stw task calculated as the initiation of forward 
 acromion motion as recorded by the motion capture system . after initiation of the stw task , 
 sagittal plane acceleration was negative and reached a local minimum . this was defined as 
 the first peak , and the timing of the first peak was compared to the timing of maximum trunk 
 inclination recorded by the motion capture system . the last positive peak , which was followed by rapid negative 
 acceleration , was defined as the second peak . the timing of the second peak was then 
 compared to the timing of the first heel strike determined using the foot pressure sensing 
 system . for each comparison , a bland - altman plot was generated by plotting the difference 
 between the two measures against the mean of the two measures to provide a visual 
 representation of heteroscedasticity . , a simple linear regression analysis was performed using the accelerometer - measured 
 event timing as the independent variable and the motion analysis system- or foot pressure 
 sensor - measured event timing as the dependent variable . phase 1 was defined as the onset of the stw task to the first peak , 
 phase 2 as the first peak to the second peak , and phase 1 + 2 as the onset of the stw task 
 to the second peak . a typical acceleration waveform and definitions of the events from acceleration and 
 reference data a negative change indicates anterior trunk inclination or backward acceleration . this index corresponds to the percent 
 change in the body forward momentum , which is calculated from motion capture system data . in 
 a typical trial , the forward body momentum increases immediately after the peak initiation 
 of motion and subsequently decreases to the lowest value . fi is calculated as the ratio of the lowest to the peak value . in a fluid 
 motor strategy , the body forward momentum is maintained or slightly decreased after the 
 first peak , and this is reflected by a higher fi . simple and multiple linear regression 
 analyses were performed to estimate the fi from acceleration data . in these analyses , the 
 durations of phase 1 , phase 2 , and phase 1 + 2 and the magnitude of the second acceleration 
 peak were used as independent variables . in all statistical analyses , all statistical analyses were performed using spss statistics 22.0 
 ( ibm , armonk , ny , usa ) . statistical significance was set at p < 0.05 . differences in the timing of events calculated using either accelerometer or reference data 
 ( motion analysis system or foot pressure sensor ) are shown in table 1table 1.differences of event timings measured by two systemsspeed conditiononset of stwaccelerometer -motion analysis system1st peak -maximum trunk inclination2nd peak -heel strikemean sd 95% cimeansd95% cimeansd95% cicomfortable0.200.140.070.470.100.050.20 0.00 0.040.070.180.09maximum0.270.160.050.590.050.040.14 0.04 0.050.050.140.04stw : sit - to - walk , sd : standard deviation , ci : confidence interval . accelerometer - defined stw task initiation appeared sooner than did motion 
 analysis data - defined task initiation under both speed conditions . the second acceleration peak 
 and first heel strike were also simultaneous . according to the bland - altman analysis , the results of a 
 simple linear regression analysis to estimate the timing of events from the acceleration 
 data are shown in table 2table 2.results of a single regression analysis to predict event timing from acceleration 
 dataspeed conditionregression equationrcomfortableonset = 2.11 + 0.59  ( timing of onset defined by 
 acceleration)0.36*maximum trunk inclination = 1.29 + 0.81  ( timing of 1st peak)0.61***heel strike = 0.73 + 0.90  ( timing of 2nd peak)0.96***maximumonset = 1.56 + 0 .67  ( timing of onset defined by 
 acceleration)0.56**maximum trunk inclination = 0.05 + 1.02  ( timing of 1st 
 peak)0.88***heel strike = 0.22 + 0.97  ( timing of 2nd peak)0.95 * * * * p < 0.05 , * * p < 0.01 , * * * p < 0.001 . for all events , fi , the 
 duration of each phase , the magnitude of the second acceleration peak , and the results of a 
 simple linear regression analysis to estimate fi are shown in table 3table 3.results of a single regression analysis to predict fi from the duration of each 
 phase and the degree of second peak accelerationmeansdregression equationrfi ( % ) comfortable70.717.5maximum95.75.4phase 1 ( s)comfortable0.550.15fi=81.76  20.06  ( phase 1)0.03maximum0.410.15fi=96.96  2.95  ( phase 1)0.01phase 2 ( s)comfortable0.850.17fi = 142.10  83.63  ( phase 2)0.64**maximum0.560.08fi = 118.85  2.95  ( phase 2)0.34*phase 1 + 2 ( s)comfortable1.40.24fi=140.81  49.96  ( phase 1 + 2)0.46**maximum0.970.15fi=108.84  13.56  ( phase 1 + 2)0.14magnitude of 2nd peak acceleration 
 ( m / s)comfortable2.251.36fi=77.13  0.28  ( 2nd peak)0.05maximum2.511.79fi=93.25 + 0.98  ( 2nd peak)0.11 * p < 0.05 , * * p < 0.01 . phase 1 : 
 duration from the onset of the sit - to - walk task to the first peak . phase 1 + 2 : duration from the onset of the 
 sit - to - walk task to the second peak . under the comfortable speed condition , the durations of phase 2 and phase 1 + 
 2 similarly , in the multiple regression analysis , only the 
 duration of phase 2 was retained for both speed conditions . stw : sit - to - walk , sd : standard deviation , ci : confidence interval . 2nd peak : the last positive peak followed by 
 rapid negative acceleraiton * p < 0.05 , * * p < 0.01 , * * * p < 0.001 * p < 0.05 , * * p < 0.01 . phase 1 : 
 duration from the onset of the sit - to - walk task to the first peak . phase 1 + 2 : duration from the onset of the 
 sit - to - walk task to the second peak the acceleration waveform recorded during the stw task was similar to that reported by 
 mellone et al.19 for the timed up - and - go 
 test . to evaluate the events identified using the acceleration waveform , we compared the 
 event timings with those of events identified using other data , namely motion analysis 
 system and foot pressure sensor data . some differences between the two measurements were 
 apparent from the results ; however , bland altman plots indicated that these differences were 
 random and small , yet sufficient to identify specific events . the timing of the first 
 acceleration peak agreed with the timing of maximum trunk inclination . therefore , the first 
 acceleration peak can be used to identify the completion of trunk inclination and the switch 
 to an extension movement . the second acceleration peak was similar to that observed by 
 zijlstra9 and mellone19 in accelerator - based gait analyses . these 
 authors defined the timing of the second peak using a peak detection method and equated this 
 peak to the heel strike . in the present study , we used the same method to identify the 
 second peak and found that the outcome compared well with the heel strike determined from 
 foot pressure sensor data . events such as the maximum trunk inclination and first heel 
 strike have been used in kinematic analyses . fs , which evaluates whether the first 
 toe off occurs before the completion of trunk extension , correlates with the fi . therefore , in this study we used 
 the timing of the first heel strike , which appears clearly on the acceleration waveform and 
 exhibits a similarly high correlation with the fi . phase 2 is defined as the period from 
 maximum trunk inclination to the first heel strike and therefore includes the transition 
 from sitting to standing to gait initiation . during this phase , motor control is important 
 for maintaining a high momentum . in addition , in the maximum speed condition , phase 2 was 
 the best predictor of fi , but the coefficient of determination was slightly lower than that 
 observed in the comfortable speed condition . in the maximum speed condition , this finding might explain why the 
 determination was lower at the maximum speed than at a comfortable speed . in the present 
 study , it was difficult to estimate the fi from the magnitude of the second acceleration 
 peak . , we clarified that accelerometer data could be used to conveniently and 
 precisely estimate the fi . it must be considered , however , that all study participants were 
 healthy young individuals . the acceleration waveforms in elderly individuals or patient 
 populations are not necessarily identical to those in healthy young individuals . in 
 addition a 
 peculiarity of this sensor renders it difficult to distinguish between dynamic acceleration 
 and gravitational acceleration ( static acceleration ) . in other words , it is difficult to 
 identify precisely whether the waveform indicates anteroposterior movement or accelerometer 
 inclination . , this method should be 
 used for the clinical analysis of elderly individuals or patient populations . to improve the 
 precision of this method , it might be useful to test other types of accelerometers such as 
 capacitance - type or equipping gyroscope accelerometers .