An adaptive gyroscope based algorithm for temporal gait analysis
Barry R. Greene, Intel Digital Health Group, Leixlip, Co. Kildare, Ireland and the TRIL Centre (email:
[email protected])
Denise McGrath, School of Physiotherapy and Performance Science, University College Dublin, Ireland (e-mail:
[email protected])
Ross O‟Neill, TRIL Centre, University College Dublin, Ireland (e-mail:
[email protected])
Karol J. O‟Donovan, Intel Digital Health Group Leixlip, Co. Kildare, Ireland and the TRIL Centre
Adrian Burns, Intel Digital Health Group Leixlip, Co. Kildare, Ireland and the TRIL Centre (e-mail:
[email protected])
Brian Caulfield, CLARITY Centre for Sensor Web Technologies and School of Physiotherapy and Performance Science, University College Dublin, Ireland (e-mail:
[email protected])
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An adaptive gyroscope based algorithm for temporal gait analysis Barry R. Greene, Denise McGrath, Ross O‟Neill, Karol J. O‟Donovan, Adrian Burns, Brian Caulfield
Abstract Body-worn kinematic sensors have been widely proposed as the optimal solution for portable, low cost, ambulatory monitoring of gait. This study aims to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using bodyworn wireless gyroscopes. Gyroscope data from nine healthy adult subjects performing four walks at four different speeds were then compared against data acquired simultaneously using two force plates and an optical motion capture system. Data from a poliomyelitis patient, exhibiting pathological gait walking with and without the aid of a crutch were also compared to the forceplate. Results show that the mean true error between the adaptive gyroscope algorithm and force plate was -4.5±14.4 ms and 43.4±6.0 ms for IC and TC points respectively in healthy subjects. Similarly, the mean true error when data from the polio patient were compared against the force plate was -75.61±27.53 ms and 99.20±46.00 ms for IC and TC points respectively. A comparison of the present algorithm against temporal gait parameters derived from an optical motion analysis system showed good agreement for nine healthy subjects at four speeds. These results show that the algorithm reported here could constitute the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.
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1. Introduction Quantitative measurement of gait has been possible for many years using laboratory based kinematic and kinetic equipment such as marker-based motion capture systems, force-platforms, and electrophysiological sensors/electrodes. Evaluation of temporal gait parameters such as stride and swing time and the variability of these parameters during walking has been shown to be useful in assessing the health of the neuromuscular system in a variety of populations including the elderly and patients with specific diseases [10, 12]. Traditional, high-cost laboratory-based methods for measurement of these parameters involve restrictive environments and long set-up times. Consequently, an extensive body of research has recently emerged that examines portable, low-cost, light-weight ambulatory monitoring systems that can potentially be deployed in a variety of settings such as a doctor‟s office or in the home. Inertial sensors consisting of either accelerometers [28], gyroscopes [1, 20, 24], or a combination of sensors [7, 15] have been widely proposed as the optimal solution for this purpose. The use of gyroscopes for the calculation of temporal parameters is a particularly attractive solution as, unlike accelerometers, gyroscopes are less sensitive to the influence of gravity and therefore the signal is less dependent on exact sensor positioning [27]. Several studies have calculated initial contact (IC) and terminal contact (TC) times from gyroscopes, and compared their results to either footswitches [1, 24] or force-plates and an optical motion capture system [2, 25], with promising results.
Previous studies used gait event detection algorithms that were applied to the gyroscope signal to identify the appropriate time-points that corresponded to either initial or terminal contact. Reliable detection of gait events can be difficult across
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varying gait-speeds and subject populations. Furthermore, the angular velocity signal derived from body-worn gyroscopes can be prone to noise and artifact. Han et al. used an adaptive approach with ankle mounted tri-axial accelerometers to discriminate gait phases in Parkinson‟s disease patients [11]. Adaptive signal processing approaches have also found utility in a number of biomedical research areas such as adaptive noise cancellation [23], heart-beat classification [8] and epileptic seizure prediction [14]. A temporal gait event detection algorithm that could adapt to the differing characteristics of the gyroscope signal, for different modes and speeds of human gait (such as shuffling & fast walking) across varying ages and gait types, as well as being robust to artifact and noise, could be beneficial.
The purpose of this study was to evaluate the performance of a gyroscope based gait analysis algorithm that includes adaptive threshold calculation and artifact rejection to enhance robustness to differing walking characteristics and speeds as well as noise in the angular velocity signal due to artifact. We sought to test the algorithm against a force-plate (which is considered the “gold standard” for measuring temporal gait parameters) and also against an optical motion-capture system (which allows the capture of a greater number of strides than a force-plate and so can be used to calculate temporal gait parameters) for the calculation of IC and TC in a group of healthy volunteers walking at slow, medium and fast pace, as well as while mimicking shuffling. We also included a patient with Poliomyelitis to test our algorithm on pathological gait.
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2. Method 2.1. Experimental set-up The gait of nine healthy subjects (8M, 1F, mean age: 29.7±3.5) was measured simultaneously using three gait measurement technologies; body-worn gyroscopes, a force-plate and an optical motion capture system. The purpose of the study was explained to all subjects before they provided signed consent to take part in the study. All speeds were self-selected and not controlled, to minimise the effect of the experimental protocol on the subjects‟ gait. Data were recorded whilst each subject performed four walks at four different selfselected speeds; normal, fast, slow and a mimicked shuffling gait along a 15m walkway in a motion analysis laboratory. In all, 144 walking trials were completed, yielding 187 separate IC and 190 TC events for subsequent data analysis. Data were also recorded from an additional poliomyelitis (polio) patient (M, age 60), exhibiting pathological gait in both lower limbs. Paralytic poliomyelitis affecting the left limb requires the patient to wear an above the knee calliper mobility device. He also has a clubbed left foot and fused left ankle. There is therefore no knee flexion/extension or ankle dorsi/plantar flexion on the left side. On the right side the patient walks with a “steppage” gait due to a dropped foot, which causes him to lift the right foot high, enabling the toes to clear the ground safely. We recorded 22 walking trials (12 with and 10 without the use of a crutch) from this patient. Six trials were neglected from analysis owing to improper contact with the forceplate and synchronization errors between acquisition systems. Fig.1 below illustrates the experimental set-up used to capture the data used in this study.
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Insert Fig 1. here
2.2. Force plate data acquisition Two AMTI (Advanced Mechanical Technology Inc.) force plates were located in the middle of a 15m walkway. The participants were instructed to walk naturally over the force plates, at four different speeds. The participants were given an opportunity to practice their different walking speeds. Subjects wore their own shoes. Force plate data were acquired at 1kHz. A 10N threshold was applied to the vertical component of the ground reaction force to detect IC and TC events. This threshold was considered high enough to avoid erroneous identification of gait events due to noise, but low enough not to miss them altogether [6]. A step was considered to be valid if the foot landed clearly within the force platform.
2.3. Optical motion data acquisition Reference kinematic data were acquired using a CODA optical motion analysis system (http://www.codamotion.com, Charnwood Dynamics Ltd, Leicestershire, UK). Two CODA cx1 units were used to acquire data, one placed at either side of the walkway. The CODA cx1 unit is a commercially available optoelectronic motion capture system for recording and analyzing human movement. Two CODA infrared light-emitting diode markers were placed on the left and right foot. Markers were positioned on the inferior lateral aspect of the heel, and the lateral aspect of the fifth metatarsal head, on the exterior of the subjects‟ training shoes. The optical kinematic data were collected at a sampling rate of 200Hz. Kinematic data were analyzed using the CODAmotion analysis software. Kinematic data from the optical motion capture system and force plates were synchronized through the analysis software. The IC and TC times for each trial were calculated manually using the algorithms reported by
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Hreljac & Marshall [13], referred to here as the Hreljac-Marshall algorithm (HMA). These algorithms are based on the values of the vertical and horizontal components of jerk equal to zero, for IC and TC respectively.
2.4. Gyroscope data acquisition Inertial monitoring unit (IMU) kinematic data was acquired using four SHIMMER (http://shimmer-research.com/) wireless sensors [4, 17], one each attached to the foot and shank of the left and right leg. For the purpose of this study, only data from the sensors on the shank were analysed. Each gyroscope sensor was attached to the anterior of each shank, oriented to capture movement about the anatomical mediolateral axis of the shank of the leg, and secured using surgical tape, half way on the imaginary line between the Tibial Tuberosity (TT) and the Lateral Malleolus (LM). Each sensor contained both a tri-axial accelerometer on the baseboard and an add-on tri-axial gyroscope daughterboard. Each was programmed to sample each axis at a rate of 102.4Hz. Data were acquired from each body-worn sensor using a custom built application developed using the BioMOBIUS software development environment (http://www.biomobius.org) [5, 18]. Gyroscope data were acquired through BioMOBIUS on a Dell Optiplex GX745 PC, with 2GB RAM and an Intel Core® 2 1.8GHz CPU. All post-processing and analysis was carried out off-line using the MATLAB® (http://www.mathworks.com/, Natick, VA, USA) programming environment. The raw gyroscope data were calibrated to derive the angular velocity vectors with respect to the sensor unit coordinate axis. A standard calibration procedure [9] was used to calibrate all gyroscopes used in this study. Before further processing, the raw gyroscope signal was low pass filtered using zero-phase 5th order Butterworth filter with a 5 Hz corner frequency. 7
The BioMOBIUS based acquisition system and the optical motion capture/force plate system were synchronised using a dedicated trigger output from the CODA motion analysis system. This trigger is activated at the initiation and deactivated at the conclusion of a capture from the CODA system. The analog trigger signal was connected to the analog-to-digital input of a separate SHIMMER device (using an analog expansion breakout board), sampled at 102.4Hz and transmitted wirelessly via Bluetooth to the BioMOBIUS acquisition software. Data from the synchronisation and
kinematic
SHIMMER
devices
were
simultaneously
recorded
within
BioMOBIUS.
2.5. Temporal gait parameters Temporal parameters of gait were derived using an algorithm which adaptively calculates thresholds in order to determine IC and TC events from the medio-lateral angular velocity of the shank. A number of authors have proposed methods for detecting IC and TC points from shank angular velocity signals. The present algorithm differs from previously reported methods in that it includes adaptive threshold calculation and artefact rejection to improve robustness to differing walking characteristics and speeds as well as noise in the angular velocity signal due to artefact. In order to ensure the angular velocity signal derived from the gyroscope has the correct polarity, the „skewness‟ of the signal (a measure of the asymmetry of the signal) is calculated for each walk. If the skewness of the filtered signal is less than zero the angular velocity signal is inverted automatically in software to ensure correct polarity of the signal when applied to the algorithm.
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2.5.1. Adaptive Threshold calculation As with previously reported gait analysis algorithms [24, 25] employing gyroscopes, the IC and TC points are defined relative to the swing phase which is more easily detected (mid-swing points manifest in the medio-lateral angular velocity signal as large positive peaks, see Fig. 4 & 5) A number of adaptive thresholds were employed to ensure accurate and robust detection of IC and TC points from the medio-lateral shank angular velocity signal over a range of speeds: In detecting the mid-swing point for each gait cycle on the medio-lateral angular velocity signal ( ML ), valid local maximum peaks of the signal should have a preceding minimum at least th1 deg/s less than the maximum, calculated as
th1 0.6 max ML In detecting the mid-swing point for each stride from the medio-lateral angular velocity signal for each leg, valid local maximum peaks of the signal should be greater than th2 deg/s. th2 is calculated as 0.8 times the mean of all data points greater than the mean value of the angular velocity signal. th2 0.8
1 N MLi ML N i 1
where ML is the mean of the medio-lateral angular velocity signal and N is the number of samples. For detecting IC points, the local minimum should have a preceding maximum at least th3 deg/s greater than the local minimum. th3 is calculated as 0.8 times the absolute value
of
th3 0.8
mean 1 N
of
all
MLi
ML
N
i 1
points
less
than
the
mean
angular
velocity;
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For detecting TC points, valid local minima should be less than th4. th4 is calculated as 0.8 times the mean of all points less than the mean value of the angular velocity signal. th4 0.8
1 N MLi ML N i 1
For IC detection, valid local minima should be less than th5, defined as the mean value of the angular velocity signal for that trial ( ML ). For TC detection, the local minimum should have a preceding maximum at least th6 deg/s greater than the local minimum, defined as th6 2th3 . For mid swing detection, if two max peaks are found within t1 seconds of each other only the greater max is considered. t1 is defined as 0.5 seconds or t1 0.5 f s . Similarly for IC and TC detection following mid-swing detection, only data within ±t2 seconds is considered, defined as 1.5 seconds or t1 1.5 f s . Fig. 2 is a flowchart detailing the operation of the adaptive gyroscope algorithm. Insert Fig. 2 here
2.5.2. Artefact rejection Temporal gait parameters are calculated from the gait events, IC and TC. An artefact rejection routine is employed to remove spurious temporal parameters that are calculated from noisy or artefactual gyroscope data. This routine is also designed to account for missing and extra IC and TC points detected by the adaptive gyroscope algorithm. Artefact rejection is based on two strands; examination of temporal sequence information and examination of times between successive characteristic points (cycle times)
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a) Temporal sequence information Once all characteristic points are detected using the present algorithm, each point is assigned a numerical label 1 to 4, where; 1. right IC, 2. left TC, 3. left IC and 4. right TC. A correct gait cycle (if starting on a right IC) will then follow the sequence 1,2,3,4. This is determined by subtracting each from label the previous label. Those samples which do not follow this sequence and so do not produce a difference equal to either 3 or 1 are deemed artefact. Additional checks were also carried out to ensure that a TC is always preceded by an IC and an IC preceded by a mid-swing point. b) Gait cycle information The time between adjacent gait characteristic points is calculated for each set of characteristic points (right IC, left TC, left IC, right TC). This is commonly referred to as gait cycle time when right IC is the characteristic point used. If the difference between any successive characteristic point is greater than 2.5 seconds the associated characteristic point is flagged as artefact. Similarly, if the difference between any successive IC or TC point is zero seconds, the associated point is flagged as artefact. Furthermore, any gait parameters with a negative or zero value are also neglected from analysis.
2.6. Statistical analysis IC and TC points derived from the body-worn gyroscopes using the algorithm described here were compared against those derived from the optical motion capture system (using the HMA) and the force plate using a number of metrics. The true error is defined as the difference in time (in milliseconds) between the IC or TC time detected using the force plate, and the time that same point is detected using the adaptive gyroscope algorithm. The IC and TC characteristic points derived using the
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present algorithm and the motion capture system were used to calculate the four temporal gait parameters; stride time, stance time, swing time and step time. Stride time is defined as the time from IC of one foot to IC of the same foot. The stance time is defined as the time between IC and TC of the same foot. Similarly swing time is defined as the time between TC and IC on the same foot. Step time is calculated as the time between IC on one foot and IC on the other foot. In this study, the data for left and right feet for each temporal gait parameter were merged as it was assumed that gait asymmetry would not be an issue in our control population. Temporal gait parameters derived from shank angular velocity signals were compared against those obtained from the HMA using the mean percentage error, intraclass correlation coefficient (ICC(2, k) [26] and the Bland-Altman method [3]. BlandAltman plots are shown to illustrate graphically the agreement between temporal parameters, simultaneously derived using the adaptive gyroscope algorithm and the HMA.
3. Results 3.1. Force plate results Table 1 below compares the performance of the adaptive gyroscope-based algorithm in terms of true error against the force plate in detecting IC and TC points. Insert Table 1 here In total, the experiment yielded 187 IC and 190 TC points for comparison. The mean true error for IC across all speeds was -4.5±14.4 ms while for TC the mean true error was 43.4±6.0 ms. Fig.3 graphically illustrates the range of true error between IC and TC point derived automatically from the gyroscope at four different self-selected speeds against those derived using the force plate.
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Insert Fig. 3 here Similarly walking trials recorded from the polio patient yielded 28 IC and 26 TC points for comparison. The mean true error for IC -75.61±27.53 ms while for TC the mean true error was 99.20±46.00 ms. For walking trials where the patient made use of a crutch the mean true error for IC and TC were -80.98±28.29 ms and 106.83±56.62 ms respectively. Similarly, for trials where the patient did not use a crutch the mean true error for IC and TC were -70.96±27.45 ms and 92.65±36.69 respectively. Fig.4 shows the shank medio-lateral angular velocity signals as well as the force plate timings (GRF curves) for the polio patient. Insert Fig. 4 here
3.2. Optical motion capture system results The mean velocities (taken across all subjects) for each of the walking speeds were as follows; fast: 1.56 m/s (5.61 km/h); normal: 1.10 m/s (3.95 km/h); slow: 0.65 m/s (2.36 km/h); shuffle: 0.48 m/s (1.71 km/h). Table 3 below gives the results for agreement between temporal gait parameters derived from both adaptive gyroscope algorithm and the HMA at four different speeds. The results show that there is a good agreement (ICC > 0.80) between both for each temporal gait parameter. Insert Table 2 here Fig.5 is a Bland-Altman plot for stride times calculated from data acquired from the adaptive gyroscope algorithm and data acquired using the HMA on kinematic data acquired from the optical motion analysis system during fast walking. Insert Fig. 5 here
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4. Discussion A vital requisite for a portable gait monitoring system is its ability to accurately and reliably determine when IC and TC occur. In this study, we sought to investigate whether the temporal parameters obtained from nine healthy subjects, as calculated by the adaptive gyroscope algorithm using body-worn gyroscopes, corresponded to those simultaneously obtained from the same subject by two force plates and a markerbased motion analysis system. In addition we sought to show that the present algorithm is robust in automatically detecting the gait characteristic points from the shank inertial sensor data of a patient exhibiting pathological gait. Adaptive algorithms have recently been employed throughout the literature in developing robust classification and detection algorithms. The algorithm presented here applies a similar concept in its ability to automatically adapt to individual gait features present in the gyroscope signal, through the inclusion of adaptive thresholds. Previous research by the authors [22] successfully implemented a previously reported algorithm for gait event detection from body-worn gyroscopes [25]. However, upon applying this algorithm to our present data set, we experienced difficulties in identifying gait events across varying speeds and subjects. Whilst this algorithm was successful in event detection at slow and normal walking speeds, we found it to be unsuitable for some trials where subjects had been mimicking shuffling or walking very fast. Furthermore, we found that our implementation of this algorithm was prone to missing and extra detection of IC and TC points due to noise and artefact that can appear on the gyroscope derived angular velocity signal. Fig.6 compares the performance of the adaptive gyroscope algorithm to that of our implementation of the Salarian et al. algorithm in detecting gait events on two samples of over-ground walking at two different speeds, from two separate healthy subjects. The graphs on
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the left hand side show the IC and TC points, as identified by the adaptive algorithm. The graphs on the right hand side show the IC and TC points as identified by our implementation of an algorithm previously presented in the literature [25]. Insert Fig. 6 here Body-worn gyroscopes and associated algorithms to assess gait have been employed in other studies, with four studies in particular reporting successful validations with reference systems [2, 15, 24, 25]. For comparison, these studies showed average true errors (±SD) of -2 ms (±16), -8.7 ms (±12.5), 16.6 ms (±11.9) and -14ms (±23), for IC events, and -35ms, -2.9 (±26.8), 3.7ms (±26.5) and 23ms (±28), for TC events, respectively. Using our normal walking speed data for comparison, an average error of -2.3 ms (±4.87 ms) for IC and 43.61 ms (±15.30 ms) for TC, between the adaptive gyroscope algorithm and the force plate was found. We can see from these results that the algorithm presented here compares favourably to previous gait studies using gyroscopes in the detection of IC. In our study we found the detection of TC to be biased across all speeds by an average 43.4 ms. A similar finding is observed in Sabatini et al. [24], who found a bias in the opposite direction of -35 ms for TC detection from foot worn gyroscopes. However, the low SD of average error for TC detection across all speeds in this study is encouraging. A systematic bias in the adaptive gyroscope algorithm‟s detection of TC may be related to the fact that the point in the angular velocity signal thought to represent TC may in fact represent a slightly different point in the gait cycle owing to the physical separation of the shank mounted gyroscope from the toe. In identifying a consistent bias in the detection of TC, it is possible that calculations can be corrected by taking the systematic error into account. However, in many cases this would not be necessary. If this system is ultimately being used to collect baseline data for long-term
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gait monitoring in the field for example, then any variation/deterioration in gait patterns will be flagged with respect to baseline measurements, regardless of the systematic delay in TC identification. Moreover, Miller et al. [19] argue that the timing of foot-off events may not be as crucial as foot-contact events because the start and end points of the gait-cycle are defined by foot contact events. The analysis of stride time is used for the time normalisation of data per gait cycle that is prevalent in almost all aspects of gait analysis. In this study, data were analysed from the gyroscope mounted on each shank rather than data from the gyroscopes mounted on each foot. This was because the signals from the foot mounted gyroscopes were found to be more prone to noise and artefact, owing to vibration from heel impact. This finding is in contrast to that found in previous research by Jasiewicz et al. [15] and so may be a limitation of the sensor used in this study rather than being generally true for all foot-mounted gyroscopes. We included a mimicked shuffling gait in this study to test the compliance of the algorithm to differing gait dynamics. It is of particular interest to note that the performance of the adaptive algorithm during the mimicked shuffling gait was upheld, allowing easy detection of gait events, as can be seen in Fig. 6. However, it was difficult to clearly identify IC and TC during shuffling using the force-plate, as the increase and decrease of the force exerted on the plate was much more gradual. In addition, both feet were often simultaneously on the force plate, resulting in a large amount of unusable steps. This made the comparison difficult, as the force plate no longer represented the “gold-standard” for this walking speed. Nevertheless, the adaptive algorithm presented here has been shown to be robust in identifying IC and TC in this condition, and may be considered a promising method for the calculation of gait events at very slow speeds that emulate a shuffling gait.
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A systematic bias in the adaptive gyroscope algorithm‟s detection of IC and TC is also evident in the mean true error comparison against the forceplate in the polio patient‟s data. Similar to healthy subjects, TC detection is slightly less accurate than IC detection, particularly on the left foot where a more irregular gait was exhibited. However, as previously discussed, systematic errors can be corrected for, and the low standard deviation in the data is encouraging. As can be seen in figure 3, the adaptive algorithm successfully extracted IC and TC points from the gyroscope signal, despite left and right legs producing differing signals. Kotiadis et al. [16] deemed timing limits of 150ms for early and late heel down detection as acceptable in their system for control of a drop-foot stimulator. Our results fall well within these limits for pathological gait. The second part of our analysis focused on comparing the temporal parameters from a larger number of steps captured by the optical motion capture system to those simultaneously captured by the gyroscopes. We used a method that has been previously described in the literature by Hreljac and Marshall (2000) (HMA) to extract IC and TC times from kinematic data captured using a marker-based motion capture system, resulting in absolute average errors of 4.7 and 5.6 ms, respectively, using a force plate as a reference. We compared stride, swing, stance and step times as calculated by the adaptive gyroscope algorithm with the results produced using the HMA. We demonstrated good to excellent agreement (ICC(2,k) > 0.8) for all parameters, across all speeds, as can be seen in table 3. We experienced some of the same problems with the HMA as outlined in O‟Connor et al. [21]. We were unable to extract IC and TC times automatically as there are multiple peaks in the acceleration curves and in many cases, the HMA was unable to
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identify the appropriate peak. The location of the local maximum points had to be verified manually through visual inspection to ensure the correct points were identified, by looking at a sagittal view of the markers that could be advanced frame by frame. This was a laborious process when compared to the ease at which large amounts of data were automatically processed using the adaptive gyroscope algorithm described here, with no checking of the data required. We have assessed the performance of the adaptive gyroscope algorithm reported here across a range of walking speeds, and we have found that the inclusion of adaptive threshold calculation for IC and TC determination as well as and artefact rejection has resulted in good to excellent agreement for temporal parameters, compared to another kinematic method.
Acknowledgment This research was completed as part of a wider programme of research within the TRIL Centre, (Technology Research for Independent Living). The TRIL Centre is a multi-disciplinary research centre, bringing together researchers from UCD, TCD, NUIG, Intel, and GE Healthcare, funded by Intel, GE Healthcare and IDA Ireland. http://www.trilcentre.org. The authors would like to thank Dr. Emer Doheny for providing useful feedback on the manuscript as well as Mr. Ben Dromey for his help with graphically detailing the experimental layout.
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Figure captions Figure 1: Experimental setup to acquire data simultaneously from body-worn gyroscopes, motion capture system and two force plates. Figure 2: Flowchart detailing operation of adaptive kinematic algorithm Figure 3: True error between force plate and adaptive gyroscope algorithm for nine healthy subjects walking at four different speeds. Figure 4: Shank medio-lateral angular velocity signals and GRF curves from a polio patient exhibiting pathological gait in both limbs. Mid-swing, IC and TC points are marked for each leg. True errors between force-plate and adaptive gyroscope algorithm in detecting IC and TC points are shown graphically (true errors (in ms) are as follows; right IC: 16.63, left IC: -74.26, right IC: 75.68, left IC; 148.73). Figure 5: Bland Altman plot illustrating the agreement between stride times from nine healthy subjects calculated using the adaptive gyroscope algorithm and those derived from the HMA using the optical motion capture system during fast walking. Figure 6: The top two panels show a sample of shank medio-lateral shank angular velocity signal derived from a gyroscope mounted on the shank while a healthy subject was mimicking shuffling while the bottom two panels show a sample shank angular velocity signal from healthy subject while walking fast. The performance of the adaptive gyroscope algorithm against the Salarian et al. algorithm for both walks is shown.
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Figures
Figure 1
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Start Data can be obtained using any mode of kinematic data acquisition
Gyroscope data
Calibration
Low pass filter medio-lateral angular velocity
LPF Correct for orientation of body-worn sensor
Skewness
Adaptive threshold calculation
Detect HS and TO points
Calculate adaptive threshold parameters from filtered medio-lateral angular velocity signal
Detect heel-strike and toe-off points from filtered angular velocity signals using adaptive thresholds
Calculate temporal gait parameters
Artifact rejection
Temporal gait parameters Figure 2
23
80
80
60
60
40
40
Toe off true error [ms]
Heel strike true error [ms]
True error between forceplate and adaptive gyroscope algorithm
20 0
20 0
-20
-20
-40
-40
-60
-60 Shuffle
Slow
Med
Walking speed
Fast
Shuffle
Slow
Med
Fast
Walking speed
Figure 3
24
Figure 4
25
Agreement between adaptive gyroscope algorihm and HMA stride times during fast walking 0.15
Difference between stride times [s]
0.1
Intraclass correlation coefficient ICC(2,k) = 0.9571
0.05
0
-0.05
-0.1
-0.15
-0.2
1
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
Mean of stride times [s]
Figure 5
26
Figure 6
27
Table captions Table 1: Mean true error (mean TE ± SD) in IC and TC between force plate and the adaptive gyroscope algorithm for each of the four walking speeds. Table 2: Mean results for agreement between temporal gait parameters derived from the adaptive gyroscope algorithm and the HMA (using the optical motion capture system) at four different speeds. Mean error refers to mean percentage error between the adaptive gyroscope algorithm and the HMA for each temporal gait parameter.
28
Tables
Shuffle
Slow
Normal
Fast
IC
-1.39±36.14
-7.62±8.27
-2.30± 4.87
-6.50±9.15
TC
51.63±25.85
45.28±8.19
43.61±15.30
32.90±14.11
Table 1
29
Shuffle
Stride time Stance time Swing time Step time
True error [ms]
Error [%]
-0.03
Slow ICC(2,k)
True error [ms]
Error [%]
3.03
0.96
-4.86
97.27
10.5
0.86
-90.2
29.55
-24.31
11.4
Normal ICC(2,k)
True error [ms]
Error [%]
2.23
0.97
-6.35
74.09
9.16
0.8
0.83
-89.76
20.93
0.88
-0.24
5.21
Fast ICC(2,k)
True error [ms]
Error [%]
ICC(2,k)
1.87
0.98
-5.14
2.58
0.97
35.43
8.03
0.92
31.47
6.56
0.93
0.82
-58.04
14.07
0.93
-43.53
14.46
0.94
0.91
-6.64
5.59
0.89
-5.74
10.14
0.94
Table 2
30
