Quantification of gait parameters with inertial sensors and inverse kinematics

Measuring human gait is important in medicine to obtain outcome parameter for therapy, for instance in Parkinson’s disease. Recently, small inertial sensors became available which allow for the registration of limb-position outside of the limited space of gait laboratories. The computation of gait parameters based on such recordings has been the subject of many scientific papers. We want to add to this knowledge by presenting a 4-segment leg model which is based on inverse kinematic and Kalman filtering of data from inertial sensors. To evaluate the model, data from four leg segments (shanks and thighs) were recorded synchronously with accelerometers and gyroscopes and a 3D motion capture system while subjects (n = 12) walked at three different velocities on a treadmill. Angular position of leg segments was computed from accelerometers and gyroscopes by Kalman filtering and compared to data from the motion capture system. The four-segment leg model takes the stance foot as a pivotal point and computes the position of the remaining segments as a kinematic chain (inverse kinematics). Second, we evaluated the contribution of pelvic movements to the model and evaluated a five segment model (shanks, thighs and pelvis) against ground-truth data from the motion capture system and the path of the treadmill.ResultsWe found the precision of the Kalman filtered angular position is in the range of 2–6° (RMS error). The 4-segment leg model computed stride length and length of gait path with a constant undershoot of 3% for slow and 7% for fast gait. The integration of a 5th segment (pelvis) into the model increased its precision. The advantages of this model and ideas for further improvements are discussed.     

Stride length determination during overground running using a single foot-mounted inertial measurement unit

From a research perspective, detailed knowledge about stride length (SL) is important for coaches, clinicians and researchers because together with stride rate it determines the speed of locomotion. Moreover, individual SL vectors represent the integrated output of different biomechanical determinants and as such provide valuable insight into the control of running gait. In recent years, several studies have tried to estimate SL using body-mounted inertial measurement units (IMUs) and have reported promising results. However, many studies have used systems based on multiple sensors or have only focused on estimating SL for walking. Here we test the concurrent validity of a single foot-mounted, 9-degree of freedom IMU to estimate SL for running. We employed a running-specific, Kalman filter based zero-velocity update (ZUPT) algorithm to calculate individual SL vectors with the IMU and compared the results to SLs that were simultaneously recorded by a 6-camera 3D motion capture system. The results showed that the analytical procedures were able to successfully identify all strides that were recorded by the camera system and that excellent levels of absolute agreement (ICC(3,1) = 0.955) existed between the two methods. The findings demonstrate that individual SL vectors can be accurately estimated with a single foot-mounted IMU when running in a controlled laboratory setting.     

Influence of IMU position and orientation placement errors on ground reaction force estimation

Wearable inertial measurement units (IMU) have been proposed to estimate GRF outside of specialized laboratories, however the precise influence of sensor placement error on accuracy is unknown. We investigated the influence of IMU position and orientation placement errors on GRF estimation accuracy. Methods: Kinematic data from twelve healthy subjects based on marker trajectories were used to simulate 1848 combinations of sensor position placement errors (range ± 100 mm) and orientation placement errors (range ± 25°) across eight body segments (trunk, pelvis, left/right thighs, left/right shanks, and left/right feet) during normal walking trials for baseline cases when a single sensor was misplaced and for the extreme cases when all sensors were simultaneously misplaced. Three machine learning algorithms were used to estimate GRF for each placement error condition and compared with the no placement error condition to evaluate performance. Results: Position placement errors for a single misplaced IMU reduced vertical GRF (VGRF), medio-lateral GRF (MLGRF), and anterior-posterior GRF (APGRF) estimation accuracy by up to 1.1%, 2.0%, and 0.9%, respectively and for all eight simultaneously misplaced IMUs by up to 4.9%, 6.0%, and 4.3%, respectively. Orientation placement errors for a single misplaced IMU reduced VGRF, MLGRF, and APGRF estimation accuracy by up to 4.8%, 7.3%, and 1.5%, respectively and for all eight simultaneously misplaced IMUs by up to 20.8%, 23.4%, and 12.3%, respectively. Conclusion: IMU sensor misplacement, particularly orientation placement errors, can significantly reduce GRF estimation accuracy and thus measures should be taken to account for placement errors in implementations of GRF estimation via wearable IMUs.     

Human crawling performance and technique revealed by inertial measurement units

Human crawling performance and technique are of broad interest to roboticists, biomechanists, and military personnel. This study explores the variables that define crawling performance in the context of an outdoor obstacle course used by military organizations worldwide to evaluate the effects of load and personal equipment on warfighter performance. Crawling kinematics, measured from four body-worn inertial measurement units (IMUs) attached to the upper arms and thighs, are recorded for thirty-three participants. The IMU data is distilled to four metrics of crawling performance; namely, crawl speed, crawl stride time, ipsilateral limb coordination, and contralateral limb coordination. We hypothesize that higher performance (as identified by higher crawl speeds) is associated with more coordinated limbs and lower stride times. A cluster analysis groups participants into high and low performers exhibiting statistically significant differences across the four performance metrics. In particular, high performers exhibit superior limb coordination associated with a “diagonal gait” in which contralateral limbs move largely in-phase to produce faster crawl speeds and shorter crawl stride times. In contrast, low performers crawl at slower speeds with longer crawl stride times and less limb coordination. Beyond these conclusions, a major contribution of this study is a method for deploying wearable IMUs to study crawling in contextually relevant (i.e. non-laboratory) environments.     

Dynamic accuracy of inertial measurement units during simple pendulum motion

A motion measurement system based on inertial measurement units (IMUs) has been suggested as an alternative to contemporary video motion capture. This paper reports an investigation into the accuracy of IMUs in estimating 3D orientation during simple pendulum motion. The IMU vendor's (XSens Technologies) accuracy claim of 3 degrees root mean squared (RMS) error is tested. IMUs are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. The motion of a pendulum swing was measured using both IMUs and video motion capture as a reference. The IMU raw data were processed by the Kalman filter algorithm supplied by the vendor and a custom fusion algorithm developed by the authors. The IMU measurement of pendulum motion using the vendor's Kalman filter algorithm did not compare well with the video motion capture with a RMS error of between 8.5 degrees and 11.7 degrees depending on the length and type of pendulum swing. The maximum orientation error was greater than 30 degrees , occurring approximately eight seconds into the motion. The custom fusion algorithm estimation of orientation compared well with the video motion capture with a RMS error of between 0.8 degrees and 1.3 degrees . Future research should concentrate on developing a general purpose fusion algorithm and vendors of IMUs should provide details about the errors to be expected in different measurement situations, not just those in a 'best case' scenario.     

Dynamic accuracy of inertial measurement units during simple pendulum motion

A motion measurement system based on inertial measurement units (IMUs) has been suggested as an alternative to contemporary video motion capture. This paper reports an investigation into the accuracy of IMUs in estimating 3D orientation during simple pendulum motion. The IMU vendor's (XSens Technologies) accuracy claim of 3° root mean squared (RMS) error is tested. IMUs are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. The motion of a pendulum swing was measured using both IMUs and video motion capture as a reference. The IMU raw data were processed by the Kalman filter algorithm supplied by the vendor and a custom fusion algorithm developed by the authors. The IMU measurement of pendulum motion using the vendor's Kalman filter algorithm did not compare well with the video motion capture with a RMS error of between 8.5° and 11.7° depending on the length and type of pendulum swing. The maximum orientation error was greater than 30°, occurring approximately eight seconds into the motion. The custom fusion algorithm estimation of orientation compared well with the video motion capture with a RMS error of between 0.8° and 1.3°. Future research should concentrate on developing a general purpose fusion algorithm and vendors of IMUs should provide details about the errors to be expected in different measurement situations, not just those in a ‘best case’ scenario.     

IMU-based ambulatory walking speed estimation in constrained treadmill and overground walking

This study evaluated the performance of a walking speed estimation system based on using an inertial measurement unit (IMU), a combination of accelerometers and gyroscopes. The walking speed estimation algorithm segments the walking sequence into individual stride cycles (two steps) based on the inverted pendulum-like behaviour of the stance leg during walking and it integrates the angular velocity and linear accelerations of the shank to determine the displacement of each stride. The evaluation was performed in both treadmill and overground walking experiments with various constraints on walking speed, step length and step frequency to provide a relatively comprehensive assessment of the system. Promising results were obtained in providing accurate and consistent walking speed/step length estimation in different walking conditions. An overall percentage root mean squared error (%RMSE) of 4.2 and 4.0% was achieved in treadmill and overground walking experiments, respectively. With an increasing interest in understanding human walking biomechanics, the IMU-based ambulatory system could provide a useful walking speed/step length measurement/control tool for constrained walking studies.     

IMU-based ambulatory walking speed estimation in constrained treadmill and overground walking

This study evaluated the performance of a walking speed estimation system based on using an inertial measurement unit (IMU), a combination of accelerometers and gyroscopes. The walking speed estimation algorithm segments the walking sequence into individual stride cycles (two steps) based on the inverted pendulum-like behaviour of the stance leg during walking and it integrates the angular velocity and linear accelerations of the shank to determine the displacement of each stride. The evaluation was performed in both treadmill and overground walking experiments with various constraints on walking speed, step length and step frequency to provide a relatively comprehensive assessment of the system. Promising results were obtained in providing accurate and consistent walking speed/step length estimation in different walking conditions. An overall percentage root mean squared error (%RMSE) of 4.2 and 4.0% was achieved in treadmill and overground walking experiments, respectively. With an increasing interest in understanding human walking biomechanics, the IMU-based ambulatory system could provide a useful walking speed/step length measurement/control tool for constrained walking studies.     

Estimation of stride length in level walking using an inertial measurement unit attached to the foot: a validation of the zero velocity assumption during stance

In a variety of applications, inertial sensors are used to estimate spatial parameters by double integrating over time their coordinate acceleration components. In human movement applications, the drift inherent to the accelerometer signals is often reduced by exploiting the cyclical nature of gait and under the hypothesis that the velocity of the sensor is zero at some point in stance. In this study, the validity of the latter hypothesis was investigated by determining the minimum velocity of progression of selected points of the foot and shank during the stance phase of the gait cycle while walking at three different speeds on level ground. The errors affecting the accuracy of the stride length estimation resulting from assuming a zero velocity at the beginning of the integration interval were evaluated on twenty healthy subjects. Results showed that the minimum velocity of the selected points on the foot and shank increased as gait speed increased. Whereas the average minimum velocity of the foot locations was lower than 0.011 m/s, the velocity of the shank locations were up to 0.049 m/s corresponding to a percent error of the stride length equal to 3.3%. The preferable foot locations for an inertial sensor resulted to be the calcaneus and the lateral aspect of the rearfoot. In estimating the stride length, the hypothesis that the velocity of the sensor can be set to zero sometimes during stance is acceptable only if the sensor is attached to the foot.     

A comparison of algorithms for body-worn sensor-based spatiotemporal gait parameters to the GAITRite electronic walkway

This study compares the performance of algorithms for body-worn sensors used with a spatiotemporal gait analysis platform to the GAITRite electronic walkway. The mean error in detection time (true error) for heel strike and toe-off was 33.9 ± 10.4 ms and 3.8 ± 28.7 ms, respectively. The ICC for temporal parameters step, stride, swing and stance time was found to be greater than 0.84, indicating good agreement. Similarly, for spatial gait parameters--stride length and velocity--the ICC was found to be greater than 0.88. Results show good to excellent concurrent validity in spatiotemporal gait parameters, at three different walking speeds (best agreement observed at normal walking speed). The reported algorithms for body-worn sensors are comparable to the GAITRite electronic walkway for measurement of spatiotemporal gait parameters in healthy subjects.     

Classification of deadlift biomechanics with wearable inertial measurement units

The deadlift is a compound full-body exercise that is fundamental in resistance training, rehabilitation programs and powerlifting competitions. Accurate quantification of deadlift biomechanics is important to reduce the risk of injury and ensure training and rehabilitation goals are achieved. This study sought to develop and evaluate deadlift exercise technique classification systems utilising Inertial Measurement Units (IMUs), recording at 51.2 Hz, worn on the lumbar spine, both thighs and both shanks. It also sought to compare classification quality when these IMUs are worn in combination and in isolation. Two datasets of IMU deadlift data were collected. Eighty participants first completed deadlifts with acceptable technique and 5 distinct, deliberately induced deviations from acceptable form. Fifty-five members of this group also completed a fatiguing protocol (3-Repition Maximum test) to enable the collection of natural deadlift deviations. For both datasets, universal and personalised random-forests classifiers were developed and evaluated. Personalised classifiers outperformed universal classifiers in accuracy, sensitivity and specificity in the binary classification of acceptable or aberrant technique and in the multi-label classification of specific deadlift deviations. Whilst recent research has favoured universal classifiers due to the reduced overhead in setting them up for new system users, this work demonstrates that such techniques may not be appropriate for classifying deadlift technique due to the poor accuracy achieved. However, personalised classifiers perform very well in assessing deadlift technique, even when using data derived from a single lumbar-worn IMU to detect specific naturally occurring technique mistakes.     

3D gait assessment in young and elderly subjects using foot-worn inertial sensors

This study describes the validation of a new wearable system for assessment of 3D spatial parameters of gait. The new method is based on the detection of temporal parameters, coupled to optimized fusion and de-drifted integration of inertial signals. Composed of two wirelesses inertial modules attached on feet, the system provides stride length, stride velocity, foot clearance, and turning angle parameters at each gait cycle, based on the computation of 3D foot kinematics. Accuracy and precision of the proposed system were compared to an optical motion capture system as reference. Its repeatability across measurements (test-retest reliability) was also evaluated. Measurements were performed in 10 young (mean age 26.1±2.8 years) and 10 elderly volunteers (mean age 71.6±4.6 years) who were asked to perform U-shaped and 8-shaped walking trials, and then a 6-min walking test (6 MWT). A total of 974 gait cycles were used to compare gait parameters with the reference system. Mean accuracy±precision was 1.5±6.8 cm for stride length, 1.4±5.6 cm/s for stride velocity, 1.9±2.0 cm for foot clearance, and 1.6±6.1° for turning angle. Difference in gait performance was observed between young and elderly volunteers during the 6 MWT particularly in foot clearance. The proposed method allows to analyze various aspects of gait, including turns, gait initiation and termination, or inter-cycle variability. The system is lightweight, easy to wear and use, and suitable for clinical application requiring objective evaluation of gait outside of the lab environment.     

Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes

In this study we describe an ambulatory system for estimation of spatio-temporal parameters during long periods of walking. This original method based on wavelet analysis is proposed to compute the values of temporal gait parameters from the angular velocity of lower limbs. Based on a mechanical model, the medio-lateral rotation of the lower limbs during stance and swing, the stride length and velocity are estimated by integration of the angular velocity. Measurement's accuracy was assessed using as a criterion standard the information provided by foot pressure sensors. To assess the accuracy of the method on a broad range of performance for each gait parameter, we gathered data from young and elderly subjects. No significant error was observed for toe-off detection, while a slight systematic delay (10 ms on average) existed between heelstrike obtained from gyroscopes and footswitch. There was no significant difference between actual spatial parameters (stride length and velocity) and their estimated values. Errors for velocity and stride length estimations were 0.06 m/s and 0.07 m, respectively. This system is light, portable, inexpensive and does not provoke any discomfort to subjects. It can be carried for long periods of time, thus providing new longitudinal information such as stride-to-stride variability of gait. Several clinical applications can be proposed such as outcome evaluation after total knee or hip replacement, external prosthesis adjustment for amputees, monitoring of rehabilitation progress, gait analysis in neurological diseases, and fall risk estimation in elderly.     

Measurement of the temporal and spatial parameters of gait using a microcomputer based system

This paper describes a system for measuring the temporal and spatial parameters of gait. The basis of the system in a resistive grid walkway which is controlled by a microcomputer which also collects, processes and stores the data from the walkway. The data obtained from the system, including the temporal and spatial parameters of gait such as step and stride lengths, the durations of swing and support phases of the gait cycle and walking speed, are presented in both numerical and graphical forms. Accuracy to within 1 cm has been verified by analysing videotapes of foot placement during a walk. Special emphasis has been placed on making the system software user-friendly. The presentation of date uses several types of display, from a simple graphical summary of the walk statistics to a more complete report showing all the data from each stride. In the year during which the walkway system has been operational, it has been found easy to use by both subjects and operators, and it produces very useful data.     

Symmetrical and asymmetrical gaits in the mouse: patterns to increase velocity

The gaits of the adult SWISS mice during treadmill locomotion at velocities ranging from 15 to 85 cm s^–1 have been analysed using a high-speed video camera combined with cinefluoroscopic equipment. The sequences of locomotion were analysed to determine the various space and time parameters of limb kinematics. We found that velocity adjustments are accounted for differently by the stride frequency and the stride length if the animal showed a symmetrical or an asymmetrical gait. In symmetrical gaits, the increase of velocity is provided by an equal increase in the stride length and the stride frequency. In asymmetrical gaits, the increase in velocity is mainly assured by an increase in the stride frequency in velocities ranging from 15 to 29 cm s^–1. Above 68 cm s^–1, velocity increase is achieved by stride length increase. In velocities ranging from 29 to 68 cm s^–1, the contribution of both variables is equal as in symmetrical gaits. Both stance time and swing time shortening contributed to the increase of the stride frequency in both gaits, though with a major contribution from stance time decrease. The pattern of locomotion obtained in a normal mouse should be used as a template for studying locomotor control deficits after lesions or in different mutations affecting the nervous system.     

The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units

The use of inertial measurement units (IMUs) for gait analysis has emerged as a tool for clinical applications. Shank gyroscope signals have been utilized to identify heel-strike and toe-off, which serve as the foundation for calculating temporal parameters of gait such as single and double limb support time. Recent publications have shown that toe-off occurs later than predicted by the dual minima method (DMM), which has been adopted as an IMU-based gait event detection algorithm. In this study, a real-time algorithm, Noise-Zero Crossing (NZC), was developed to accurately compute temporal gait parameters. Our objective was to determine the concurrent validity of temporal gait parameters derived from the NZC algorithm against parameters measured by an instrumented walkway. The accuracy and precision of temporal gait parameters derived using NZC were compared to those derived using the DMM. The results from Bland-Altman Analysis showed that the NZC algorithm had excellent agreement with the instrumented walkway for identifying the temporal gait parameters of Gait Cycle Time (GCT), Single Limb Support (SLS) time, and Double Limb Support (DLS) time. By utilizing the moment of zero shank angular velocity to identify toe-off, the NZC algorithm performed better than the DMM algorithm in measuring SLS and DLS times. Utilizing the NZC algorithm’s gait event detection preserves DLS time, which has significant clinical implications for pathologic gait assessment.     

Ultrasonic motion analysis system--measurement of temporal and spatial gait parameters

The duration of stance and swing phase and step and stride length are important parameters in human gait. In this technical note a low-cost ultrasonic motion analysis system is described that is capable of measuring these temporal and spatial parameters while subjects walk on the floor. By using the propagation delay of sound when transmitted in air, this system is able to record the position of the subjects' feet. A small ultrasonic receiver is attached to both shoes of the subject while a transmitter is placed stationary on the floor. Four healthy subjects were used to test the device. Subtracting positions of the foot with zero velocity yielded step and stride length. The duration of stance and swing phase was calculated from heel-strike and toe-off. Comparison with data obtained from foot contact switches showed that applying two relative thresholds to the speed graph of the foot could reliably generate heel-strike and toe-off. Although the device is tested on healthy subjects in this study, it promises to be extremely valuable in examining pathological gait. When gait is asymmetrical, walking speed is not constant or when patients do not completely lift their feet, most existing devices will fail to correctly assess the proper gait parameters. Our device does not have this shortcoming and it will accurately demonstrate asymmetries and variations in the patient's gait. As an example, the recording of a left hemiplegic patient is presented in the discussion.     

Validation of an ambient system for the measurement of gait parameters

Fall risk in elderly people is usually assessed using clinical tests. These tests consist in a subjective evaluation of gait performed by healthcare professionals, most of the time shortly after the first fall occurrence. We propose to complement this one-time, subjective evaluation, by a more quantitative analysis of the gait pattern using a Microsoft Kinect. To evaluate the potential of the Kinect sensor for such a quantitative gait analysis, we benchmarked its performance against that of a gold-standard motion capture system, namely the OptiTrack. The “Kinect” analysis relied on a home-made algorithm specifically developed for this sensor, whereas the OptiTrack analysis relied on the “built-in” OptiTrack algorithm. We measured different gait parameters as step length, step duration, cadence, and gait speed in twenty-five subjects, and compared the results respectively provided by the Kinect and OptiTrack systems. These comparisons were performed using Bland-Altman plot (95% bias and limits of agreement), percentage error, Spearman’s correlation coefficient, concordance correlation coefficient and intra-class correlation. The agreement between the measurements made with the two motion capture systems was very high, demonstrating that associated with the right algorithm, the Kinect is a very reliable and valuable tool to analyze gait. Importantly, the measured spatio-temporal parameters varied significantly between age groups, step length and gait speed proving the most effective discriminating parameters. Kinect-monitoring and quantitative gait pattern analysis could therefore be routinely used to complete subjective clinical evaluation in order to improve fall risk assessment during rehabilitation.     

An effortless procedure to align the local frame of an inertial measurement unit to the local frame of another motion capture system

Inertial measurement units (IMUs) offer great opportunities to analyze segmental and joints kinematics. When combined with another motion capture system (MCS), for example, to validate new IMU-based applications or to develop mixed systems, it is necessary to align the local frame of the IMU sensors to the local frame of the MCS. Currently, all alignment methods use landmarks on the IMU's casing. Therefore, they can only be used with well-documented IMUs and they are prone to error when the IMU's casing is small. This study proposes an effortless procedure to align the local frame of any IMU to the local frame of any other MCS able to measure the orientation of its local frame. The general concept of this method is to derive the gyroscopic angles for both devices during an alignment movement, and then to use an optimization algorithm to calculate the alignment matrix between both local frames. The alignment movement consists of rotations around three more or less orthogonal axes and it can easily be performed by hands. To test the alignment procedure, an IMU and a magnetic marker were attached to a plate, and 20 alignment movements were recorded. The maximum errors of alignment (accuracy±precision) were 1.02°±0.32° and simulations showed that the method was robust against noise that typically affect IMUs. In conclusion, this study describes an efficient alignment procedure that is quick and easy to perform, and that does not require any alignment device or any knowledge about the IMU casing.     

Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables

Spatiotemporal characteristics of gait such as step time and length are often associated with overall physical function in clinical populations, but can be difficult, time consuming and obtrusive to measure. This study assessed the concurrent validity of overground walking spatiotemporal data recorded using a criterion reference – a marker-based three-dimensional motion analysis (3DMA) system – and a low-cost, markerless alternative, the automated skeleton tracking output from the Microsoft Kinect™ (Kinect). Twenty-one healthy adults performed normal walking trials while being monitored using both systems. The outcome measures of gait speed, step length and time, stride length and time and peak foot swing velocity were derived using supervised automated analysis. To assess the agreement between the Kinect and 3DMA devices, Bland–Altman 95% bias and limits of agreement, percentage error, relative agreement (Pearson's correlation coefficients: r) overall agreement (concordance correlation coefficients: r_c) and landmark location linearity as a function of distance from the sensor were determined. Gait speed, step length and stride length from the two devices possessed excellent agreement (r and r_c values >0.90). Foot swing velocity possessed excellent relative (r=0.93) but only modest overall (r_c=0.54) agreement. Step time (r=0.82 and r_c=0.23) and stride time (r=0.69 and r_c=0.14) possessed excellent and modest relative agreement respectively but poor overall agreement. Landmark location linearity was excellent (R²=0.991). This widely available, low-cost and portable system could provide clinicians with significant advantages for assessing some spatiotemporal gait parameters. However, caution must be taken when choosing outcome variables as some commonly reported variables cannot be accurately measured.