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

Comparative assessment of heel rise detection for consistent gait phase separation

Background

Accurate identification of gait events is crucial to reliable gait analysis. Heel rise, a key event marking the transition from mid-stance to terminal stance, poses challenges in precise detection due to its gradual nature. This leads to variability in accuracy across studies utilizing diverse measuring techniques.

Research question

How do different HR detection methods compare when assessed against the underlying heel motion pattern and visual detection across varying speed, footwear conditions, and individuals?

Methods

Leveraging data from over 10,000 strides in diverse scenarios with 15 healthy subjects, we evaluated methods based on measurements from optical motion capture (OMC), force plates, and shank-mounted inertial measurement units (IMUs). The evaluation of these methods included an assessment of their precision and consistency with the heel marker's motion pattern and agreement with visually detected heel rise.

Results

OMC-based heel rise detection methods, utilizing the heel marker's vertical acceleration and jerk, consistently identified the same point in the heel motion pattern, outperforming velocity-based methods and our new position-based method resembling traditional footswitch-based heel rise detection. Variability in velocity and position-based methods derives from subtle heel rise variations after mid-stance, exhibiting individual differences. Our proposed IMU-based methods show promise by closely matching OMC-based accuracy.

Significance

The results have significant implications for gait analysis, providing insights into heel rise event detection's complexities. Accurate HR identification is crucial for gait phase separation, and our findings, especially with the robust heel marker's jerk-based method, enhance precision and consistency across walking conditions. Moreover, our successful development and validation of IMU-based algorithm offer cost-effective and mobile alternative for HR detection, expanding their potential use in comprehensive gait analysis.

A novel method for accurate division of the gait cycle into seven phases using shank angular velocity

BACKGROUND

Accurate detection of gait events is crucial for gait analysis, enabling the assessment of gait patterns and abnormalities. Inertial measurement unit (IMU) sensors have gained traction for event detection, mainly focusing on initial contact (IC) and toe-off (TO) events. However, effective detection of other key events such as heel rise (HR), feet adjacent (FA), and tibia vertical (TBV) is essential for comprehensive gait analysis.

RESEARCH QUESTION

Can a novel IMU-based method accurately detect HR, TO, FA, and TBV events, and how does its performance compare with existing methods?

METHODS

We developed and validated an IMU-based method using cumulative mediolateral shank angular velocity (CSAV) for event detection. A dataset of nearly 25,000 gait cycles from healthy adults walking at varying speeds and footwear conditions was used for validation. The method's accuracy was assessed against force plate and motion capture data and compared with existing TO detection methods.

RESULTS

The CSAV method demonstrated high accuracy in detecting TO, FA, and TBV events and moderate accuracy in HR event detection. Comparisons with existing TO detection methods showcased superior performance. The method's stability across speed and shoe variations underscored its robustness.

SIGNIFICANCE

This study introduces a highly accurate IMU-based method for detecting gait events needed to divide the gait cycle into seven phases. The effectiveness of the CSAV method in capturing essential events across different scenarios emphasizes its potential applications. Although HR event detection can be further improved, the precision of the CSAV method in TO, FA, and TBV detection advance the field. This study bridges a critical gap in IMU-based gait event detection by introducing a method for subdividing the swing phase into its subphases. Further research can focus on refining HR detection and expanding the method's utility across diverse gait contexts, thereby enhancing its clinical and scientific significance.

A Novel Gait Event Detection Algorithm Using a Thigh-Worn Inertial Measurement Unit and Joint Angle Information

This paper describes the development and evaluation of a novel, threshold-based gait event detection algorithm utilizing only one thigh inertial measurement unit (IMU) and unilateral, sagittal plane hip and knee joint angles. The algorithm was designed to detect heel strike (HS) and toe off (TO) gait events, with the eventual goal of detection in a real-time exoskeletal control system. The data used in the development and evaluation of the algorithm were obtained from two gait databases, each containing synchronized IMU and ground reaction force (GRF) data. All database subjects were healthy individuals walking in either a level-ground, urban environment or a treadmill lab environment. Inertial measurements used were three-dimensional thigh accelerations and three-dimensional thigh angular velocities. Parameters for the TO algorithm were identified on a per-subject basis. The GRF data were utilized to validate the algorithm's timing accuracy and quantify the fidelity of the algorithm, measured by the F1-Score. Across all participants, the algorithm reported a mean timing error of -41±20 ms with an F1-Score of 0.988 for HS. For TO, the algorithm reported a mean timing error of -1.4±21 ms with an F1-Score of 0.991. The results of this evaluation suggest that this algorithm is a promising solution to inertial based gait event detection; however, further refinement and real-time evaluation are required for use in exoskeletal control.

Comparison of prosthetic mobility and balance in transfemoral amputees with bone-anchored prosthesis vs. socket prosthesis

BACKGROUND

The literature comparing bone-anchored prosthesis (BAP) with socket prosthesis (SP) consistently reports improvement in physical health and quality of life using primarily patient-reported outcome measures (PROMs).

OBJECTIVE

To determine the differences in mobility and balance using performance-based outcome measures and PROMs in people with transfemoral amputations (TFAs) fitted with BAP vs. SP.

STUDY DESIGN

Causal comparative.

METHODS

Two groups of people with TFAs were recruited: one using a BAP (N = 11; mean age ± standard deviation, 44 ± 14.9 years; mean residual limb length as a percentage of the intact femur, 68% ± 15.9) and another group using a SP (N = 11; mean age ± standard deviation, 49.6 ± 16.0 years; mean residual limb length as a percentage of the intact femur, 81% ± 13.9), and completed the 10-meter walk test, component timed-up-and-go, Prosthetic Limb Users Survey of Mobility™ 12-item, and Activities-specific Balance Confidence Scale.

RESULTS

There were no statistically significant differences between the BAP and SP groups in temporal spatial gait parameters and prosthetic mobility as measured by the 10-meter walk test and component timed-up-and-go, yet large effect sizes were found for several variables. In addition, Activities-specific Balance Confidence Scale and Prosthetic Limb Users Survey of Mobility™ scores were not statistically different between the BAP and SP groups, yet a large effect sizes were found for both variables.

CONCLUSIONS

This study found that people with TFA who use a BAP can demonstrate similar temporal spatial gait parameters and prosthetic mobility, as well as self-perceived balance confidence and prosthetic mobility as SP users. Therefore, suggesting that the osseointegration reconstruction surgical procedure provides an alternative option for a specific population with TFA who cannot wear nor have limitations with a SP. Future research with a larger sample and other performance-based outcome measures and PROMs of prosthetic mobility and balance would further determine the differences between the prosthetic options.

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait

Inertial measurement unit (IMU) sensors are widely used for motion analysis in sports and rehabilitation. The attachment of IMU sensors to predefined body segments and sides (left/right) is complex, time-consuming, and error-prone. Methods for solving the IMU-2-segment (I2S) pairing work properly only for a limited range of gait speeds or require a similar sensor configuration. Our goal was to propose an algorithm that works over a wide range of gait speeds with different sensor configurations while being robust to footwear type and generalizable to pathologic gait patterns. Eight IMU sensors were attached to both feet, shanks, thighs, sacrum, and trunk, and 12 healthy subjects (training dataset) and 22 patients (test dataset) with medial compartment knee osteoarthritis walked at different speeds with/without insole. First, the mean stride time was estimated and IMU signals were scaled. Using a decision tree, the body segment was recognized, followed by the side of the lower limb sensor. The accuracy and precision of the whole algorithm were 99.7% and 99.0%, respectively, for gait speeds ranging from 0.5 to 2.2 m/s. In conclusion, the proposed algorithm was robust to gait speed and footwear type and can be widely used for different sensor configurations.

Estimation of Gait Parameters for Transfemoral Amputees Using Lower Limb Kinematics and Deterministic Algorithms

Accurate estimation of gait parameters depends on the prediction of key gait events of heel strike (HS) and toe-off (TO). Kinematics-based gait event estimation has shown potential in this regard, particularly using leg and foot velocity signals and gyroscopic sensors. However, existing algorithms demonstrate a varying degree of accuracy for different populations. Moreover, the literature lacks evidence for their validity for the amputee population. The purpose of this study is to evaluate this paradigm to predict TO and HS instants and to propose a new algorithm for gait parameter estimation for the amputee population. An open data set containing marker data of 12 subjects with unilateral transfemoral amputation during treadmill walking was used, containing around 3400 gait cycles. Five deterministic algorithms detecting the landmarks (maxima, minima, and zero-crossings [ZC]) in the foot, shank, and thigh angular velocity data indicating HS and TO events were implemented and their results compared against the reference data. Two algorithms based on foot and shank velocity minima performed exceptionally well for the HS prediction, with median accuracy in the range of 6–13 ms. However, both these algorithms produced inferior accuracy for the TO event with consistent early prediction. The peak in the thigh velocity produced the best result for the TO prediction with <25 ms median error. By combining the HS prediction using shank velocity and TO prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters (step, stride times, stance, and double support timings) with a median error of less than 25 ms. In conclusion, combined shank and thigh velocity-based prediction leads to improved gait parameter estimation than traditional algorithms for the amputee population.

A database of physical therapy exercises with variability of execution collected by wearable sensors

This document introduces the PHYTMO database, which contains data from physical therapies recorded with inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. PHYTMO includes magneto-inertial data, together with a highly accurate location and orientation in the 3D space provided by the optical system. The files were stored in CSV format to ensure its usability. The aim of this dataset is the availability of data for two main purposes: the analysis of techniques for the identification and evaluation of exercises using inertial sensors and the validation of inertial sensor-based algorithms for human motion monitoring. Furthermore, the database stores enough data to apply Machine Learning-based algorithms. The participants' age range is large enough to establish age-based metrics for the exercises evaluation or the study of differences in motions between different groups.

Estimation of gait parameters using leg velocity for amputee population

Quantification of key gait parameters plays an important role in assessing gait deficits in clinical research. Gait parameter estimation using lower-limb kinematics (mainly leg velocity data) has shown promise but lacks validation for the amputee population. The aim of this study is to assess the accuracy of lower-leg angular velocity to predict key gait events (toe-off and heel strike) and associated temporal parameters for the amputee population. An open data set of reflexive markers during treadmill walking from 10 subjects with unilateral transfemoral amputation was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating toe-off and heel strike events. Four temporal gait parameters were also estimated (step time, stride time, stance and swing duration). These predictions were compared against the force platform data for 3000 walking cycles from 239 walking trials. Considerable accuracy was achieved for the HS event as well as for step and stride timings, with mean errors ranging from 0 to -13ms. The TO prediction exhibited a larger error with its mean ranging from 35-81ms. The algorithm consistently predicted the TO earlier than the actual event, resulting in prediction errors in stance and swing timings. Significant differences were found between the prediction for sound and prosthetic legs, with better TO accuracy on the prosthetic side. The prediction accuracy also appeared to improve with the subjects’ mobility level (K-level). In conclusion, the leg velocity profile, coupled with the dual-minima algorithm, can predict temporal parameters for the transfemoral amputee population with varying degrees of accuracy.

Validity of Dual-Minima Algorithm for Heel-Strike and Toe-Off Prediction for the Amputee Population

Assessment of gait deficits relies on accurate gait segmentation based on the key gait events of heel strike (HS) and toe-off (TO). Kinematics-based estimation of gait events has shown promise in this regard, especially using the leg velocity signal and gyroscopic sensors. However, its validation for the amputee population is not established in the literature. The goal of this study is to assess the accuracy of lower-leg angular velocity signal in determining the TO and HS instants for the amputee population. An open data set containing marker data of 10 subjects with unilateral transfemoral amputation during treadmill walking was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating TO and HS events. The predictions were compared against the force platform data for 2595 walking cycles from 239 walking trials. The results showed considerable accuracy for the HS with a median error of −1 ms. The TO prediction error was larger with the median ranging from 35–84 ms. The algorithm consistently predicted the TO earlier than the actual event. Significant differences were found between the prediction accuracy for the sound and prosthetic legs. The prediction accuracy was also affected by the subjects’ mobility level (K-level) but was largely unaffected by gait speed. In conclusion, the leg velocity profile during walking can predict the heel-strike and toe-off events for the transfemoral amputee population with varying degrees of accuracy depending upon the leg side and the amputee’s functional ability level.

Effect of Torso Kinematics on Gait Phase Estimation at Different Walking Speeds

Human gait phase estimation has been studied in the field of robotics due to its importance for controlling wearable devices (e.g., prostheses or exoskeletons) in a synchronized manner with the user. As data-driven approaches have recently risen in the field, researchers have attempted to estimate the user gait phase using a learning-based method. Thigh and torso information have been widely utilized in estimating the human gait phase for wearable devices. Torso information, however, is known to have high variability, specifically in slow walking, and its effect on gait phase estimation has not been studied. In this study, we quantified torso variability and investigated how the torso information affects the gait phase estimation result at various walking speeds. We obtained three different trained models (i.e., general, slow, and normal-fast models) using long short-term memory (LSTM). These models were compared to identify the effect of torso information at different walking speeds. In addition, the ablation study was performed to identify the isolated effect of the torso on the gait phase estimation. As a result, when the torso segment's angular velocity was used with thigh information, the accuracy of gait phase estimation was increased, while the torso segment's angular position had no apparent effect on the accuracy. This study suggests that the torso segment's angular velocity enhances human gait phase estimation when used together with the thigh information despite its known variability.

Foot angular kinematics measured with inertial measurement units: A reliable criterion for real-time gait event detection

Accurate and reliable real-time detection of gait events using inertial measurement units (IMUs) is crucial for (1) developing clinically meaningful gait parameters to differentiate normal and impaired gait or (2) creating patient-tailored gait rehabilitation strategies or control of prosthetic devices using feedback from gait phases. However, most previous studies focused only on algorithms with high temporal accuracy and neglected the importance of (1) high reliability, i.e., detecting only and all true gait events, and (2) real-time implementation. Thus, in this study, we presented a novel approach for initial contact (IC) and terminal contact (TC) detection in real-time based on the measurement of the foot orientation. Unlike foot/shank angular velocity and acceleration, foot orientation provides physiologically meaningful kinematic features corresponding to our observational recognition of IC and TC, regardless of the walking modality. We conducted an experimental study to validate our algorithm, including seven participants performing four walking/running activities. By analyzing 5,555 ICs/TCs recorded during the tests, only our algorithm achieved a sensitivity and precision of 100%. Our obtained temporal accuracy (mean ± standard deviation of errors ranging from 0 ± 3 to 6 ± 5 time samples; sampling frequency: 100 Hz) was better than or comparable to those reported in the literature. Our algorithm's performance does not depend on thresholds and gait speed/modality, and it can be used for feedback-based therapeutic gait training or real-time control of assistive or prosthetic technologies. Nevertheless, its performance for pathological gait must be validated in the future. Finally, we shared the codes and sample data on https://www.ncbl.ualberta.ca/codes.

Evaluating the Impact of IMU Sensor Location and Walking Task on Accuracy of Gait Event Detection Algorithms

There are several algorithms that use the 3D acceleration and/or rotational velocity vectors from IMU sensors to identify gait events (i.e., toe-off and heel-strike). However, a clear understanding of how sensor location and the type of walking task effect the accuracy of gait event detection algorithms is lacking. To address this knowledge gap, seven participants were recruited (4M/3F; 26.0 ± 4.0 y/o) to complete a straight walking task and obstacle navigation task while data were collected from IMUs placed on the foot and shin. Five different commonly used algorithms to identify the toe-off and heel-strike gait events were applied to each sensor location on a given participant. Gait metrics were calculated for each sensor/algorithm combination using IMUs and a reference pressure sensing walkway. Results show algorithms using medial-lateral rotational velocity and anterior-posterior acceleration are fairly robust against different sensor locations and walking tasks. Certain algorithms applied to heel and lower lateral shank sensor locations will result in degraded algorithm performance when calculating gait metrics for curved walking compared to straight overground walking. Understanding how certain types of algorithms perform for given sensor locations and tasks can inform robust clinical protocol development using wearable technology to characterize gait in both laboratory and real-world settings.

Unrestricted stride detection during stair climbing using IMUs

Stride detection, or the identification of the initial (IC) and terminal contact (TC) of the feet while walking, is important for gait analysis. Automatic stride detection based only on kinematic data is challenging, even when using portable, low-cost, user-friendly Inertial Measurement Units (IMUs). Although there are algorithms for straight walking available, they are often not applicable to other movement patterns. Furthermore, these algorithms are based on the use of different IMUs placed on different locations of the body with different pre-processing filters and rely on analyzing different measurement signals. Therefore, it is difficult to apply existing algorithms for specific study settings. To achieve a new algorithm, thirty-five healthy participants were analyzed during walking and stair climbing while kinematic motion data was measured using the IMU system MyoMotion. Based on the analysis of different published methods for IC and TC detection, a new robust stride detection algorithm was developed and validated in comparison with two different algorithms. From this, it was determined that the newly developed algorithm was successful in automatic stride detection during walking and ascending/ descending stairs with 100% detected gait events, while the other algorithms failed during stair climbing with only 44% and 91% detected gait events.

Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

Objective

To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.

Methods

A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.

Results

In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.

Conclusion

The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

Identification of Gait Events in Healthy Subjects and With Parkinson's Disease Using Inertial Sensors: An Adaptive Unsupervised Learning Approach

Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson's disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ( F₁ -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages.

A novel gait parameter estimation method for healthy adults and postoperative patients with an ear-worn sensor

OBJECTIVE

Gait analysis helps to assess recovery during rehabilitation. Previous gait analysis studies are primarily applicable to healthy subjects or to postoperative patients. The purpose of this paper is to construct a new gait parameter estimation platform based on an ear-worn activity recognition (e-AR) sensor, which can be used for both normal and pathological gait signals.

APPROACH

Thirty healthy adults and eight postoperative patients participated in the experiment. A method based on singular spectrum analysis (SSA) and iterative mean filtering (IMF) is proposed to detect gait events and estimate three key gait parameters, i.e. stride time, swing time, and stance time.

MAIN RESULTS

Experimental results show that the estimated gait parameters provided by the proposed method are very close to the gait parameters provided by the gait assessment system. For normal gait signals, the average absolute errors of stride, swing, and stance time are 27.8 ms, 35.8 ms, and 37.5 ms, respectively. For pathological gait signals, the average absolute error of stride time is 32.1 ms.

SIGNIFICANCE

The proposed parameter estimation method can be applied to both general analysis for healthy subjects and rehabilitation evaluation for postoperative patients. The convenience and comfort of the ear-worn sensor increase its potential for practical applications.

Validity and reliability of wearable inertial sensors in healthy adult walking: a systematic review and meta-analysis

BACKGROUND

Inertial measurement units (IMUs) offer the ability to measure walking gait through a variety of biomechanical outcomes (e.g., spatiotemporal, kinematics, other). Although many studies have assessed their validity and reliability, there remains no quantitive summary of this vast body of literature. Therefore, we aimed to conduct a systematic review and meta-analysis to determine the i) concurrent validity and ii) test-retest reliability of IMUs for measuring biomechanical gait outcomes during level walking in healthy adults.

METHODS

Five electronic databases were searched for journal articles assessing the validity or reliability of IMUs during healthy adult walking. Two reviewers screened titles, abstracts, and full texts for studies to be included, before two reviewers examined the methodological quality of all included studies. When sufficient data were present for a given biomechanical outcome, data were meta-analyzed on Pearson correlation coefficients (r) or intraclass correlation coefficients (ICC) for validity and reliability, respectively. Alternatively, qualitative summaries of outcomes were conducted on those that could not be meta-analyzed.

RESULTS

A total of 82 articles, assessing the validity or reliability of over 100 outcomes, were included in this review. Seventeen biomechanical outcomes, primarily spatiotemporal parameters, were meta-analyzed. The validity and reliability of step and stride times were found to be excellent. Similarly, the validity and reliability of step and stride length, as well as swing and stance time, were found to be good to excellent. Alternatively, spatiotemporal parameter variability and symmetry displayed poor to moderate validity and reliability. IMUs were also found to display moderate reliability for the assessment of local dynamic stability during walking. The remaining biomechanical outcomes were qualitatively summarized to provide a variety of recommendations for future IMU research.

CONCLUSIONS

The findings of this review demonstrate the excellent validity and reliability of IMUs for mean spatiotemporal parameters during walking, but caution the use of spatiotemporal variability and symmetry metrics without strict protocol. Further, this work tentatively supports the use of IMUs for joint angle measurement and other biomechanical outcomes such as stability, regularity, and segmental accelerations. Unfortunately, the strength of these recommendations are limited based on the lack of high-quality studies for each outcome, with underpowered and/or unjustified sample sizes (sample size median 12; range: 2-95) being the primary limitation.

The Effectiveness of the DoD/VA Mobile Device Outcomes-Based Rehabilitation Program for High Functioning Service Members and Veterans with Lower Limb Amputation

Introduction

The objective was to determine if the Mobile Device Outcomes-based Rehabilitation Program (MDORP) improved strength, mobility, and gait quality in service members (SMs) and Veterans with lower limb amputation (LLA).

Methods

Seven SMs and 10 Veterans with LLA enrolled and were trained to use a mobile sensor system, called Rehabilitative Lower Limb Orthopedic Analysis Device (ReLOAD). ReLOAD provided participants with real-time assessment of gait deviations, subsequent corrective audio feedback, and exercise prescription for normalizing gait at home and in the community. After baseline testing, prosthetic gait and exercise training, participants took ReLOAD home and completed an 8-week walking and home exercise program. Home visits were conducted every 2 weeks to review gait training and home exercises.

Results

Significant improvements in hip extensor strength, basic and high-level mobility, musculoskeletal endurance, and gait quality (P &lt; 0.05) were found at the completion of the 8-week intervention.

Conclusion

Preliminary MDORP results are promising in its ability to improve basic and high-level mobility, lower limb strength, and gait quality in a group of SMs and Veterans with LLA. In addition, “booster” prosthetic training may be justified in an effort helps maintain an active lifestyle, promotes prosthetic use, and mitigates secondary health effects.

Inertial sensor-based measures of gait symmetry and repeatability in people with unilateral lower limb amputation

BACKGROUND

People with lower limb amputation often walk with asymmetrical gait patterns potentially leading to long-term health problems, ultimately affecting their quality of life. The ability to discreetly detect and quantify the movement of bilateral thighs and shanks using wearable sensor technology can provide additional insight into how a person walks with a lower limb prosthesis. This study investigated segmental symmetry and segmental repeatability of people with unilateral lower limb amputation, examining performance of the prosthetic and intact limbs.

METHODS

Gyroscope signals were recorded from four inertial measurement units worn on bilateral lower limb segments of subjects with unilateral lower limb amputation during the 10-m walk test. Raw angular velocity signals were processed using dynamic time warping and application of algorithms resulting in symmetry measures comparing similarity of prosthetic to intact limb strides, and repeatability measures comparing movement of one limb to its consecutive strides.

FINDINGS

Biomechanical differences in performance of the prosthetic and intact limb segments were detected with the segmental symmetry and segmental repeatability measures in 128 subjects. More asymmetries and less consistent movements of the lower limbs were exhibited by subjects with transfemoral amputation versus transtibial amputation (p < .004, Cohen's d = 0.65-1.1).

INTERPRETATION

Sensor-based measures of segmental symmetry and segmental repeatability were found to be reliable in detecting discreet differences in movement of the prosthetic versus intact lower limbs in amputee subjects. These measures provide a convenient tool for enhanced prosthetic gait analysis with the potential to focus rehabilitative and prosthetic interventions.

Reliability and concurrent validity of spatiotemporal stride characteristics measured with an ankle-worn sensor among older individuals

BACKGROUND

Wearable inertial sensors have been shown to provide valid mean gait characteristics assessments, however, assessment of variability is less convincingly established.

RESEARCH QUESTION

What level of concurrent validity, and session-to-session reliability does an ankle-worn inertial measurement unit (IMU)-based gait assessment with a novel angular velocity-based gait event detection algorithm have among older adults?

METHODS

Twenty seven (women N = 17) participants volunteered (age 74.4 (SD 4.3) years, body mass 74.5 (12.0) kg, height 165.9 (9.9) cm). Right leg stance, swing, and stride duration and stride length, and stride velocity were concurrently assessed with motion capture and with an IMU from a 3 min self-paced walk up and back a 14 m track repeated twice a week apart. Gait variability was assessed as the SD of all of the registered strides.

RESULTS

Significant difference was observed between methods for many of the mean stride characteristics and stride variability (all p < 0.05), fair to excellent agreement was observed for mean values of all of the five stride characteristics evaluated (intra-class correlation coefficient [ICC] from 0.43 to 1.00). However, poor agreement was observed for the SD of all of the evaluated stride characteristics (ICC from -0.25 to 0.00). Both methods indicated excellent session to session reliability for all of the five stride characteristics evaluated (ICC from 0.84 to 0.98, CV%_RMS from 1.6% to 3.6%), whereas the variability characteristics exhibited poor to good reliability (ICC from 0.0 to 0.69, CV%_RMS from 18.0% to 34.4%).

SIGNIFICANCE

Excellent concurrent validity and reliability was observed for mean spatiotemporal stride characteristics, however, gait variability exhibited poor concurrent validity and reliability. Although IMUs and the presented algorithm could be used to assess mean spatiotemporal stride characteristics among older individuals, either a more reliable gait event detection algorithm or alternative analytical approaches should be used for gait variability.

Quantification of Agility Testing with Inertial Sensors after a Knee Injury

INTRODUCTION

A common criterion in decision making regarding return to sport (RTS) after knee ligament injury is that athletes should achieve symmetrical bilateral movement between the injured limb and the noninjured limb. Body-worn wireless inertial measurement units (IMU) can provide clinicians with valuable information about lower-limb kinematics and athletic performance.

METHODS

The IMU-based novel kinematic metrics were developed. The Transitional Angular Displacement of Segment (TADS) and Symmetry Index (SI) measures that quantify lower-limb motions and interlimb symmetry during the 4-m side step test (FmSST) were developed. Test-retest reliability was measured in 20 healthy adults. Experimental application of the metrics was also determined in 15 National Collegiate Athletic Association Division I collegiate athletes who completed rehabilitation after a knee ligament injury.

RESULTS

The intraclass correlation coefficient for test-retest reliability for FmSST, TADS right lower limb, TADS left lower limb, and TADS SI was 0.90 (95% confidence interval, [0.61-0.95]); 0.87 [0.63-0.96]; 0.89 [0.64-0.96], and 0.81 [0.58-0.92], respectively. The differences between TADS SI at baseline (preinjury) and RTS were also compared with those between the total times for performing the FmSST at baseline and RTS. There was no significant difference in the FmSST times between baseline and RTS (P = 0.32); however, TADS SI at the time of RTS was significantly lower than at baseline (P = 0.046). A large effect size (d = -1.04) was observed for the change in TADS SI from baseline to RTS.

CONCLUSIONS

Using IMU sensor technology can provide quantitative and discrete analysis to detect kinematic differences during agility after a knee ligament injury in the field or nonlaboratory setting. This approach has the potential to help clinicians improve decisions about rehabilitation at a time when an athlete is reintegrating back into sport.

Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients

Hemiparesis is one of the common sequelae of neurological diseases such as strokes, which can significantly change the gait behavior of patients and restrict their activities in daily life. The results of gait characteristic analysis can provide a reference for disease diagnosis and rehabilitation; however, gait correlation as a gait characteristic is less utilized currently. In this study, a new non-contact electrostatic field sensing method was used to obtain the electrostatic gait signals of hemiplegic patients and healthy control subjects, and an improved Detrended Cross-Correlation Analysis cross-correlation coefficient method was proposed to analyze the obtained electrostatic gait signals. The results show that the improved method can better obtain the dynamic changes of the scaling index under the multi-scale structure, which makes up for the shortcomings of the traditional Detrended Cross-Correlation Analysis cross-correlation coefficient method when calculating the electrostatic gait signal of the same kind of subjects, such as random and incomplete similarity in the trend of the scaling index spectrum change. At the same time, it can effectively quantify the correlation of electrostatic gait signals in subjects. The proposed method has the potential to be a powerful tool for extracting the gait correlation features and identifying the electrostatic gait of hemiplegic patients.

Reliability and practical clinical application of an accelerometer-based dual-task gait balance control assessment

BACKGROUND

Gait balance control assessment using whole body center of mass (COM) kinematic measures in concussed individuals reveals persistent balance deficits up to two months post-injury. A reliable and clinically practical gait balance control assessment leveraging similar kinematic measures is necessary to improve concussion assessment and management.

RESEARCH QUESTION

Can peak accelerations collected during a dual-task (DT) gait assessment from a single low back placed accelerometer be measured reliably on different days, by different raters, in different environments, and be practically applied in a Division One (D1) athletics program?

METHODS

A single accelerometer placed on the low back over the L5 vertebra was utilized with a DT gait analysis protocol. Twenty (10 F) healthy participants performed the assessment in a laboratory and non-laboratory environment, on two separate days, and with two different raters. Eight gait event specific peak accelerations along three orthogonal axes were collected. In addition, data were collected from a cohort of 14 D1 female soccer players during a single assessment to explore the practical clinical application.

RESULTS

Cronbach's α values for the eight metrics ranged from 0.881 to 0.980 and ICC values from 0.868 to 0.987. Average assessment time for the 14 D1 female athletes was 8.50 ± 0.58 min, and significant differences between walking conditions were identified for Vert Accel 1 (p < .01), Vert Accel 2 (p = .01), and A-P Accel (p < .01).

SIGNIFICANCE

High Cronbach's α and ICC values coupled with a short assessment time and sensitivity to differences in gait balance control indicate our testing apparatus and protocol are both reliable and clinically practical. Additionally, gait event specific peak accelerations from a single accelerometer can detect subtle changes in gait balance control and may facilitate improvements in sport-related concussion diagnosis and return to activity decision making.

Using Inertial Sensors to Quantify Postural Sway and Gait Performance during the Tandem Walking Test

Vestibular dysfunction typically manifests as postural instability and gait irregularities, in part due to inaccuracies in processing spatial afference. In this study, we have instrumented the tandem walking test with multiple inertial sensors to easily and precisely investigate novel variables that can distinguish abnormal postural and gait control in patients with unilateral vestibular hypofunction. Ten healthy adults and five patients with unilateral vestibular hypofunction were assessed with the tandem walking test during eyes open and eyes closed conditions. Each subject donned five inertial sensors on the upper body (head, trunk, and pelvis) and lower body (each lateral malleolus). Our results indicate that measuring the degree of balance and gait regularity using five body-worn inertial sensors during the tandem walking test provides a novel quantification of movement that identifies abnormalities in patients with vestibular impairment.

Feasibility of a mobile feedback system for gait retraining in people with lower limb loss-A technical note

Gait retraining in people with musculoskeletal and/or neurological impairments requires sustained dedicated efforts by the patient and the rehabilitation therapist. Various technical approaches have been proposed and utilized to improve the effectiveness of training interventions. Among the most promising approaches is the provision of real-time feedback information to the patient, which has been used with success on treadmill-based interventions in the past. We are describing a mobile visual feedback system that is intended to work in the user's everyday-life environment. The data are captured by a small mobile load cell, processed in a wearable computer, and displayed to the user via smart-glasses. Preliminary testing of the initially selected feedback variable stance/step ratio (i.e., the duration of a step's stance phase in relation to the overall step's duration) confirmed that data quality is sufficient for purposes of generating feedback information and that the chosen variable is responsive to changes in gait symmetry. The presented work may inform future studies and developments on the topic of mobile visual feedback for gait rehabilitation.

Exposure to an extreme environment comes at a sensorimotor cost

Long duration space flight is known to induce severe modifications in the sensorimotor and musculoskeletal systems. While in-flight strategies including physical fitness have been used to prevent the loss of bone and muscle mass using appropriate rehabilitative countermeasures, less attention has been put forth in the design of technologies that can quickly and effectively assess sensorimotor function during missions in space. The aims of the present study were therefore (1) to develop a Portable Sensorimotor Assessment Platform (PSAP) to enable a crewmember to independently and quickly assess his/her sensorimotor function during the NASA's Extreme Environment Mission Operations (NEEMO) and (2) to investigate changes in performance of static posture, tandem gait, and lower limb ataxia due to exposure in an extreme environment. Our data reveal that measuring the degree of upper body balance and gait regularity during tandem walking using PSAP provided a sensitive and objective quantification of body movement abnormalities due to changes in sensorimotor performance over the duration of mission exposure.

A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices

Tele-rehabilitation of patients with gait abnormalities could benefit from continuous monitoring of knee joint angle in the home and community. Continuous monitoring with mobile devices can be restricted by the number of body-worn sensors, signal bandwidth, and the complexity of operating algorithms. Therefore, this paper proposes a novel algorithm for estimating knee joint angle using lower limb angular velocity, obtained with only two leg-mounted gyroscopes. This gyroscope only (GO) algorithm calculates knee angle by integrating gyroscope-derived knee angular velocity signal, and thus avoids reliance on noisy accelerometer data. To eliminate drift in gyroscope data, a zero-angle update derived from a characteristic point in the knee angular velocity is applied to every stride. The concurrent validity and construct convergent validity of the GO algorithm was determined with two existing IMU-based algorithms, complementary and Kalman filters, and an optical motion capture system, respectively. Bland⁻Altman analysis indicated a high-level of agreement between the GO algorithm and other measures of knee angle.

Measurement of lower limb segmental excursion using inertial sensors during single limb stance

Advances in wearable technology have afforded health scientists and clinicians the ability to quantify clinically meaningful kinematic data with performance-based outcome measures in a variety of environments. However, no method for assessing segmental excursion of the lower limb during single limb stance (SLS) with wearable technology has been described in the literature nor has its clinical meaning been explored. This study introduces a clinically friendly measure to quantify lower limb segmental excursion during SLS with inertial measurement units (IMUs) which called the region of limb stability (ROLS). The purpose of this study was to determine the concurrent validity of an IMU-based system versus an optical motion capture system and to determine the effects of knee injury on the ROLS value. Excursion areas of five healthy adults were calculated with the IMU-based system and data were compared with an optical motion capture system. There were high correlations (0.82-0.93) and no significant difference (p > 0.05) in the tested parameters between the optical- and IMU-based systems. The IMU-based method was also implemented in five Division I athletes with knee injuries to determine changes in ROLS due to the injury. The ROLS Symmetry Index value offered a higher sensitivity and specificity to assess the presence of knee impairment than the sacral IMU. Quantified lower limb segmental excursion via IMUs can make better and more precise return-to-sport decisions that would decrease the risk of re-injury.

A practical step length algorithm using lower limb angular velocities

The use of Inertial Measurement Units (IMUs) for spatial gait analysis has opened the door to unconstrained measurements within the home and community. Bandwidth, cost limitations, and ease of use has historically restricted the number and location of sensors worn on the body. In this paper, we describe a four-sensor configuration of IMUs placed on the shanks and thighs that is sufficient to provide an accurate measure of temporal gait parameters, spatial gait parameters, and joint angle dynamics during ambulation. Estimating spatial gait parameters solely from gyroscope data is preferred because gyroscopes are less susceptible to sensor noise and a system comprised of only gyroscopes uses decreased bandwidth compared to a typical 9 degree-of-freedom IMU. The purpose of this study was to determine the validity of a novel method of step length estimation using gyroscopes attached to the shanks and thighs. An Inverted Pendulum Model algorithm (IPM) was proposed to calculate step length, stride length, and gait speed. The algorithm incorporates heel-strike events and average forward velocity per step to make these assessments. IMU algorithm accuracy was determined via concurrent validity with an instrumented walkway and results explained via the collision model of gait. The IPM produced accurate estimates of step length, stride length, and gait speed with a mean difference of 3 cm and an RMSE of 6.6 cm for step length, thus establishing a new approach for spatial gait parameter calculation. The lack of numerical integration in IPM makes it well suited for use in continuous monitoring applications where sensor sampling rates are restricted.

Development of an Inertial Motion Capture System for Clinical Application

The ability to capture human motion based on wearable sensors has a wide range of applications, e.g., in healthcare, sports, well-being, and workflow analysis. This article focuses on the development of an online-capable system for accurately capturing joint kinematics based on inertial measurement units (IMUs) and its clinical application, with a focus on locomotion analysis for rehabilitation. The article approaches the topic from the technology and application perspectives and fuses both points of view. It presents, in a self-contained way, previous results from three studies as well as new results concerning the technological development of the system. It also correlates these with new results from qualitative expert interviews with medical practitioners and movement scientists. The interviews were conducted for the purpose of identifying relevant application scenarios and requirements for the technology used. As a result, the potentials of the system for the different identified application scenarios are discussed and necessary next steps are deduced from this analysis.