Automated estimation of initial and terminal contact timing using accelerometers; development and validation in transtibial amputees and controls

Validation of Step Detection and Distance Calculation Algorithms for Soccer Performance Monitoring

This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created. Experiments were conducted on a specially designed soccer-specific testing circuit performed by 15 athletes, simulating a range of locomotion activities such as walking, jogging, and high-intensity actions. The algorithm outcome was compared with manually tagged data from a high-quality video camera-based system for validation, by assessing the agreement between the paired values using limits of agreement, concordance correlation coefficient, and further metrics. Results returned a step detection accuracy of 95.8% and a distance estimation Root Mean Square Error (RMSE) of 17.6 m over about 202 m of track. A sub-sample (N = 6) also wore two pairs of devices concurrently to evaluate inter-unit reliability. The performance analysis suggested that the algorithm was effective and reliable in tracking diverse soccer-specific movements. The proposed algorithm offered a robust and efficient solution for tracking step count and distance covered in soccer, particularly beneficial in indoor environments where global navigation satellite systems are not feasible. This advancement in sports technology widens the spectrum of tools for coaches and athletes in monitoring soccer performance.

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.

Longitudinal changes in trunk acceleration and their relationship with gait parameters in post-stroke hemiplegic patients

OBJECTIVE

The purpose of this study was to examine the longitudinal changes in trunk acceleration, gait speed, and paretic leg motion in patients with post-stroke hemiparesis, the relationships between variables at each time point, and whether initial trunk acceleration and gait parameters were related to gait speed 2 months later.

METHODS

Gait was assessed monthly in patients who could walk under supervision after stroke onset. Gait parameters, including gait speed and trailing limb angle (TLA), were measured. Trunk acceleration was quantified using acceleration root mean square (RMS) and stride regularity (SR) indices.

RESULTS

This study found statistically significant longitudinal changes in gait speed (p < .001), acceleration RMS of the total axes (p < .001), and SR of the vertical axes (p < .001). Gait speed correlated significantly with the acceleration RMS of the mediolateral (r = -0.815 to -0.901), vertical (r = -0.541 to -0.747), and anteroposterior (r = -0.718 to -0.829) axes, as well as the SR of the vertical axes (r = 0.558 to 0.724) at all time points from T0 to T2. For the TLA, only the acceleration RMS of the mediolateral axis correlated significantly over the entire study period (r = -0.530 to -0.724). In addition, initial TLA correlated significantly with gait speed after 2 months (r = -0.572).

CONCLUSION

This study showed that assessing trunk acceleration helps estimate the improvement in gait status in patients with post-stroke hemiparesis. The magnitude and regularity of trunk acceleration varied longitudinally and were related to gait speed and paretic leg motion at each time point; however, they could not predict future changes in gait speed.

WARNING: A Wearable Inertial-Based Sensor Integrated with a Support Vector Machine Algorithm for the Identification of Faults during Race Walking

Due to subjectivity in refereeing, the results of race walking are often questioned. To overcome this limitation, artificial-intelligence-based technologies have demonstrated their potential. The paper aims at presenting WARNING, an inertial-based wearable sensor integrated with a support vector machine algorithm to automatically identify race-walking faults. Two WARNING sensors were used to gather the 3D linear acceleration related to the shanks of ten expert race-walkers. Participants were asked to perform a race circuit following three race-walking conditions: legal, illegal with loss-of-contact and illegal with knee-bent. Thirteen machine learning algorithms, belonging to the decision tree, support vector machine and k-nearest neighbor categories, were evaluated. An inter-athlete training procedure was applied. Algorithm performance was evaluated in terms of overall accuracy, F1 score and G-index, as well as by computing the prediction speed. The quadratic support vector was confirmed to be the best-performing classifier, achieving an accuracy above 90% with a prediction speed of 29,000 observations/s when considering data from both shanks. A significant reduction of the performance was assessed when considering only one lower limb side. The outcomes allow us to affirm the potential of WARNING to be used as a referee assistant in race-walking competitions and during training sessions.

Comparison of EMG activity in shank muscles between individuals with and without chronic ankle instability when running on a treadmill

Changes in movement capabilities after an injury to the ankle may impose adaptations in the peripheral and central nervous system. The purpose of our study was to compare the electromyogram (EMG) profile of ankle stabilizer muscles and stride-time variation during treadmill running in individuals with and without chronic ankle instability (CAI). Recreationally active individuals with (n = 12) and without (n = 15) CAI ran on a treadmill at two speeds. EMG activity of four shank muscles as well as tibial acceleration data were recorded during the running trials. EMG amplitude, timing of EMG peaks, and variation in stride-time were analyzed from 30 consecutive stride cycles. EMG data were time-normalized to stride duration and amplitude was normalized relative to the appropriate maximal voluntary contraction (MVC) task. Individuals with CAI had similar EMG amplitudes and peak timing, but an altered order of peak EMG activity in ankle stabilizer muscles, a significantly greater EMG amplitude for PL with an increase in speed, and a greater stride-time variability during treadmill running compared with individuals who had no history of ankle sprains. The results of our study indicate that individuals with CAI exhibit altered activation strategies for ankle stabilizer muscles when running on a treadmill.

Comparison of EMG Activity in Leg Muscles between Overground and Treadmill Running

INTRODUCTION

Treadmills have been widely used for training and performance testing during which the treadmill grade is usually set to 0%-2% grade. The purpose of our study was to compare the level of activation of lower body muscles when running at two speeds in an overground condition and on a treadmill at 0%, 1%, and 2% grades.

METHODS

We recorded EMG data of eight lower body muscles from 13 recreationally active individuals during overground and treadmill running at 2.92 and 4.58 m·s -1 . Maximal voluntary contraction (MVC) tests were performed (3 × 6 s) to identify maximal torque and EMG values. The stride cycles, from one foot strike to the next, were identified using a pair of triaxial accelerometers. A two-way repeated-measures ANOVA was used to examine the differences in EMG activity across running conditions and speeds. Cohen's d effect size was calculated to indicate the difference between the overground and the treadmill running conditions.

RESULTS

The effect sizes were moderate to negligible for differences between the EMG integral values for overground running and the three treadmill grades. The coefficient of variation for stride time during overground running was significantly larger than that of the treadmill running at 4.58 m·s -1 .

CONCLUSIONS

The results showed that the overall EMG profiles of the thigh and shank muscles were similar for the overground and treadmill conditions, but the similarity was greatest for thigh muscles when running on the treadmill at 1% grade and for shank muscles at 2% grade. The variability in stride time was greater during overground running than when running on a treadmill and was associated with elevated EMG activity of some muscles.

Walking asymmetry and its relation to patient-reported and performance-based outcome measures in individuals with unilateral lower limb loss

Gait asymmetry persists for most people after lower limb amputation and is associated with slower walking speeds. However, the relationship between gait asymmetry and patient-reported function remains unclear because they are not commonly assessed together. The purpose of this study was to determine relationships between gait asymmetries in people with lower limb loss and (1) patient-reported outcomes and (2) performance-based prosthetic functional measures. This cross-sectional analysis included nine people with unilateral limb loss aged 48.2 ± 13.1 years of mixed amputation etiology. Patient-reported outcomes included the Prosthetic Evaluation Questionnaire mobility subscale and Activities-specific Balance Confidence scale. Performance outcomes included the Berg Balance Scale and the 30-second sit-to-stand test. Walking performance measures included the 2-Minute Walk Test, during which APDM Opal sensors recorded spatiotemporal gait parameters, and daily step-counts from StepWatch4 activity monitors. The study found that the most asymmetric gait symmetry ratios (prosthetic-limb divided by intact-limb) could be attributed to prosthetic foot dorsiflexion-plantarflexion and rotation motion limitations: prosthetic-limb trailing double support (0.789 ± 0.052), toe-off (0.760 ± 0.068) and toe-out angle (0.653 ± 0.256). Single limb stance, and stance and swing phase durations were most strongly associated with balance and walking performance measures. Notably, no symmetry ratio was significantly associated with patient-reported prosthetic function (unadjusted Pearson correlation coefficients r < 0.50, P > 0.05). More gait symmetry was associated with better balance and walking performance but had no significant relationship with patient-reported function. Although achieving gait symmetry after lower limb loss is a common walking goal, symmetry was unrelated to the perception of functional mobility for people with lower limb loss.

Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking

Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains.

Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection

Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high quality. The subsequent foot contact and foot off event detection proved highly accurate when compared to the gold standard based on co-registered ground reaction forces. Our proof-of-concept illustrates the capacity of combining accelerometry with machine learning for detecting isolated gait events irrespective of mode of locomotion.

Detecting Gait Events from Accelerations Using Reservoir Computing

Segmenting the gait cycle into multiple phases using gait event detection (GED) is a well-researched subject with many accurate algorithms. However, the algorithms that are able to perform accurate and robust GED for real-life environments and physical diseases tend to be too complex for their implementation on simple hardware systems limited in computing power and memory, such as those used in wearable devices. This study focuses on a numerical implementation of a reservoir computing (RC) algorithm called the echo state network (ESN) that is based on simple computational steps that are easy to implement on portable hardware systems for real-time detection. RC is a neural network method that is widely used for signal processing applications and uses a fast-training method based on a ridge regression adapted to the large quantity and variety of IMU data needed to use RC in various real-life environment GED. In this study, an ESN was used to perform offline GED with gait data from IMU and ground force sensors retrieved from three databases for a total of 28 healthy adults and 15 walking conditions. Our main finding is that despite its low complexity, ESN is robust for GED, with performance comparable to other state-of-the-art algorithms. Our results show the ESN is robust enough to obtain good detection results in all conditions if the algorithm is trained with variable data that match those conditions. The distribution of the mean absolute errors (MAE) between the detection times from the ESN and the force sensors were between 40 and 120 ms for 6 defined gait events (95th percentile). We compared our ESN with four different state-of-the-art algorithms from the literature. The ESN obtained a MAE not more than 10 ms above three other reference algorithms for normal walking indoor and outdoor conditions and yielded the 2nd lowest MAE and the 2nd highest true positive rate and specificity when applied to outdoor walking and running conditions. Our work opens the door to using the ESN as a GED for applications in wearable sensors for long-term patient monitoring.

Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking

A person's walking pattern can reveal important information about their health. Mounting multiple sensors onto loose clothing potentially offers a comfortable way of collecting data about walking and other human movement. This research investigates how well the data from three sensors mounted on the lateral side of clothing (on a pair of trousers near the waist, upper thigh and lower shank) correlate with the data from sensors mounted on the frontal side of the body. Data collected from three participants (two male, one female) for two days were analysed. Gait cycles were extracted based on features in the lower-shank accelerometry and analysed in terms of sensor-to-vertical angles (SVA). The correlations in SVA between the clothing- and body-mounted sensor pairs were analysed. Correlation coefficients above 0.76 were found for the waist sensor pairs, while the thigh and lower-shank sensor pairs had correlations above 0.90. The cyclical nature of gait cycles was evident in the clothing data, and it was possible to distinguish the stance and swing phases of walking based on features in the clothing data. Furthermore, simultaneously recording data from the waist, thigh, and shank was helpful in capturing the movement of the whole leg.

Intelligent wearable system with accurate detection of abnormal gait and timely cueing for mobility enhancement of people with Parkinson's disease

Previously reported wearable systems for people with Parkinson's disease (PD) have been focused on the detection of abnormal gait. They suffered from limited accuracy, large latency, poor durability, comfort, and convenience for daily use. Herewith we report an intelligent wearable system (IWS) that can accurately detect abnormal gait in real-time and provide timely cueing for PD patients. The system features novel sensitive, comfortable and durable plantar pressure sensing insoles with a highly compressed data set, an accurate and fast gait algorithm, and wirelessly controlled timely sensory cueing devices. A total of 29 PD patients participated in the first phase without cueing for developing processes of the algorithm, which achieved an accuracy of over 97% for off-line detection of freezing of gait (FoG). In the second phase with cueing, the evaluation of the whole system was conducted with 16 PD subjects via trial and a questionnaire survey. This system demonstrated an accuracy of 94% for real-time detection of FoG and a mean latency of 0.37 s between the onset of FoG and cueing activation. In questionnaire survey, 88% of the PD participants confirmed that this wearable system could effectively enhance walking, 81% thought that the system was comfortable and convenient, and 70% overcame the FoG. Therefore, the IWS makes it an effective, powerful, and convenient tool for enhancing the mobility of people with PD.

Inertial Sensor Estimation of Initial and Terminal Contact during In-Field Running

Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro–Inertial Measurement Unit (IMU) data to estimate contact time (CT) and flight time (FT) during running based on gyroscope and accelerometer sensors in a single location (ankle). Furthermore, a pre-processing system that detected the running period was introduced to analyse and enhance CT and FT detection accuracy and reduce noise. Results showed pre-processing successfully detected the designated running periods to remove noise of non-running periods. Furthermore, accelerometer and gyroscope algorithms showed good consistency within 95% confidence interval, and average absolute error of 31.53 ms and 24.77 ms, respectively. In turn, the combined system obtained a consistency of 84–100% agreement within tolerance values of 50 ms and 30 ms, respectively. Interestingly, both accuracy and consistency showed a decreasing trend as speed increased (36% at high-speed fore-foot strike). Successful CT and FT detection and output validation with consistency checking algorithms make in-field measurement of running gait possible using ankle-worn IMU sensors. Accordingly, accurate IMU-based gait analysis from gyroscope and accelerometer information can inform future research on in-field gait analysis.

Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data

Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.

Estimation of free-living walking cadence from wrist-worn sensor accelerometry data and its association with SF-36 quality of life scores

Objective. We evaluate the stride segmentation performance of the Adaptive Empirical Pattern Transformation (ADEPT) for subsecond-level accelerometry data collected in the free-living environment using a wrist-worn sensor.Approach. We substantially expand the scope of the existing ADEPT pattern-matching algorithm. Methods are applied to subsecond-level accelerometry data collected continuously for 4 weeks in 45 participants, including 30 arthritis and 15 control patients. We estimate the daily walking cadence for each participant and quantify its association with SF-36 quality of life measures.Main results. We provide free, open-source software to segment individual walking strides in subsecond-level accelerometry data. Walking cadence is significantly associated with the role physical score reported via SF-36 after adjusting for age, gender, weight and height.Significance. Methods provide automatic, precise walking stride segmentation, which allows estimation of walking cadence from free-living wrist-worn accelerometry data. Results provide new evidence of associations between free-living walking parameters and health outcomes.

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.

Classification of Standing and Walking States Using Ground Reaction Forces

The operation of wearable robots, such as gait rehabilitation robots, requires real-time classification of the standing or walking state of the wearer. This report explains a technique that measures the ground reaction force (GRF) using an insole device equipped with force sensing resistors, and detects whether the insole wearer is standing or walking based on the measured results. The technique developed in the present study uses the waveform length that represents the sum of the changes in the center of pressure within an arbitrary time window as the determining factor, and applies this factor to a conventional threshold method and an artificial neural network (ANN) model for classification of the standing and walking states. The results showed that applying the newly developed technique could significantly reduce classification errors due to shuffling movements of the patient, typically noticed in the conventional threshold method using GRF, i.e., real-time classification of the standing and walking states is possible in the ANN model. The insole device used in the present study can be applied not only to gait analysis systems used in wearable robot operations, but also as a device for remotely monitoring the activities of daily living of the wearer.

PyTHang: an open-source wearable sensor system for real-time monitoring of head-torso angle for ambulatory applications

This article presents the realization of a low-cost wearable sensor system and its Python-based software that can measure and record relative head-torso angle, especially in sagittal plane. The system is mainly developed to track head-torso angle during walk in a clinical study. The open-hardware part of the system is composed of a pair of triaxial digital accelerometers, a microprocessor, a Bluetooth module and a rechargeable battery unit. The reception of the transmitted acceleration data, visualization, interactive sensor alignment, angle estimation and data-logging are realized by the developed open-source graphical user interface. The system is tested on a tripod for verification and on a subject for practical demonstration. Developed system can be constructed and used for ambulatory monitoring and analysis of relative head-torso angle. Open-source user interface can be downloaded and developed for further (different) algorithms and device hardware.

Neuromuscular response to a single session of whole-body vibration in children with cerebral palsy: A pilot study

BACKGROUND

Whole-body vibration (WBV) is a relative new intervention paradigm that could reduce spasticity and improve motor function in children with cerebral palsy (CP). We investigated neuromuscular response to a single session of side-alternating WBV with different amplitudes in children with CP.

METHODS

Ten children with spastic CP aged 7-17 years at GMFCS level I-III participated in this pilot study. Participants received two sessions of side-alternating WBV with the same frequency (20 Hz) but different amplitudes (low-amplitude: 1 mm and high-amplitude: 2 mm). Each session included six sets of 90 s of WBV and 90 s of rest. Before and after each WBV session, we used (a) the modified Ashworth scale to evaluate the spasticity of the participants' leg muscles, (b) a quiet standing task to analyze center-of-pressure (CoP) pattern and postural control, and (c) overground walking trials to assess spatiotemporal gait parameters and joint range-of-motion (RoM).

RESULTS

Both WBV sessions similarly reduced the spasticity of the ankle plantarflexors, improved long-range correlation of CoP profile during standing, and reduced muscle activity of tibialis anterior during walking. The high-amplitude WBV further increased ankle RoM during walking.

CONCLUSIONS

This study demonstrates that a single session of WBV with either a low or a high amplitude can reduce spasticity, enhance standing posture, and improve gait patterns in children with CP. It suggests that low-amplitude WBV may induce similar neuromuscular response as high-amplitude WBV in children with spastic CP and can provide positive outcomes for those who are not able to tolerate stronger vibration.

Agreement of Gait Events Detection during Treadmill Backward Walking by Kinematic Data and Inertial Motion Units

Backward walking (BW) is being increasingly used in neurologic and orthopedic rehabilitation as well as in sports to promote balance control as it provides a unique challenge to the sensorimotor control system. The identification of initial foot contact (IC) and terminal foot contact (TC) events is crucial for gait analysis. Data of optical motion capture (OMC) kinematics and inertial motion units (IMUs) are commonly used to detect gait events during forward walking (FW). However, the agreement between such methods during BW has not been investigated. In this study, the OMC kinematics and inertial data of 10 healthy young adults were recorded during BW and FW on a treadmill at different speeds. Gait events were measured using both kinematics and inertial data and then evaluated for agreement. Excellent reliability (Interclass Correlation > 0.9) was achieved for the identification of both IC and TC. The absolute differences between methods during BW were 18.5 ± 18.3 and 20.4 ± 15.2 ms for IC and TC, respectively, compared to 9.1 ± 9.6 and 10.0 ± 14.9 for IC and TC, respectively, during FW. The high levels of agreement between methods indicate that both may be used for some applications of BW gait analysis.

Derivative Based Gait Event Detection Algorithm Using Unfiltered Accelerometer Signals

Wearable sensors have been investigated for the purpose of gait analysis, namely gait event detection. Many types of algorithms have been developed specifically using inertial sensor data for detecting gait events. Though much attention has turned toward machine learning algorithms, most of these approaches suffer from large computational requirements and are not yet suitable for real-time applications such as in prostheses or for feedback control. Current rules-based algorithms for real-time use often require fusion of multiple sensor signals to achieve high accuracy, thus increasing complexity and decreasing usability of the instrument. We present our results of a novel, rules-based algorithm using a single accelerometer signal from the foot to reliably detect heel-strike and toe-off events. Using the derivative of the raw accelerometer signal and applying an optimizer and windowing approach, high performance was achieved with a sensitivity and specificity of 94.32% and 94.70% respectively, and a timing error of 6.52 ± 22.37 ms, including trials involving multiple speed transitions. This would enable development of a compact wearable system for robust gait analysis in real-world settings, providing key insights into gait quality with the capability for real-time system control.

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.

Gait event detection using inertial measurement units in people with transfemoral amputation: a comparative study

In recent years, inertial measurement units (IMUs) have been proposed as an alternative to force platforms and pressure sensors for gait events (i.e., initial and final contacts) detection. While multiple algorithms have been developed, the impact of gait event timing errors on temporal parameters and asymmetry has never been investigated in people with transfemoral amputation walking freely on level ground. In this study, five algorithms were comparatively assessed on gait data of seven people with transfemoral amputation, equipped with three IMUs mounted at the pelvis and both shanks, using pressure insoles for reference. Algorithms' performance was first quantified in terms of gait event detection rate (sensitivity, positive predictive value). Only two algorithms, based on shank mounted IMUs, achieved an acceptable detection rate (positive predictive value > 99%). For these two, accuracy of gait events timings, temporal parameters, and absolute symmetry index of stance-phase duration (SPD-ASI) were assessed. Whereas both algorithms achieved high accuracy for stride duration estimates (median errors: 0%, interquartile ranges < 1.75%), lower accuracy was found for other temporal parameters due to relatively high errors in the detection of final contact events. Furthermore, SPD-ASI derived from IMU-based algorithms proved to be significantly different to that obtained from insoles data. Graphical abstract Gait event detection with IMU in people with transfemoral amputation: initial contact (IC) and final contact (FC) events at the sound (s) and prosthetic (p) side are identified. Five algorithms were implemented using either shank-mounted or pelvis-mounted IMUs. Gait events were used to estimate temporal parameters (stride duration, stance phase duration [SPD], and double support time) and SPD asymmetry.

A Determination Method for Gait Event Based on Acceleration Sensors

A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors

The sliding window technique is widely used to segment inertial sensor signals, i.e., accelerometers and gyroscopes, for activity recognition. In this technique, the sensor signals are partitioned into fix sized time windows which can be of two types: (1) non-overlapping windows, in which time windows do not intersect, and (2) overlapping windows, in which they do. There is a generalized idea about the positive impact of using overlapping sliding windows on the performance of recognition systems in Human Activity Recognition. In this paper, we analyze the impact of overlapping sliding windows on the performance of Human Activity Recognition systems with different evaluation techniques, namely, subject-dependent cross validation and subject-independent cross validation. Our results show that the performance improvements regarding overlapping windowing reported in the literature seem to be associated with the underlying limitations of subject-dependent cross validation. Furthermore, we do not observe any performance gain from the use of such technique in conjunction with subject-independent cross validation. We conclude that when using subject-independent cross validation, non-overlapping sliding windows reach the same performance as sliding windows. This result has significant implications on the resource usage for training the human activity recognition systems.

Time series classification using a modified LSTM approach from accelerometer-based data: A comparative study for gait cycle detection

BACKGROUND

Gait event detection (GED) is an important aspect in identifying and interpret a user's gait to assess gait abnormalities and design intelligent assistive devices.

RESEARCH QUESTION

There is a need to develop robust GED models that can accurately detect various gait instances in different scenarios and environments.

METHODS

This paper presents a novel method of detecting heel strikes (HS) and toe offs (TO) during the user's gait cycle using a modified Long Short-Term Memory (LSTM) networks approach. The method was tested on a database from Movement Analysis in Real-world Environments using Accelerometers (MAREA) (n = 20 healthy subjects) that consisted of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. Modifications include oversampling, composite accelerations and optimizing the LSTM network architecture were made.

RESULTS

Performance of our modified model was found to be better than six state-of-the-art GED algorithms, with a median F1 score of 0.98 for Heel Strikes and 0.98 for Toe Offs in the scenario of steady walking in an indoor environment, and a median F1 score of 0.94 for Heel Strikes and 0.68 for Toe-offs in the scenario of walking and running in an outdoor environment.

SIGNIFICANCE

This paper highlights the potential of the single proposed model to be an alternative to the six GED models in gait detection under various conditions.

Adaptive empirical pattern transformation (ADEPT) with application to walking stride segmentation

Quantifying gait parameters and ambulatory monitoring of changes in these parameters have become increasingly important in epidemiological and clinical studies. Using high-density accelerometry measurements, we propose adaptive empirical pattern transformation (ADEPT), a fast, scalable, and accurate method for segmentation of individual walking strides. ADEPT computes the covariance between a scaled and translated pattern function and the data, an idea similar to the continuous wavelet transform. The difference is that ADEPT uses a data-based pattern function, allows multiple pattern functions, can use other distances instead of the covariance, and the pattern function is not required to satisfy the wavelet admissibility condition. Compared to many existing approaches, ADEPT is designed to work with data collected at various body locations and is invariant to the direction of accelerometer axes relative to body orientation. The method is applied to and validated on accelerometry data collected during a $450$-m outdoor walk of $32$ study participants wearing accelerometers on the wrist, hip, and both ankles. Additionally, all scripts and data needed to reproduce presented results are included in supplementary material available at Biostatistics online.

Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking

Lower limb exoskeletons require the correct support magnitude and timing to achieve user assistance. This study evaluated whether the sign of the angular velocity of lower limb segments can be used to determine the timing of the stance and the swing phase during walking. We assumed that stance phase is characterized by a positive, swing phase by a negative angular velocity. Thus, the transitions can be used to also identify heel-strike and toe-off. Thirteen subjects without gait impairments walked on a treadmill at speeds between 0.5 and 2.1 m/s on level ground and inclinations between −10 and +10°. Kinematic and kinetic data was measured simultaneously from an optical motion capture system, force plates, and five inertial measurement units (IMUs). These recordings were used to compute the angular velocities of four lower limb segments: two biological (thigh, shank) and two virtual that were geometrical projections of the biological segments (virtual leg, virtual extended leg). We analyzed the reliability (two sign changes of the angular velocity per stride) and the accuracy (offset in timing between sign change and ground reaction force based timing) of the virtual and biological segments for detecting the gait phases stance and swing. The motion capture data revealed that virtual limb segments seem superior to the biological limb segments in the reliability of stance and swing detection. However, increased signal noise when using the IMUs required additional rule sets for reliable stance and swing detection. With IMUs, the biological shank segment had the least variability in accuracy. The IMU-based heel-strike events of the shank and both virtual segment were slightly early (3.3–4.8% of the gait cycle) compared to the ground reaction force-based timing. Toe-off event timing showed more variability (9.0% too early to 7.3% too late) between the segments and changed with walking speed. The results show that the detection of the heel-strike, and thus stance phase, based on IMU angular velocity is possible for different segments when additional rule sets are included. Further work is required to improve the timing accuracy for the toe-off detection (swing).

Gait Event Detection From Accelerometry Using the Teager-Kaiser Energy Operator

OBJECTIVE

A novel method based on the application of the Teager-Kaiser Energy Operator is presented to estimate instances of initial contact (IC) and final contact (FC) from accelerometry during gait. The performance of the proposed method was evaluated against four existing gait event detection (GED) methods under three walking conditions designed to capture the variance of gait in real-world environments.

METHODS

A symmetric discrete approximation of the Teager-Kaiser energy operator was used to capture simultaneous amplitude and frequency modulations of the shank acceleration signal at IC and FC during flat treadmill walking, inclined treadmill walking, and flat indoor walking. Accuracy of estimated gait events were determined relative to gait events detected using force-sensitive resistors. The performance of the proposed algorithm was assessed against four established methods by comparing mean-absolute error, sensitivity, precision, and F1-score values.

RESULTS

The proposed method demonstrated high accuracy for GED in all walking conditions, yielding higher F1-scores (IC: >0.98, FC: >0.9) and lower mean-absolute errors (IC: <0.018s, FC: <0.039s) than other methods examined. Estimated ICs from shank-based methods tended to exhibit unimodal distributions preceding the force-sensitive resistor estimated ICs, whereas estimated gait events for waist-based methods had quasiuniform random distributions and lower accuracy.

CONCLUSION

Compared with the established gait event detection methods, the proposed method yielded comparably high accuracy for IC detection, and was more accurate than all other methods examined for FC detection.

SIGNIFICANCE

The results support the use of the Teager-Kaiser Energy Operator for accurate automated GED across a range of walking conditions.

Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual's risk for falls.

Real-time gait event detection in a real-world environment using a laser-ranging sensor and gyroscope fusion method

OBJECTIVE

Wearable gait event detection (GED) techniques have great potential for clinical applications by aiding the rehabilitation of individuals in their daily living environment. Unlike previous wearable GED techniques, which have been proposed for offline detection or laboratory settings, we aimed to develop a real-time GED system adapted for utilization in the daily living environment.

APPROACH

This study presents a novel GED system in which foot clearance and sagittal angular velocity were incorporated to realize real-time GED in a real-world environment. The accuracy and robustness of the proposed system were validated in a real-world scenario that consisted of complex ground surfaces, i.e. varying inclinations. Forty-three subjects (23-83 years) were included in this study, and a total of 8866 gait cycles were recorded for analysis.

MAIN RESULTS

The proposed system demonstrated consistently high performance in detecting toe off (TO) and heel strike (HS) events in indoor and outdoor walking data which was supported by high performance scores. The detection accuracy of the walking data reached 2.59  ±  13.26 ms (indoor) and 3.31  ±  14.78 ms (outdoor) for TO events, 3.36  ±  15.92 ms (indoor) and 3.77  ±  16.99 ms (outdoor) for HS events. The proposed system showed better performance in detection precision than state-of-the-art real-time GED methods.

SIGNIFICANCE

The proposed system will benefit the development of long-term analysis and intervention techniques for use in clinics and daily living environments.

Accelerations from wearable accelerometers reflect knee loading during running after anterior cruciate ligament reconstruction

BACKGROUND

Following anterior cruciate ligament reconstruction, individuals exhibit sagittal plane knee loading deficits as they underload their injured limb during running. These between-limb biomechanical differences are difficult to clinically detect. Wearable accelerometers may aid in the development of early rehabilitation programs to improve symmetrical loading. This study aimed to identify whether segment accelerations from wearable accelerometers can predict knee loading asymmetry in an anterior cruciate ligament reconstructed population.

METHODS

14 individuals 5-months post-anterior cruciate ligament reconstruction performed self-selected speed running. Data were collected concurrently using a marker-based motion system and accelerometers positioned on participants' shanks and thighs. Stepwise linear regression was used to determine predictive value of accelerometer data on biomechanical variables.

FINDING

Shank acceleration was not predictive of any biomechanical variable. Between-limb differences in thigh axial acceleration explained 30% of the variance in between-limb differences in knee power absorption (p = 0.045), suggesting that accelerometers placed on proximal joint segments may provide information regarding knee loading asymmetry. Between-limb differences in thigh axial acceleration also explained 38% of the variance in between-limb differences in ground reaction force (p = 0.002).

INTERPRETATION

These relationships indicate that accelerations from wearable accelerometers may provide some useful information regarding knee loading during running in individuals following anterior cruciate ligament reconstruction.

Abnormal muscle activation patterns are associated with chronic gait deficits following traumatic brain injury

BACKGROUND

Gait and balance disorders are common among individuals who have experienced a mild to moderate traumatic brain injury (TBI). However, little is known about how the neuromuscular control of gait is altered following a TBI.

RESEARCH QUESTION

Investigate the relationship between lower limb muscle activation patterns and chronic gait deficits in individuals who previously experienced a mild to moderate TBI.

METHODS

Lower extremity electromyographic (EMG) signals were collected bilaterally during treadmill and overground walking in 44 ambulatory individuals with a TBI >1 year prior and 20 unimpaired controls. Activation patterns of TBI muscles were cross-correlated with normative data from control subjects to assess temporal phasing of muscle recruitment. Clinical assessments of gait and balance were performed using dynamic posturography, the dynamic gait index, six-minute walk test, and preferred walking speed.

RESULTS

TBI subjects exhibited abnormal activation patterns in the tibialis anterior, medial gastrocnemius, and rectus femoris muscles during both overground and treadmill walking. Activation patterns of the vastus lateralis and soleus muscles did not differ from normal. There was considerable heterogeneity in performance on clinical balance and gait assessments. Abnormal muscle activation patterns were significantly correlated with variations in the dynamic gait index among the TBI subjects.

SIGNIFICANCE

Individuals who have experienced a prior TBI do exhibit characteristic changes in the temporal coordination of select lower extremity muscles, which may contribute to impairments during challenging walking tasks.

No differences in objective dynamic instability during acceleration of the knee with or without subjective instability post-total knee arthroplasty

INTRODUCTION

Instability after total knee arthroplasty is a critical problem. The purpose of this study was to clarify the stability of implanted knees during walking by comparing differences in dynamic instability during knee acceleration between individuals with or without previously experienced subjective instability, as measured by self-reported questionnaire.

MATERIALS AND METHODS

We examined 92 knees with medial pivot implants. Mean patient age and follow-up duration were 78.4 years and 32.8 months, respectively. An accelerometer was used to investigate the accelerations along three axes; that is, vertical (VT), mediolateral (ML), and anteroposterior (AP) directions in 3-dimensional (3D) space. The analysis in the stance phase and gait cycle was performed by: (1) root mean square (RMS) values of acceleration and (2) frequency domain analysis using fast Fourier transformation (FFT). A self-reported knee instability score was used for the subjective feeling of instability.

RESULTS

A total of 76 knees did not feel unstable (group 0), but 16 knees felt unstable (group 1) in patients during activities of daily living. Regarding the RMS, there were no differences in each direction between the groups. For FFT, the cumulative amplitude in the frequency < 30 Hz also showed no significant differences in all directions between the groups during the stance phase (VT, p = 0.335; ML, p = 0.219; AP, p = 0.523) or gait cycle (VT, p = 0.077; ML, p = 0.082; AP, p = 0.499).

DISCUSSION

Gait analysis based on the acceleration data showed that there were no between-group differences in objective dynamic instability during acceleration of the knee, with or without reports of previously experienced subjective instability, as assessed by the self-reported questionnaire.

Inertial Sensing for Gait Event Detection and Transfemoral Prosthesis Control Strategy

OBJECTIVE

This paper presents a method for walking gait event detection using a single inertial measurement unit (IMU) mounted on the shank.

METHODS

Experiments were conducted to detect heel strike (HS) and toe off (TO) gait events of 10 healthy subjects and 5 transfemoral amputees walking at various speeds and slopes on an instrumented treadmill. The performance of three different algorithms [thresholding (THR), linear discriminant analysis, and quadratic discriminant analysis] was evaluated on both timing and frequency of gait event detections compared to data collected using force plates.

RESULTS

Though all algorithms could be used reliably (within 8.2% stride temporal error and 0.2% frequency error), THR was the most accurate, detecting 100% of gait events within an average of 2% stride for both the healthy subjects and the amputees. Furthermore, universal parameters could be used across all speeds and slopes within each demographic.

CONCLUSION

HS and TO for walking gait can be reliably detected in healthy and transfemoral amputee subjects using a single IMU.

SIGNIFICANCE

This work provides a robust, simple, and inexpensive method of gait event detection that does not rely on a load cell and could be easily implemented in a lower-limb prosthesis.

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.

Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson's Disease

Robust gait segmentation is the basis for mobile gait analysis. A range of methods have been applied and evaluated for gait segmentation of healthy and pathological gait bouts. However, a unified evaluation of gait segmentation methods in Parkinson’s disease (PD) is missing. In this paper, we compare four prevalent gait segmentation methods in order to reveal their strengths and drawbacks in gait processing. We considered peak detection from event-based methods, two variations of dynamic time warping from template matching methods, and hierarchical hidden Markov models (hHMMs) from machine learning methods. To evaluate the methods, we included two supervised and instrumented gait tests that are widely used in the examination of Parkinsonian gait. In the first experiment, a sequence of strides from instructed straight walks was measured from 10 PD patients. In the second experiment, a more heterogeneous assessment paradigm was used from an additional 34 PD patients, including straight walks and turning strides as well as non-stride movements. The goal of the latter experiment was to evaluate the methods in challenging situations including turning strides and non-stride movements. Results showed no significant difference between the methods for the first scenario, in which all methods achieved an almost 100% accuracy in terms of F-score. Hence, we concluded that in the case of a predefined and homogeneous sequence of strides, all methods can be applied equally. However, in the second experiment the difference between methods became evident, with the hHMM obtaining a 96% F-score and significantly outperforming the other methods. The hHMM also proved promising in distinguishing between strides and non-stride movements, which is critical for clinical gait analysis. Our results indicate that both the instrumented test procedure and the required stride segmentation algorithm have to be selected adequately in order to support and complement classical clinical examination by sensor-based movement assessment.

Accuracy of three methods in gait event detection during overground running

Inertial measurement units (IMUs) have been extensively used to detect gait events. Various methods have been proposed for detecting initial contact (IC) and toe-off (TO) using IMUs affixed at various anatomical locations. However, the accuracy of such methods has yet to be compared. This study evaluated the accuracy of three common methods used for detecting gait events during jogging and running: (1) S-method, in which IC is identified as the instant of peak foot-resultant acceleration and TO is identified when the acceleration exceeds a threshold of 2g in the region of interest; (2) M-method, in which IC and TO are defined as the minimum before the positive peak shank vertical acceleration and the minimum in the region of interest, respectively; and (3) L-method, in which IC is indicated by the instant of peak pelvis anteroposterior acceleration and TO is identified by the maximum in the region of interest. The performance of the IMU-based methods in detecting IC and TO and estimating stance time (ST) were tested on 11 participants at jogging and running speeds against a reference provided by a force-platform method. The S-method was the most accurate for IC detection (overall mean absolute difference (MAD): 4.7±4.1ms). The M-method was the most accurate for TO detection (overall MAD: 7.0±3.5ms). A combination of M- and S-methods, called the MS-method, was the most accurate for ST estimation (overall MAD: 9.0±3.9ms). Thus, the MS-method is recommended for ST estimation; however, this method requires four IMUs for bilateral estimation.

Reference values of gait using APDM movement monitoring inertial sensor system

Normal gait data reported show variability depending on specific equipment and techniques. Reference values of initial contact angle (ICA) and toe-off angle (TOA) are still lacking. We present a normative gait database of 292 healthy adults using the APDM Movement Monitoring inertial sensor system across a large age span of adulthood. Data were collected as participants completed a walk test for 2 min. Normalization was conducted and two factors were extracted by a factor analysis. Six reference gait variables under each factor were presented and the impacts of age, gender and BMI were evaluated by MANOVA and ANCOVA. ICA and TOA were highly correlated with speed and stride length. ICA was significantly larger in men, whereas larger TOA could be observed in women in all age groups but could not achieve significant difference. Overweight and obese adults walked at significantly lower speed, shorter stride length, reduced cadence and longer gait cycle duration. TOA was smaller in the obese group. However, the differences in ICA were not significant. Reference gait values described herein were valuable for identifying and interpreting gait phenomena using APDM®, contributing to rehabilitation of gait dysfunction.

Influence of anterior load carriage on lumbar muscle activation while walking in stable and unstable shoes

Load carriage can be harmful for workers, and alternative interventions to reduce back pain while walking and carrying loads are necessary. Unstable shoes have been used to improve balance and reduce back pain, but it is unknown whether walking wearing unstable shoes while carrying loads anteriorly causes excessive trunk extensors muscle activation. The aim of this study was to investigate the effects of different shoe types and anterior load carriage on gait kinematics and lumbar electromyographic (EMG) activity. Fourteen adults that predominantly walk or stand during the work day were asked to walk with and without carrying 10% of body mass anteriorly while wearing regular walking shoes (REG) and unstable shoes (MBT). The effects of shoe type, load carriage, and shoe × load interactions on the longissimus thoracis (LT) and iliocostalis lumborum (IC) EMG, stride duration, and stride frequency were assessed. MBT shoes induced a significant increase in LT (44.4 ± 35%) and IC EMG (33.0 ± 32%, p < .005), while load carriage increased LT (58.5 ± 41%) and IC EMG (55.1 ± 32%, p < .001). No significant shoe × load interaction was found (p>.05). However, walking wearing MBT shoes while carrying loads induced a 46 ± 40% higher EMG activity compared to walking wearing MBT shoes without load carriage. No effects of shoes or load carriage were found on stride duration and stride frequency. It was concluded that walking wearing MBT shoes and carrying 10% of total body mass induced greater activation of trunk extensors muscle compared to these factors in isolation, such a combination may not influence gait patterns.

Real-time gait event detection for lower limb amputees using a single wearable sensor

This paper presents a rule-based real-time gait event/phase detection system (R-GEDS) using a shank mounted inertial measurement unit (IMU) for lower limb amputees during the level ground walking. Development of the algorithm is based on the shank angular velocity in the sagittal plane and linear acceleration signal in the shank longitudinal direction. System performance was evaluated with four control subjects (CS) and one transfemoral amputee (TFA) and the results were validated with four FlexiForce footswitches (FSW). The results showed a data latency for initial contact (IC) and toe off (TO) within a range of ± 40 ms for both CS and TFA. A delay of about 3.7 ± 62 ms for a foot-flat start (FFS) and an early detection of -9.4 ± 66 ms for heel-off (HO) was found for CS. Prosthetic side showed an early detection of -105 ± 95 ms for FFS whereas intact side showed a delay of 141 ±73 ms for HO. The difference in the kinematics of the TFA and CS is one of the potential reasons for high variations in the time difference. Overall, detection accuracy was 99.78% for all the events in both groups. Based on the validated results, the proposed system can be used to accurately detect the temporal gait events in real-time that leads to the detection of gait phase system and therefore, can be utilized in gait analysis applications and the control of lower limb prostheses.

Instrumented gait analysis: a measure of gait improvement by a wheeled walker in hospitalized geriatric patients

Background

In an increasing aging society, reduced mobility is one of the most important factors limiting activities of daily living and overall quality of life. The ability to walk independently contributes to the mobility, but is increasingly restricted by numerous diseases that impair gait and balance. The aim of this cross-sectional observation study was to examine whether spatio-temporal gait parameters derived from mobile instrumented gait analysis can be used to measure the gait stabilizing effects of a wheeled walker (WW) and whether these gait parameters may serve as surrogate marker in hospitalized patients with multifactorial gait and balance impairment.

Methods

One hundred six patients (ages 68–95) wearing inertial sensor equipped shoes passed an instrumented walkway with and without gait support from a WW. The walkway assessed the risk of falling associated gait parameters velocity, swing time, stride length, stride time- and double support time variability. Inertial sensor-equipped shoes measured heel strike and toe off angles, and foot clearance.

Results

The use of a WW improved the risk of spatio-temporal parameters velocity, swing time, stride length and the sagittal plane associated parameters heel strike and toe off angles in all patients. First-time users (FTUs) showed similar gait parameter improvement patterns as frequent WW users (FUs). However, FUs with higher levels of gait impairment improved more in velocity, stride length and toe off angle compared to the FTUs.

Conclusion

The impact of a WW can be quantified objectively by instrumented gait assessment. Thus, objective gait parameters may serve as surrogate markers for the use of walking aids in patients with gait and balance impairments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12984-017-0228-z) contains supplementary material, which is available to authorized users.

Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring.

Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database

Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings. This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios.

A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation

Lower extremity amputees suffer from mobility limitations which will result in a degradation of their quality of life. Wearable sensors are frequently used to assess spatio-temporal, kinematic and kinetic parameters providing the means to establish an interactive control of the amputee-prosthesis-environment system. Gait events and the gait phase detection of an amputee's locomotion are vital for controlling lower limb prosthetic devices. The paper presents an approach to real-time gait event detection for lower limb amputees using a wireless gyroscope attached to the shank when performing level ground and ramp activities. The results were validated using both healthy and amputee subjects and showed that the time differences in identifying Initial Contact (IC) and Toe Off (TO) events were larger in a transfemoral amputee when compared to the control subjects and a transtibial amputee (TTA). Overall, the time difference latency lies within a range of ±50 ms while the detection rate was 100% for all activities. Based on the validated results, the IC and TO events can be accurately detected using the proposed system in both control subjects and amputees when performing activities of daily living and can also be utilized in the clinical setup for rehabilitation and assessing the performance of lower limb prosthesis users.

Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Gait analysis using wearable wireless sensors can be an economical, convenient and effective way to provide diagnostic and clinical information for various health-related issues. In this work, our custom designed low-cost wireless gait analysis sensor that contains a basic inertial measurement unit (IMU) was used to collect the gait data for four patients diagnosed with balance disorders and additionally three normal subjects, each performing the Dynamic Gait Index (DGI) tests while wearing the custom wireless gait analysis sensor (WGAS). The small WGAS includes a tri-axial accelerometer integrated circuit (IC), two gyroscopes ICs and a Texas Instruments (TI) MSP430 microcontroller and is worn by each subject at the T4 position during the DGI tests. The raw gait data are wirelessly transmitted from the WGAS to a near-by PC for real-time gait data collection and analysis. In order to perform successful classification of patients vs. normal subjects, we used several different classification algorithms, such as the back propagation artificial neural network (BP-ANN), support vector machine (SVM), k-nearest neighbors (KNN) and binary decision trees (BDT), based on features extracted from the raw gait data of the gyroscopes and accelerometers. When the range was used as the input feature, the overall classification accuracy obtained is 100% with BP-ANN, 98% with SVM, 96% with KNN and 94% using BDT. Similar high classification accuracy results were also achieved when the standard deviation or other values were used as input features to these classifiers. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real time using various classifiers, the success of which may eventually lead to accurate and objective diagnosis of abnormal human gaits and their underlying etiologies in the future, as more patient data are being collected.