Stride Detection Research Articles

Evaluation of a finite state machine algorithm to measure stepping with ankle accelerometry: performance across a range of gait speeds, tasks, and individual walking ability.

Wearable sensors, including accelerometers, are a widely accepted tool to assess gait in clinical and free-living environments. Methods to identify phases and subphases of the gait cycle are necessary for comprehensive assessment of pathological gait. The current study evaluated the accuracy of a finite state machine (FSM) algorithm to detect strides by identifying gait cycle subphases from ankle-worn accelerometry. Algorithm performance was challenged across a range of speeds (0.4-2.6 m/s), task conditions (e.g., single- and dual-task walking), and individual characteristics. Specifically, the study included a range of treadmill speeds in young adults and overground walking conditions in older adults with neurological disease. Manually counted and algorithm-derived stride detection from acceleration data were evaluated using error analysis and Bland-Altman plots for visualization. Overall, the algorithm successfully detected strides (>96% accuracy) across gait speed ranges and tasks, for young and older adults. The accuracy of an FSM algorithm combined with ankle-worn accelerometers, provides an analytical approach with affordable and portable tools that permits comprehensive assessment of gait unbounded by setting and proves to perform well in in walking tasks characterized by variable walking. These algorithm capabilities and advancements are critical for identifying phase dependent gait impairments in clinical and free-living assessment.

Development of Stride Detection System for Helping Stroke Walking Training

Walking is a popular post-stroke rehabilitation exercise for patients. Stroke walking training is a sort of physical therapy that aims to help people who have had a stroke improve their walking ability. The goal of this research is to classify stride length and include it into a mobile application. The accelerometer sensor on a smartphone can be used to construct a stride detection system to aid in stroke walking training. This application was created for Android-powered smartphones. A binder must be used to secure the smartphone device to the patient's thigh. This application reads the accelerometer sensor included into the smartphone. In this study, a stride detection model is designed to increase the performance of stride length and circumduction detection. The accelerometer is read and saved by the application as the participant walks on the specific path. After the signal has been pre-processed and its feature extracted, the data is used to create the stride detection model. The performance is good, as evidenced by accuracy, precision, recall, and f-measure values of 88.60%, 88.60%, 88.60%, and 88.60%, respectively. When utilized on a stride detection system, the decision tree algorithms function admirably. The model is then loaded into the Android walking app.

Automated Implementation of the Edinburgh Visual Gait Score (EVGS) Using OpenPose and Handheld Smartphone Video

Recent advancements in computing and artificial intelligence (AI) make it possible to quantitatively evaluate human movement using digital video, thereby opening the possibility of more accessible gait analysis. The Edinburgh Visual Gait Score (EVGS) is an effective tool for observational gait analysis, but human scoring of videos can take over 20 min and requires experienced observers. This research developed an algorithmic implementation of the EVGS from handheld smartphone video to enable automatic scoring. Participant walking was video recorded at 60 Hz using a smartphone, and body keypoints were identified using the OpenPose BODY25 pose estimation model. An algorithm was developed to identify foot events and strides, and EVGS parameters were determined at relevant gait events. Stride detection was accurate within two to five frames. The level of agreement between the algorithmic and human reviewer EVGS results was strong for 14 of 17 parameters, and the algorithmic EVGS results were highly correlated (r > 0.80, “r” represents the Pearson correlation coefficient) to the ground truth values for 8 of the 17 parameters. This approach could make gait analysis more accessible and cost-effective, particularly in areas without gait assessment expertise. These findings pave the way for future studies to explore the use of smartphone video and AI algorithms in remote gait analysis.

Human gait-labeling uncertainty and a hybrid model for gait segmentation.

Evaluate manual labeling uncertainty and introduce a hybrid stride detection and gait-event estimation model for autonomous, long-term, and remote monitoring. Estimate inter-labeler inconsistencies by computing the limits-of-agreement. Develop a hybrid model based on dynamic time warping and convolutional neural network to identify valid strides and eliminate non-stride data in inertial (walking) data collected by a wearable device. Finally, detect gait events within a valid stride region. The limits of inter-labeler agreement for key gait events heel off, toe off, heel strike, and flat foot are 72, 16, 24, and 80 ms, respectively; The hybrid model's classification accuracy for stride and non-stride are 95.16 and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24, 5, 9, and 13 ms, respectively, when compared to the average human labels. The results show the inherent labeling uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers, and it is a valid model to reliably detect strides and estimate the gait events in human gait data. This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.

High-Performance Stacked Ensemble Model for Stride Length Estimation with Potential Application in Indoor Positioning Systems

The pedestrian dead reckoning (PDR) infrastructure-independent positioning method is of particular interest for many researchers due to its effectiveness in indoor positioning systems. The PDR technique consists of three main components including stride detection, movement heading estimation and stride length estimation (SLE). Among those, SLE results can be utilized in numerous applications, such as indoor positioning, disease diagnosis, incident detecting for patient warning, etc.. Traditional solutions for estimating stride length usually have simple structures with short calculation time but they do not ensure the expected accuracy as desired. To enhance the estimated stride length, recently, many solutions employing deep learning based techniques such as Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), have been developed to improve the SLE result. Although the accuracy in SLE has been improved compared to traditional methods, these deep learning based solutions still remain some limitations, such as complex structure and huge training parameters. This paper proposes some solutions of using machine learning based stacked ensemble model and simple machine learning algorithm to improve the accuracy of SLE while satisfying requirements such as simple structures, short execution time. The experimental results on real field data show that the proposed approaches outperform other state-of-the-art deep learning based methods on both SLE accuracy and computation time by at least 12% more accurate and seven times faster, respectively.

Robust Foot Motion Recognition Using Stride Detection and Weak Supervision-Based Fast Labeling

Foot motion recognition in daily life faces two challenges imposed by traditional machine learning frameworks: how to robustly recognize various foot motions from continuous movements in uncontrolled environments, and how to accurately extract ground truths. To address these challenges, in this paper, we propose a stride detection method to robustly identify each stride (over 99% accuracy). We then investigate two weak supervision-based fast labeling frameworks to automatically label the stride segmentations. Finally, we use these two frameworks to identify foot motions from continuous movements integrated on a route map. The route map can be replaced by a virtual-reality video game to play in daily life so that the user's long-term foot functionality can be profiled and evaluated. We test our proposed approaches using a smart insole with twenty-two subjects whose movement data are collected through the route map setting while video camera recordings serve as the ground truth. The route map integrates seven foot motions in one complete play, which includes three continuous motions resulted from continuous full-body movements (named as continuous motions) and four intermittent foot motions that are produced repetitively (named as repetitive motions). Compared to the best traditional machine learning methods, our proposed approach improves the leave-one-subject-out cross-validation accuracy of all subjects by 6.12% for the three continuous motions, 2.71% for the four repetitive motions and 4.90% for the total of seven foot motions. In addition, our proposed method saves 25% to 50% time in data labeling.

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.

Evaluation and Application of a Customizable Wireless Platform: A Body Sensor Network for Unobtrusive Gait Analysis in Everyday Life.

Body sensor networks (BSNs) represent an important research tool for exploring novel diagnostic or therapeutic approaches. They allow for integrating different measurement techniques into body-worn sensors organized in a network structure. In 2011, the first Integrated Posture and Activity Network by MedIT Aachen (IPANEMA) was introduced. In this work, we present a recently developed platform for a wireless body sensor network with customizable applications based on a proprietary communication interface. In particular, we present a sensor setup for gait analysis during everyday life monitoring. The arrangement consists of three identical inertial measurement sensors attached at the wrist, thigh, and chest. We additionally introduce a force-sensitive resistor integrated insole for measurement of ground reaction forces (GRFs), to enhance the assessment possibilities and generate ground truth data for inertial measurement sensors. Since the is not strongly represented in existing BSN implementations, we validate the proposed system concerning an application in gait analysis and use this as a representative demonstration of realizability. Hence, there are three key aspects of this project. The system is evaluated with respect to (I) accurate timing, (II) received signal quality, and (III) measurement capabilities of the insole pressure nodes. In addition to the demonstration of feasibility, we achieved promising results regarding the extractions of gait parameters (stride detection accuracy: , Root-Mean-Square Deviation (RMSE) of mean stride time: , RMSE of percentage stance time: ). Conclusion: With the satisfactory technical performance in laboratory and application environment and the convincing accuracy of the gait parameter extraction, the presented system offers a solid basis for a gait monitoring system in everyday life.

Novel, clinically applicable method to measure step-width during the swing phase of gait

Objective: Step-width during walking is an indicator of stability and balance in patients with neurological disorders, and development of objective tools to measure this clinically would be a great advantage. The aim of this study was to validate an in-house-developed gait analysis system (Striton), based on optical and inertial sensors and a novel method for stride detection, for measuring step-width during the swing phase of gait and temporal parameters. Approach: The step-width and stride-time measurements were validated in an experimental setup, against a 3D motion capture system and on an instrumented walkway. Further, test-retest and day-to-day variability were evaluated, and gait parameters were collected from 87 elderly persons (EP) and four individuals with idiopathic normal pressure hydrocephalus (iNPH) before/after surgery. Main results: Accuracy of the step-width measurement was high: in the experimental setup mean error was 0.08 ± 0.25 cm (R = 1.00) and against the 3D motion capture system 0.04 ± 1.12 cm (R = 0.98). Test-retest and day-to-day measurements were equal within ±0.5 cm. Mean difference in stride time was −0.003 ± 0.008 s between Striton and the instrumented walkway. The Striton system was successfully applied in the clinical setting on individuals with iNPH, which had larger step-width (6.88 cm, n = 4) compared to EP (5.22 cm, n = 87). Significance: We conclude that Striton is a valid, reliable and wearable system for quantitative assessment of step-width and temporal parameters during gait. Initial measurements indicate that the newly defined step-width parameter differs between EP and patients with iNPH and before/after surgery. Thus, there is potential for clinical applicability in patients with reduced gait stability.

Gait rehabilitation monitor

This paper presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), an Android smartphone and a developed app for collecting, primary processing of data and for persistence of data in a remote PostgreSQL database, which is available in a remote server and where further data processing is performed. This framework provides gait features classifier by invoking an implemented REST API available in the remote server. Low computational load algorithms based on Euler angles and filtered signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms. After segmentation of the remotely collected signals for gait strides identification relevant features were extracted to feed, train and test a classifier for prediction of gait abnormalities using supervised machine learning type and Extremely Randomized Trees method.

Comparison of algorithms and classifiers for stride detection using wearables

Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this proposal, we developed a stride detection system for daily life use. We compared mostly used algorithms min–max patterns, dynamic time warping, convolutional neural networks (CNN), and automatic framing using two data sets of 32 healthy and 28 Parkinson’s disease (PD) persons. We developed an insole with force and IMU sensors to record the gait data. The PD patients carried out the standardized time up and go test, and the healthy persons a daily life activities test (walking, sitting, standing, ascending and descending stairs). As an automatically stride detection process for daily life use, we propose a first stride detection using automatic framing, and after normalization and resampling data a CNN is used. A F1-score of 0.938 (recall 0.968, precision 0.910) for time up and go test and of 0.944 (recall 0.992, precision 0.901) for daily life activities test were obtained for CNN. Compared to the other detection methods, up to 6% F-measure improvement was shown.

Pedestrian Dead Reckoning Using Pocket-Worn Smartphone

Indoor pedestrian dead reckoning (PDR) extends the location-based service (LBS) to environments, where GPS or beacon signals are degraded or unavailable. A practical PDR system should consider the absence of any infrastructure or prior knowledge of the environment. This paper presents a PDR system based on a pocket-worn smartphone, which tracks a person's location through dead reckoning calculation by using the sensors embedded in smartphones. However, a smartphone-based PDR system faces various challenges, especially the heading drift due to gyroscope bias. In this paper, the gradient descent algorithm (GDA) is improved to reduce the heading drift, by fusing inertial data with only a fraction of magnetometer data that are accurate and usable. There is an 80% probability that the heading error is reduced to less than 4 degrees. Besides, a stride detection method is developed based on thigh angles, and then, a stride length estimation method is implemented in a complementary way. The experiments were conducted along with three types of reference paths, and the experimental results demonstrate that the average position errors along the three paths are 1.62%, 1.00%, and 0.92%, respectively. Despite the inherent sensor noise and complex human locomotion, the smartphone-based PDR system has great potential in pedestrian tracking.

Validation of distal limb mounted inertial measurement unit sensors for stride detection in Warmblood horses at walk and trot.

SummaryBackgroundInertial measurement unit (IMU) sensor‐based techniques are becoming more popular in horses as a tool for objective locomotor assessment.ObjectivesTo describe, evaluate and validate a method of stride detection and quantification at walk and trot using distal limb mounted IMU sensors.Study designProspective validation study comparing IMU sensors and motion capture with force plate data.MethodsA total of seven Warmblood horses equipped with metacarpal/metatarsal IMU sensors and reflective markers for motion capture were hand walked and trotted over a force plate. Using four custom built algorithms hoof‐on/hoof‐off timing over the force plate were calculated for each trial from the IMU data. Accuracy of the computed parameters was calculated as the mean difference in milliseconds between the IMU or motion capture generated data and the data from the force plate, precision as the s.d. of these differences and percentage of error with accuracy of the calculated parameter as a percentage of the force plate stance duration.ResultsAccuracy, precision and percentage of error of the best performing IMU algorithm for stance duration at walk were 28.5, 31.6 ms and 3.7% for the forelimbs and −5.5, 20.1 ms and −0.8% for the hindlimbs, respectively. At trot the best performing algorithm achieved accuracy, precision and percentage of error of −27.6/8.8 ms/−8.4% for the forelimbs and 6.3/33.5 ms/9.1% for the hindlimbs.Main limitationsThe described algorithms have not been assessed on different surfaces.ConclusionsInertial measurement unit technology can be used to determine temporal kinematic stride variables at walk and trot justifying its use in gait and performance analysis. However, precision of the method may not be sufficient to detect all possible lameness‐related changes. These data seem promising enough to warrant further research to evaluate whether this approach will be useful for appraising the majority of clinically relevant gait changes encountered in practice.

Validation of Distal Limb Mounted Imu Sensors for Stride Detection and Locomotor Quantification in Warmblood Horses at Walk and Trot

Reasons for performing study: IMU-sensor based techniques arebecoming more popular in horses as a tool for objective locomotorassessment. Using currently proposed methods only limited informationabout stride variables can be obtained for walk and trot.Objectives: To describe, evaluate and validate a method of stridedetection and quantification (i.e. hoof-on/off detection and stanceduration calculation) at walk and trot using distal limb mounted IMU-sensors.Study design: Validation study.Methods: To compare IMU-sensors and motion capture (MoCap) withforce plate data as the gold standard, 7 warmblood horses equippedwith metacarpal/metatarsal IMU-sensors (range: low-g accelerometer16 g; high-g accelerometer 200 g) and reflective markers for MoCapwere hand walked and trotted over a force plate. All instruments wereframe synchronised and data were collec ted at 200 Hz. Using fourcustom-built algorithms hoof-on/off timing over the force plate andstance duration were calculated for each trial from the IMU data.Accuracy of the computed parameters was calculated as the meandifference in milliseco nds between the IMU/MoCap generated data andthe data from the force plate (bias) and precision as the standarddeviation of these differences.Results: The best performing IMU algorithm achieved for stanceduration at walk an accuracy, precision and percentage of error of28.5 ms/31.6 ms/3.7% for the forelimbs and 5.5 ms/20.1 ms/2.2% forthe hindlimbs respectively. At trot the best performing algorithmachieved an accuracy, precision and percentage of error of 27.6 ms/8.8 ms/8.4% for the forelimbs and 6.3 ms/33.5 ms/9.1% for thehindlimbs. Using IMU algorithms for hoof-off detection a better overallaccuracy and precision was obtained, than when using MoCap.Conclusions: The observed IMU validation performance appears verypromising, and seems ready to practically and accurately determineimportant stride variables at walk and also at trot, being the key gait forlameness assessment and training evaluation.Ethical animal research: This study was approved by the local ethicscommittee. Horses were owned by the University. Sources of funding:This study was funded by STW Valorisation Grant 13448. Competinginterests: None declared.

An on-ice measurement approach to analyse the biomechanics of ice hockey skating.

Skating is a fundamental movement in ice hockey; however little research has been conducted within the field of hockey skating biomechanics due to the difficulties of on-ice data collection. In this study a novel on-ice measurement approach was tested for reliability, and subsequently implemented to investigate the forward skating technique, as well as technique differences across skill levels. Nine high caliber (High) and nine low caliber (Low) hockey players performed 30m forward skating trials. A 3D accelerometer was mounted to the right skate for the purpose of stride detection, with the 2nd and 6th strides defined as acceleration and steady-state, respectively. The activity of five lower extremity muscles was recorded using surface electromyography. Biaxial electro-goniometers were used to quantify hip and knee angles, and in-skate plantar force was measured using instrumented insoles. Reliability was assessed with the coefficient of multiple correlation, which demonstrated moderate (r>0.65) to excellent (r>0.95) scores across selected measured variables. Greater plantar-flexor muscle activity and hip extension were evident during acceleration strides, while steady state strides exhibited greater knee extensor activity and hip abduction range of motion (p<0.05). High caliber exhibited greater hip range of motion and forefoot force application (p<0.05). The successful implementation of this on-ice mobile measurement approach offers potential for athlete monitoring, biofeedback and training advice.

Use of an Inertial/Magnetic Sensor Module for Pedestrian Tracking During Normal Walking

The ability to track pedestrians without any infrastructure support is required by numerous applications in the healthcare, augmented reality, and entertainment industries. In this paper, we present a simple self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module. Traditional methods normally incorporate double integration of the measured acceleration, but such methods are susceptible to the acceleration noise and integration drift. To avoid this issue, alternative approaches which make use of walking dynamics to aggregate individual stride have been explored. The key for stride aggregating is to accurately and reliably detect stride boundary and estimate the associated heading direction for each stride, but it is still not well solved yet due to sensor noise and external disturbance. In this paper, we propose to make use of the inertial sensor and magnetometer measurements for stride detection and heading direction determination. In our method, a simple and reliable stride detection method, which is resilient to random bouncing motions and sensor noise, is designed based on gyroscope and accelerometer measurements. Heading direction is then determined from the foot's orientation which fuses all the three types of sensor information together. The proposed pedestrian tracking method has been evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors. The good experimental results have illustrated the effectiveness of the proposed pedestrian tracking method.

Gait and balance analysis for patients with Alzheimer's disease using an inertial-sensor-based wearable instrument.

Despite patients with Alzheimer's disease (AD) were reported of revealing gait disorders and balance problems, there is still lack of objective quantitative measurement of gait patterns and balance capability of AD patients. Based on an inertial-sensor-based wearable device, this paper develops gait and balance analyzing algorithms to obtain quantitative measurements and explores the essential indicators from the measurements for AD diagnosis. The gait analyzing algorithm is composed of stride detection followed by gait cycle decomposition so that gait parameters are developed from the decomposed gait details. On the other hand, the balance is measured by the sway speed in anterior-posterior (AP) and medial-lateral (ML) directions of the projection path of body's center of mass (COM). These devised gait and balance parameters were explored on twenty-one AD patients and fifty healthy controls (HCs). Special evaluation procedure including single-task and dual-task walking experiments for observing the cognitive function and attention is also devised for the comparison of AD and HC groups. Experimental results show that the wearable instrument with the designed gait and balance analyzing system is a promising tool for automatically analyzing gait information and balance ability, serving as assistant indicators for early diagnosis of AD.

Alzheimer’s patient activity assessment using different sensors

Purpose: Older people population is expected to grow dramatically over the next 20 years (including Alzheimer's patients), while the number of people able to provide care will decrease. We present the development of medical and information and communication technologies to support the diagnosis and evaluation of dementia progress in early stage Alzheimer disease (AD) patients.Method: We compared video and accelerometers activity assessment for the estimation of older people performance in instrumental activities of daily living (IADL) and physical tests in the clinical protocol developed by the Memory Center of the Nice Hospital and the Department of Neurology at National Cheng Kung University Hospital - Taiwan. This clinical protocol defines a set of IADLs (e.g., preparing coffee, watching TV) that could provide objective information about dementia symptoms and be realistically achieved in the two sites observation room. Previous works studied accelerometers activity assessment for the detection of changes in older people gait patterns caused by dementia progress, or video-based event detection for personal self-care activities (ADLs)[1, 2, 3], but none has used both sensors for IADLs analysis. The proposed system uses a constraint-based ontology to model and detect events based on different sensors readings (e.g., 2D video stream data is converted to 3D geometric information that is combined with a priori semantic information, like defined spatial zones or posture estimations given by accelerometer). The ontology language is declarative and intuitive (as it uses natural terminology), allowing medical experts to define and modify the IADL models. The proposed system was tested with 44 participants (healthy=21, AD=23). A stride detection algorithm was developed by the Taiwanese team for the automatic acquisition of patients gait parameters (e.g., stride length, stride frequency) using a tri-axial accelerometer embedded in a wearable device. It was tested with 33 participants (healthy=17, Alzheimer = 16) during a 40 meters walking test. Results & Discussion: The proposed system detected the full set of activities of the first part of our clinical protocol (e.g., repeated transfer test, walking test) with a true positive rate of 96.9 % to 100%. Extracted gait parameters and automatically detected IADLs will be future analyzed for the evaluation of differences between Alzheimer patients at mild to moderate stages and healthy control participants, and for the monitoring of patients motor and cognitive abilities.

Comparison of EMG-based and Accelerometer-based Speed Estimation Methods in Pedestrian Dead Reckoning

In low-cost self-contained pedestrian navigation systems, traditional Pedestrian Dead Reckoning (PDR) solutions utilize accelerometers to derive the speed as well as the distance travelled, and obtain the walking heading from magnetic compasses or gyros. However, these measurements are sensitive to instrument errors and disturbances from ambient environment. To be totally different from these signals in nature, the electromyography (EMG) signal is a typical kind of biomedical signal that measures electrical potentials generated by muscle contractions from the human body. This kind of signal would reflect muscle activities during human locomotion, so that it can not only be used for speed estimation, but also disclose the azimuth information from the contractions of lumbar muscles when changing the direction of walking. Therefore, investigating how to utilize the EMG signal for PDR is interesting and promising. In this paper, a novel EMG-based speed estimation method is presented, including setup of the EMG equipment, pre-processing procedure, stride detection and stride length estimation. Furthermore, this method suggested is compared with the traditional one based on accelerometers by means of several field tests. The results demonstrate that the EMG-based method is effective and its performance in PDR can be comparable to that of the accelerometer-based method.