A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022

Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.

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

Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems. To date, their reliability and limitations in manual labeling of gait events have not been studied.

Objectives

Evaluate manual labeling uncertainty and introduce a hybrid stride detection and gait-event estimation model for autonomous, long-term, and remote monitoring.

Methods

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.

Results

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.

Conclusions

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.

Significance

This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.

Real-Time Gait Phase Detection Using Wearable Sensors for Transtibial Prosthesis Based on a kNN Algorithm

Those with disabilities who have lost their legs must use a prosthesis to walk. However, traditional prostheses have the disadvantage of being unable to move and support the human gait because there are no mechanisms or algorithms to control them. This makes it difficult for the wearer to walk. To overcome this problem, we developed an insole device with a wearable sensor for real-time gait phase detection based on the kNN (k-nearest neighbor) algorithm for prosthetic control. The kNN algorithm is used with the raw data obtained from the pressure sensors in the insole to predict seven walking phases, i.e., stand, heel strike, foot flat, midstance, heel off, toe-off, and swing. As a result, the predictive decision in each gait cycle to control the ankle movement of the transtibial prosthesis improves with each walk. The results in this study can provide 81.43% accuracy for gait phase detection, and can control the transtibial prosthetic effectively at the maximum walking speed of 6 km/h. Moreover, this insole device is small, lightweight and unaffected by the physical factors of the wearer.

Gait phase detection based on inertial measurement unit and force-sensitive resistors embedded in a shoe

This study proposes a system to detect the phases of gait. It consists of an intelligent shoe equipped with an inertial measurement unit (IMU) and force-sensitive resistors (FSRs), and it uses a compound method to recognize gait. The continuous wavelet transform is applied according to accelerations obtained via the IMU to identify heel strike and toe-off events. These events are used to calculate the pressure threshold and proportional factor via the Lopez-Meyer (LM) method by using minimal leave-one-out for training and validation. The LM method can identify the entire sub-phase of the stance of the gait based on ground contact forces measured by using the FSRs and rules of gait event detection. The proposed system was tested on five healthy volunteers who used the intelligent shoe. The results show that it can detect all sub-phases of the gait with an overall accuracy (96%) higher than the LM method. The proportional factor was adaptable to variable body weights, and the reported average errors of competing systems in the literature significantly exceeded the average variation of the proposed system for all phases of gait. The range of errors in the swing phase and sub-phases of stance was also acceptable for application purposes. When the size of the subject's foot was close to that of the intelligent shoe, the error between normative data and phases of gait identified by the detection system was minimal. Furthermore, the proposed system detected abnormalities in the gait circle, and thus, it can be used to monitor the walking activity and measure the motor recovery.

A type-2 fuzzy index to assess high heeled gait deviations using spatial-temporal parameters

This paper introduces an intelligent index that numerically assesses high-heeled gait deviations. Experiments were conducted on 14 young female volunteers, and the spatial-temporal gait parameters were calculated at each heel height. A type-2 fuzzy system index was built using the baseline case (barefoot). The index showed sensitivity to heel height changes. Moreover, its values divided the heel heights used in this study into three groups, depending on their effect on the gait parameters. A high correlation between the proposed index and the gait profile score (GPS) was found, this supports the index validity to evaluate different human gait deviations.

Optical-Based Foot Plantar Pressure Measurement System for Potential Application in Human Postural Control Measurement and Person Identification

Plantar pressure, the pressure exerted between the sole and the supporting surface, has great potentialities in various research fields, including footwear design, biometrics, gait analysis and the assessment of patients with diabetes. This research designs an optical-based foot plantar pressure measurement system aimed for human postural control and person identification. The proposed system consists of digital cameras installed underneath an acrylic plate covered by glossy white paper and mounted with LED strips along the side of the plate. When the light is emitted from the LED stripes, it deflects the digital cameras due to the pressure exerted between the glossy white paper and the acrylic plate. In this way, the cameras generate color-coded plantar pressure images of the subject standing on the acrylic-top platform. Our proposed system performs personal identification and postural control by extracting static and dynamic features from the generated plantar pressure images. Plantar pressure images were collected from 90 individuals (40 males, 50 females) to develop and evaluate the proposed system. In posture balance evaluation, we propose the use of a posture balance index that contains both magnitude and directional information about human posture balance control. For person identification, the experimental results show that our proposed system can achieve promising results, showing an area under the receiver operating characteristic (ROC) curve of 0.98515 (98.515%), an equal error rate (EER) of 5.8687%, and efficiency of 98.515%.

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

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

An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based 'Gold Standard'

Video- and sensor-based gait analysis systems are rapidly emerging for use in ‘real world’ scenarios outside of typical instrumented motion analysis laboratories. Unlike laboratory systems, such systems do not use kinetic data from force plates, rather, gait events such as initial contact (IC) and terminal contact (TC) are estimated from video and sensor signals. There are, however, detection errors inherent in kinematic gait event detection methods (GEDM) and comparative study between classic laboratory and video/sensor-based systems is warranted. For this study, three kinematic methods: coordinate based treadmill algorithm (CBTA), shank angular velocity (SK), and foot velocity algorithm (FVA) were compared to ‘gold standard’ force plate methods (GS) for determining IC and TC in adults (n = 6), typically developing children (n = 5) and children with cerebral palsy (n = 6). The root mean square error (RMSE) values for CBTA, SK, and FVA were 27.22, 47.33, and 78.41 ms, respectively. On average, GED was detected earlier in CBTA and SK (CBTA: −9.54 ± 0.66 ms, SK: −33.41 ± 0.86 ms) and delayed in FVA (21.00 ± 1.96 ms). The statistical model demonstrated insensitivity to variations in group, side, and individuals. Out of three kinematic GEDMs, SK GEDM can best be used for sensor-based gait event detection.

A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses

Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.

Recognition of Gait Phases with a Single Knee Electrogoniometer: A Deep Learning Approach

Artificial neural networks were satisfactorily implemented for assessing gait events from different walking data. This study aims to propose a novel approach for recognizing gait phases and events, based on deep-learning analysis of only sagittal knee-joint angle measured by a single electrogoniometer per leg. Promising classification/prediction performances have been previously achieved by surface-EMG studies; thus, a further aim is to test if adding electrogoniometer data could improve classification performances of state-of-the-art methods. Gait data are measured in about 10,000 strides from 23 healthy adults, during ground walking. A multi-layer perceptron model is implemented, composed of three hidden layers and a one-dimensional output. Classification/prediction accuracy is tested vs. ground truth represented by foot–floor-contact signals, through samples acquired from subjects not seen during training phase. Average classification-accuracy of 90.6 ± 2.9% and mean absolute value (MAE) of 29.4 ± 13.7 and 99.5 ± 28.9 ms in assessing heel-strike and toe-off timing are achieved in unseen subjects. Improvement of classification-accuracy (four points) and reduction of MAE (at least 35%) are achieved when knee-angle data are used to enhance sEMG-data prediction. Comparison of the two approaches shows as the reduction of set-up complexity implies a worsening of mainly toe-off prediction. Thus, the present electrogoniometer approach is particularly suitable for the classification tasks where only heel-strike event is involved, such as stride recognition, stride-time computation, and identification of toe walking.

Real-Time Detection of Seven Phases of Gait in Children with Cerebral Palsy Using Two Gyroscopes

A recently designed gait phase detection (GPD) system, with the ability to detect all seven phases of gait in healthy adults, was modified for GPD in children with cerebral palsy (CP). A shank-attached gyroscope sent angular velocity to a rule-based algorithm in LabVIEW to identify the distinct characteristics of the signal. Seven typically developing children (TD) and five children with CP were asked to walk on treadmill at their self-selected speed while using this system. Using only shank angular velocity, all seven phases of gait (Loading Response, Mid-Stance, Terminal Stance, Pre-Swing, Initial Swing, Mid-Swing and Terminal Swing) were reliably detected in real time. System performance was validated against two established GPD methods: (1) force-sensing resistors (GPD-FSR) (for typically developing children) and (2) motion capture (GPD-MoCap) (for both typically developing children and children with CP). The system detected over 99% of the phases identified by GPD-FSR and GPD-MoCap. Absolute values of average gait phase onset detection deviations relative to GPD-MoCap were less than 100 ms for both TD children and children with CP. The newly designed system, with minimized sensor setup and low processing burden, is cosmetic and economical, making it a viable solution for real-time stand-alone and portable applications such as triggering functional electrical stimulation (FES) in rehabilitation systems. This paper verifies the applicability of the GPD system to identify specific gait events for triggering FES to enhance gait in children with CP.

Establishment of vestibular function multimodality platform

BACKGROUND

The technology of using inertial measurement units (IMUs) to detect motions in different body segments has drawn enormous attention to research and industry. In our previous research, we have applied IMUs in evaluating and treating patients with vestibular hypofunction. Furthermore, according to the research, when a person's head rotates over 60° on either side in the horizontal plane, and desires to focus vision on any targets, then the function of gaze shift comes in to operation. Herein, we aimed to use IMUs to build up a system to evaluate vestibular ocular reflex (VOR) during gaze shifting maneuver.

METHODS

In this study, we developed a platform, which combines the features of gaze shift and computerized dynamic visual acuity (cDVA), called the gaze shift DVA (gsDVA) platform. The gsDVA platform measures the orientations of the subject's head by IMU, and executed the evaluation according to the algorithm that was developed by us. Finally, we used the VICON system to validate the performance of gsDVA platform.

RESULTS

The performance of the accuracy was 2.41° ± 1.08°, the maximal sensor error was within 4.25°, and highly correlated between our platform and VICON (p < 0.05, R = 0.99). The intraclass correlation coefficient (ICC) of between-day and within-day was 0.984 and 0.999, respectively. Furthermore, the platform not only executed the evaluation automatically but also recorded other information besides the head orientation, such as rotation speed, rotation time, reaction time, and visual acuity.

CONCLUSION

In this study, we demonstrated the utility of vestibular evaluation, and this platform can help to clarify the relationship between gaze shift and VOR. This methodology is useful and can be applied efficiently to different disease groups for interactive evaluation and rehabilitation programs.

Seven phases of gait detected in real-time using shank attached gyroscopes

A new gyroscope-based gait phase detection system (GPDS) with ability to detect all seven phases of gait was proposed in this study. Gyroscopes were attached to each shank. Shank angular velocity, about the medio-lateral axis, was streamed to a PC and a rule-based algorithm was used to identify characteristics of the signals. Five subjects were asked to walk on treadmill at their self-selected speed while using this system. All 7 phases of gait: LR, MSt, TSt, PSw, ISw, MSw, and TSw were detected in real-time using only shank angular velocities. To quantify system performance, sensor data was compared to simultaneously collected motion capture data. Average gait phase detection delays of the system were less than 40ms except TSw (74ms). The present system, consisting of minimal sensors and decreased processing, is precise, cosmetic, economical, and a good alternative for portable stand-alone applications.

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.

New Kinematic Parameters for Quantifying Irregularities in the Human and Humanoid Robot Gait

Gait patterns of humans and humanoid robots are often described by analysing changes in angular rotation of hip, knee and ankle joints during one gait cycle. Each joint displays specific behaviour and irregularities of the gait pattern could be detected by measuring displacements from the normal rotation curve, while small deviations of individual gait characteristics are usually not easily detected. In this paper, an advanced gait analysis method is proposed, which incorporates analysis of angular data and its derivations of hip, knee, and ankle joints, presented in the phase plane. The gait kinematics was measured using a system based on active markers and fast digital cameras. The experiment included measurements on thirty healthy, barefoot humans while walking on a treadmill. We also simulated types of irregular gait, by measurements on subjects wearing knee constraints. The new kinematic parameters which are introduced clearly indicated the discrepancy between normal, healthy gait trials and irregular gait trials. The proposed gait factor parameter is a valuable measure for the detection of irregularities in gait patterns of humans and humanoid robots.

Quantitative method for gait pattern detection based on fiber Bragg grating sensors

This paper presents a method that uses fiber Bragg grating (FBG) sensors to distinguish the temporal gait patterns in gait cycles. Unlike most conventional methods that focus on electronic sensors to collect those physical quantities (i.e., strains, forces, pressure, displacements, velocity, and accelerations), the proposed method utilizes the backreflected peak wavelength from FBG sensors to describe the motion characteristics in human walking. Specifically, the FBG sensors are sensitive to external strain with the result that their backreflected peak wavelength will be shifted according to the extent of the influence of external strain. Therefore, when subjects walk in different gait patterns, the strains on FBG sensors will be different such that the magnitude of the backreflected peak wavelength varies. To test the reliability of the FBG sensor platform for gait pattern detection, the gold standard method using force-sensitive resistors (FSRs) for defining gait patterns is introduced as a reference platform. The reliability of the FBG sensor platform is determined by comparing the detection results between the FBG sensors and FSRs platforms. The experimental results show that the FBG sensor platform is reliable in gait pattern detection and gains high reliability when compared with the reference platform.

Computational gait analysis using fuzzy logic for everyday clinical purposes – preliminary findings

Background:

Proper, early, and exact identification of gait impairments and their causes is regarded as a prerequisite for specific therapy and a useful control tool to assess efficacy of rehabilitation. There is a need for simple tools allowing for quickly detecting general tendencies.

Objective:

The aim of this paper is to present the outcomes of traditional and fuzzy-based analysis of the outcomes of post-stroke gait reeducation using the NeuroDevelopmental Treatment-Bobath (NDT-Bobath) method.

Materials and methods:

The research was conducted among 40 adult people: 20 of them after ischemic stroke constituted the study group, and 20 healthy people constituted the reference group. Study group members were treated through 2 weeks (10 therapeutic sessions) using the NDT-Bobath method. Spatio-temporal gait parameters were assessed before and after therapy and compared using novel fuzzy-based assessment tool.

Results:

Achieved results of rehabilitation, observed as changes of gait parameters, were statistically relevant and reflected recovery. One-number outcomes from the proposed fuzzy-based estimator proved moderate to high consistency with the results of the traditional gait assessment.

Conclusions:

Observed statistically significant and favorable changes in the health status of patients, described by gait parameters, were reflected also in outcomes of fuzzy-based analysis. Proposed fuzzy-based measure increases possibility of the clinical gait assessment toward more objective clinical reasoning based on common use of the mHealth solutions.

Recognizing Parkinsonian Gait Pattern by Exploiting Fine-Grained Movement Function Features

Parkinson's disease (PD) is one of the typical movement disorder diseases among elderly people, which has a serious impact on their daily lives. In this article, we propose a novel computation framework to recognize gait patterns in patients with PD. The key idea of our approach is to distinguish gait patterns in PD patients from healthy individuals by accurately extracting gait features that capture all three aspects of movement functions, that is, stability, symmetry, and harmony. The proposed framework contains three steps: gait phase discrimination, feature extraction and selection, and pattern classification. In the first step, we put forward a sliding window--based method to discriminate four gait phases from plantar pressure data. Based on the gait phases, we extract and select gait features that characterize stability, symmetry, and harmony of movement functions. Finally, we recognize PD gait patterns by applying a hybrid classification model. We evaluate the framework using an open dataset that contains real plantar pressure data of 93 PD patients and 72 healthy individuals. Experimental results demonstrate that our framework significantly outperforms the four baseline approaches.

Gait Partitioning Methods: A Systematic Review

In the last years, gait phase partitioning has come to be a challenging research topic due to its impact on several applications related to gait technologies. A variety of sensors can be used to feed algorithms for gait phase partitioning, mainly classifiable as wearable or non-wearable. Among wearable sensors, footswitches or foot pressure insoles are generally considered as the gold standard; however, to overcome some inherent limitations of the former, inertial measurement units have become popular in recent decades. Valuable results have been achieved also though electromyography, electroneurography, and ultrasonic sensors. Non-wearable sensors, such as opto-electronic systems along with force platforms, remain the most accurate system to perform gait analysis in an indoor environment. In the present paper we identify, select, and categorize the available methodologies for gait phase detection, analyzing advantages and disadvantages of each solution. Finally, we comparatively examine the obtainable gait phase granularities, the usable computational methodologies and the optimal sensor placements on the targeted body segments.

Reliability of the step phase detection using inertial measurement units: pilot study

The use of inertial sensors for the gait event detection during a long-distance walking, for example, on different surfaces and with different walking patterns, is important to evaluate the human locomotion. Previous studies demonstrated that gyroscopes on the shank or foot are more reliable than accelerometers and magnetometers for the event detection in case of normal walking. However, these studies did not link the events with the temporal parameters used in the clinical practice; furthermore, they did not clearly verify the optimal position for the sensors depending on walking patterns and surface conditions. The event detection quality of the sensors is compared with video, used as ground truth, according to the parameters proposed by the Gait and Clinical Movement Analysis Society. Additionally, the performance of the sensor on the foot is compared with the one on the shank. The comparison is performed considering both normal walking and deviations to the walking pattern, on different ground surfaces and with or without constraints on movements. The preliminary results show that the proposed methodology allows reliable detection of gait events, even in case of abnormal footfall and in slipping surface conditions, and that the optimal location to place the sensors is the shank.

Adaptive method for real-time gait phase detection based on ground contact forces

A novel method is presented to detect real-time gait phases based on ground contact forces (GCFs) measured by force sensitive resistors (FSRs). The traditional threshold method (TM) sets a threshold to divide the GCFs into on-ground and off-ground statuses. However, TM is neither an adaptive nor real-time method. The threshold setting is based on body weight or the maximum and minimum GCFs in the gait cycles, resulting in different thresholds needed for different walking conditions. Additionally, the maximum and minimum GCFs are only obtainable after data processing. Therefore, this paper proposes a proportion method (PM) that calculates the sums and proportions of GCFs wherein the GCFs are obtained from FSRs. A gait analysis is then implemented by the proposed gait phase detection algorithm (GPDA). Finally, the PM reliability is determined by comparing the detection results between PM and TM. Experimental results demonstrate that the proposed PM is highly reliable in all walking conditions. In addition, PM could be utilized to analyze gait phases in real time. Finally, PM exhibits strong adaptability to different walking conditions.

A Novel Approach for Toe Off Estimation During Locomotion and Transitions on Ramps and Level Ground

Identification of the toe off event is critical in many gait applications. Accelerometer threshold-based algorithms lack adaptability and have not been tested for transitions between locomotion states. We describe a new approach for toe off identification using one accelerometer in over ground and ramp walking, including transitions. The method uses invariant foot acceleration features in the segment of gait, where toe off is probable. Wavelet analysis of foot acceleration is used to derive a unique feature in a particular frequency band, yielding estimated toe off occurrence. We tested the new method for five conditions: over ground walking (W), ramp ascending (RA), ramp descending (RD); transitions between states (W-RA, W-RD). Mean absolute estimation error was 17.4 ± 12.5, 13.8 ± 8.5, and 22.0 ± 16.4 ms for steady states W, RA, and RD, 20.1 ± 15.5, and 17.1 ± 13.7 ms for transitions W-RA and W-RD, respectively. Algorithm performance was equivalent across all pairs of transition and locomotion state except between RA and RD ( p = 0.03), demonstrating adaptability. The db1 wavelet outperformed db2 across states and transitions (p < 0.01). The presented algorithm is a simple, robust approach for toe off detection.

Automated detection of instantaneous gait events using time frequency analysis and manifold embedding

Accelerometry is a widely used sensing modality in human biomechanics due to its portability, non-invasiveness, and accuracy. However, difficulties lie in signal variability and interpretation in relation to biomechanical events. In walking, heel strike and toe off are primary gait events where robust and accurate detection is essential for gait-related applications. This paper describes a novel and generic event detection algorithm applicable to signals from tri-axial accelerometers placed on the foot, ankle, shank or waist. Data from healthy subjects undergoing multiple walking trials on flat and inclined, as well as smooth and tactile paving surfaces is acquired for experimentation. The benchmark timings at which heel strike and toe off occur, are determined using kinematic data recorded from a motion capture system. The algorithm extracts features from each of the acceleration signals using a continuous wavelet transform over a wide range of scales. A locality preserving embedding method is then applied to reduce the high dimensionality caused by the multiple scales while preserving salient features for classification. A simple Gaussian mixture model is then trained to classify each of the time samples into heel strike, toe off or no event categories. Results show good detection and temporal accuracies for different sensor locations and different walking terrains.

Gait Phase Detection Using Foot Acceleration for Estimating Ground Reaction Force in Long Distance Gait Rehabilitation

In gait rehabilitation, achieving a gait analysis method using a simple system during long-distance walking is important. This method is required to measure all gait parameters in a single measurement. In addition, it is required that the measurement system is not spatially constrained. Therefore, we have been developing a gait tracking system with acceleration sensors for long-distance gait rehabilitation. In this paper, we describe a gait phase detection method using foot acceleration data for estimating ground reaction force during long-distance gait rehabilitation. To develop this method, we focused on the jerk of each foot in vertical axis direction. Using two accelerometers mounted on the left and right feet, we carried out three experiments. First, we measured the jerk of each foot during a free gait to verify the relation with the walking speed. Second, we measured the jerk of each foot during walking faster than normal for each subject. We then compared these results with the results of first experiments. Finally, we measured the jerk of each foot during left-right asymmetrical walking. The results confirmed that gait phase could be detected using the jerk of each leg, calculated from acceleration data in vertical axis direction. In particular, the timing of Heel-contact / Toe-off could be obtained with an average error of 0.03 s. And as a preliminary study, we estimated the ground reaction force using the one of the results.

Human Gait Feature Extraction Including a Kinematic Analysis toward Robotic Power Assistance

The present work proposes a method for human gait and kinematic analysis. Gait analysis consists of the determination of hip, knee and ankle positions through video analysis. Gait kinematic for the thigh and knee is then generated from this data. Evaluations of the gait analysis method indicate an acceptable performance of 86.66% for hip and knee position estimation, and comparable findings with other reported works for gait kinematic. A coordinate systems assignment is performed according to the DH algorithm and a direct kinematic model of the legs is obtained. The legs' angles obtained from the video analysis are applied to the kinematic model in order to revise the application of this model to robotic legs in a power assisted system.

A wearable walking monitoring system for gait analysis

In this paper, both hardware and software design to develop a wearable walking monitoring system for gait analysis are presented. For hardware, the mechanism proposed is adaptive to different individuals to wear, and the portability of the design makes it easy to perform outdoor experiments. Four force sensors and two angle displacement sensors were used to measure plantar force distribution and the angles of hip and knee joints. For software design, a novel algorithm was developed to detect different gait phases and the four gait periods during the stance phase. Furthermore, the center of ground contact force was calculated based on the relationships of the force sensors. The results were compared with the VICON motion capture system and a force plate for validation. Experiments showed the behavior of the joint angles are similar to VICON system, and the average error in foot strike time is less than 90 ms.