A Full-Process, Fine-Grained, and Quantitative Rehabilitation Assessment Platform Enabled by On-Skin Sensors and Multi-Task Gait Transformer Model

Rehabilitation of patients with lower limb movement disorders is a gradual process, which requires full-process assessments to guide the implementation of rehabilitation plans. However, the current methods can only complete the assessment in one stage and lack objective and quantitative assessment strategies. Here, a full-process, fine-grained, and quantitative rehabilitation assessments platform (RAP) supported by on-skin sensors and a multi-task gait transformer (MG-former) model for patients with lower limb movement disorders is developed. The signal quality and sensitivity of on-skin sensor is improved by the synthesis of high-performance triboelectric material and structure design. The MG-former model can simultaneously perform multiple tasks including binary classification, multiclassification, and regression, corresponding to assessment of fall risk, walking ability, and rehabilitation progress, covering the whole rehabilitation cycle. The RAP can assess the walking ability of 23 hemiplegic patients, which has highly consistent results with the scores by the experienced physician. Furthermore, the MG-former model outputs fine-grained assessment results when performing regression task to track slight progress of patients that cannot be captured by conventional scales, facilitating adjustment of rehabilitation plans. This work provides an objective and quantitative platform, which is instructive for physicians and patients to implement effective strategy throughout the whole rehabilitation process.

AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.

Machine learning-based gait adaptation dysfunction identification using CMill-based gait data

Background

Combining machine learning (ML) with gait analysis is widely applicable for diagnosing abnormal gait patterns.

Objective

To analyze gait adaptability characteristics in stroke patients, develop ML models to identify individuals with GAD, and select optimal diagnostic models and key classification features.

Methods

This study was investigated with 30 stroke patients (mean age 42.69 years, 60% male) and 50 healthy adults (mean age 41.34 years, 58% male). Gait adaptability was assessed using a CMill treadmill on gait adaptation tasks: target stepping, slalom walking, obstacle avoidance, and speed adaptation. The preliminary analysis of variables in both groups was conducted using t-tests and Pearson correlation. Features were extracted from demographics, gait kinematics, and gait adaptability datasets. ML models based on Support Vector Machine, Decision Tree, Multi-layer Perceptron, K-Nearest Neighbors, and AdaCost algorithm were trained to classify individuals with and without GAD. Model performance was evaluated using accuracy (ACC), sensitivity (SEN), F1-score and the area under the receiver operating characteristic (ROC) curve (AUC).

Results

The stroke group showed a significantly decreased gait speed (p = 0.000) and step length (SL) (p = 0.000), while the asymmetry of SL (p = 0.000) and ST (p = 0.000) was higher compared to the healthy group. The gait adaptation tasks significantly decreased in slalom walking (p = 0.000), obstacle avoidance (p = 0.000), and speed adaptation (p = 0.000). Gait speed (p = 0.000) and obstacle avoidance (p = 0.000) were significantly correlated with global F-A score in stroke patients. The AdaCost demonstrated better classification performance with an ACC of 0.85, SEN of 0.80, F1-score of 0.77, and ROC-AUC of 0.75. Obstacle avoidance and gait speed were identified as critical features in this model.

Conclusion

Stroke patients walk slower with shorter SL and more asymmetry of SL and ST. Their gait adaptability was decreased, particularly in obstacle avoidance and speed adaptation. The faster gait speed and better obstacle avoidance were correlated with better functional mobility. The AdaCost identifies individuals with GAD and facilitates clinical decision-making. This advances the future development of user-friendly interfaces and computer-aided diagnosis systems.

Machine learning applied to gait analysis data in cerebral palsy and stroke: A systematic review

BACKGROUND

Among neurological pathologies, cerebral palsy and stroke are the main contributors to walking disorders. Machine learning methods have been proposed in the recent literature to analyze gait data from these patients. However, machine learning methods still fail to translate effectively into clinical applications. This systematic review addressed the gaps hindering the use of machine learning data analysis in the clinical assessment of cerebral palsy and stroke patients.

RESEARCH QUESTION

What are the main challenges in transferring proposed machine learning methods to clinical applications?

METHODS

PubMed, Web of Science, Scopus, and IEEE databases were searched for relevant publications on machine learning methods applied to gait analysis data from stroke and cerebral palsy patients until February the 23rd, 2023. Information related to the suitability, feasibility, and reliability of the proposed methods for their effective translation to clinical use was extracted, and quality was assessed based on a set of predefined questions.

RESULTS

From 4120 resulting references, 63 met the inclusion criteria. Thirty-one studies used supervised, and 32 used unsupervised machine learning methods. Artificial neural networks and k-means clustering were the most used methods in each category. The lack of rationale for features and algorithm selection, the use of unrepresentative datasets, and the lack of clinical interpretability of the clustering outputs were the main factors hindering the clinical reliability and applicability of these methods.

SIGNIFICANCE

The literature offers numerous machine learning methods for clustering gait data from cerebral palsy and stroke patients. However, the clinical significance of the proposed methods is still lacking, limiting their translation to real-world applications. The design of future studies must take into account clinical question, dataset significance, feature and model selection, and interpretability of the results, given their criticality for clinical translation.

A multimodal framework based on deep belief network for human locomotion intent prediction

Accurate prediction of human locomotion intent benefits the seamless switching of lower limb exoskeleton controllers in different terrains to assist humans in walking safely. In this paper, a deep belief network (DBN) was developed to construct a multimodal framework for recognizing various locomotion modes and predicting transition tasks. Three fusion strategies (data level, feature level, and decision level) were explored, and optimal network performance was obtained. This method could be tested on public datasets. For the continuous performance of steady state, the best prediction accuracy achieved was 97.64% in user-dependent testing and 96.80% in user-independent testing. During the transition state, the system accurately predicted all transitions (user-dependent: 96.37%, user-independent: 95.01%). The multimodal framework based on DBN can accurately predict the human locomotion intent. The experimental results demonstrate the potential of the proposed model in the volition control of the lower limb exoskeleton.

Systematic review of automatic post-stroke gait classification systems

BACKGROUND

Gait classification is a clinically helpful task performed after a stroke in order to guide rehabilitation therapy. Gait disorders are commonly identified using observational gait analysis in clinical settings, but this approach is limited due to low reliability and accuracy. Data-driven gait classification can quantify gait deviations and categorise gait patterns automatically possibly improving reliability and accuracy; however, the development and clinical utility of current data driven systems has not been reviewed previously.

RESEARCH QUESTION

The purpose of this systematic review is to evaluate the literature surrounding the methodology used to develop automatic gait classification systems, and their potential effectiveness in the clinical management of stroke-affected gait.

METHOD

The database search included PubMed, IEEE Xplore, and Scopus. Twenty-one studies were identified through inclusion and exclusion criteria from 407 available studies published between 2015 and 2022. Development methodology, classification performance, and clinical utility information were extracted for review.

RESULTS AND SIGNIFICANCE

Most of gait classification systems reported a classification accuracy between 80%-100%. However, collated studies presented methodological errors in machine learning (ML) model development. Further, many studies neglected model components such as clinical utility (e.g., predictions don't assist clinicians or therapists in making decisions, interpretability, and generalisability). We provided recommendations to guide development of future post-stroke automatic gait classification systems to better assist clinicians and therapists. Future automatic gait classification systems should emphasise the clinical significance and adopt a standardised development methodology of ML model.

Interpreting Stroke-Impaired Electromyography Patterns through Explainable Artificial Intelligence

Electromyography (EMG) proves invaluable myoelectric manifestation in identifying neuromuscular alterations resulting from ischemic strokes, serving as a potential marker for diagnostics of gait impairments caused by ischemia. This study aims to develop an interpretable machine learning (ML) framework capable of distinguishing between the myoelectric patterns of stroke patients and those of healthy individuals through Explainable Artificial Intelligence (XAI) techniques. The research included 48 stroke patients (average age 70.6 years, 65% male) undergoing treatment at a rehabilitation center, alongside 75 healthy adults (average age 76.3 years, 32% male) as the control group. EMG signals were recorded from wearable devices positioned on the bicep femoris and lateral gastrocnemius muscles of both lower limbs during indoor ground walking in a gait laboratory. Boosting ML techniques were deployed to identify stroke-related gait impairments using EMG gait features. Furthermore, we employed XAI techniques, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Anchors to interpret the role of EMG variables in the stroke-prediction models. Among the ML models assessed, the GBoost model demonstrated the highest classification performance (AUROC: 0.94) during cross-validation with the training dataset, and it also overperformed (AUROC: 0.92, accuracy: 85.26%) when evaluated using the testing EMG dataset. Through SHAP and LIME analyses, the study identified that EMG spectral features contributing to distinguishing the stroke group from the control group were associated with the right bicep femoris and lateral gastrocnemius muscles. This interpretable EMG-based stroke prediction model holds promise as an objective tool for predicting post-stroke gait impairments. Its potential application could greatly assist in managing post-stroke rehabilitation by providing reliable EMG biomarkers and address potential gait impairment in individuals recovering from ischemic stroke.

Musculoskeletal Rehabilitation: New Perspectives in Postoperative Care Following Total Knee Arthroplasty Using an External Motion Sensor and a Smartphone Application for Remote Monitoring

BACKGROUND

The number of total knee replacements performed annually is steadily increasing. Parallel options for postoperative care are decreasing, which reduces patient satisfaction. External devices to support physical rehabilitation and health monitoring will improve patient satisfaction and postoperative care.

METHODS

In a prospective, international multicenter study, patients were asked to use an external motion sensor and a smartphone application during the postoperative course of primary total knee arthroplasty. The collected data were transferred to a data platform, allowing for the real-time evaluation of patient data.

RESULTS

In three participating centers, 98 patients were included. The general acceptance of using the sensor and app was high, with an overall compliance in study participation rate of up to 76%. The early results showed a significant improvement in the overall quality of life (p < 0.001) and significant reductions in pain (p < 0.01) and depression (p < 0.001).

CONCLUSIONS

The early results of this clinical and multicenter study emphasize that there is a high interest in and acceptance of digital solutions in patients' treatment pathways. Motion sensor and smartphone applications support patients in early rehabilitation.

GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model

The detection of a fallen person (FPD) is a crucial task in guaranteeing individual safety. Although deep-learning models have shown potential in addressing this challenge, they face several obstacles, such as the inadequate utilization of global contextual information, poor feature extraction, and substantial computational requirements. These limitations have led to low detection accuracy, poor generalization, and slow inference speeds. To overcome these challenges, the present study proposed a new lightweight detection model named Global and Local You-Only-Look-Once Lite (GL-YOLO-Lite), which integrates both global and local contextual information by incorporating transformer and attention modules into the popular object-detection framework YOLOv5. Specifically, a stem module replaced the original inefficient focus module, and rep modules with re-parameterization technology were introduced. Furthermore, a lightweight detection head was developed to reduce the number of redundant channels in the model. Finally, we constructed a large-scale, well-formatted FPD dataset (FPDD). The proposed model employed a binary cross-entropy (BCE) function to calculate the classification and confidence losses. An experimental evaluation of the FPDD and Pascal VOC dataset demonstrated that GL-YOLO-Lite outperformed other state-of-the-art models with significant margins, achieving 2.4-18.9 mean average precision (mAP) on FPDD and 1.8-23.3 on the Pascal VOC dataset. Moreover, GL-YOLO-Lite maintained a real-time processing speed of 56.82 frames per second (FPS) on a Titan Xp and 16.45 FPS on a HiSilicon Kirin 980, demonstrating its effectiveness in real-world scenarios.

A Survey of Human Gait-Based Artificial Intelligence Applications

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals' daily activities, providing information that is reflective of an individual's health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual's health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual's health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.

Evaluation of Muscle Function by Means of a Muscle-Specific and a Global Index

Gait analysis applications in clinics are still uncommon, for three main reasons: (1) the considerable time needed to prepare the subject for the examination; (2) the lack of user-independent tools; (3) the large variability of muscle activation patterns observed in healthy and pathological subjects. Numerical indices quantifying the muscle coordination of a subject could enable clinicians to identify patterns that deviate from those of a reference population and to follow the progress of the subject after surgery or completing a rehabilitation program. In this work, we present two user-independent indices. First, a muscle-specific index (MFI) that quantifies the similarity of the activation pattern of a muscle of a specific subject with that of a reference population. Second, a global index (GFI) that provides a score of the overall activation of a muscle set. These two indices were tested on two groups of healthy and pathological children with encouraging results. Hence, the two indices will allow clinicians to assess the muscle activation, identifying muscles showing an abnormal activation pattern, and associate a functional score to every single muscle as well as to the entire muscle set. These opportunities could contribute to facilitating the diffusion of surface EMG analysis in clinics.

Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation

Background

For rehabilitation training systems, it is essential to automatically record and recognize exercises, especially when more than one type of exercise is performed without a predefined sequence. Most motion recognition methods are based on feature engineering and machine learning algorithms. Time-domain and frequency-domain features are extracted from original time series data collected by sensor nodes. For high-dimensional data, feature selection plays an important role in improving the performance of motion recognition. Existing feature selection methods can be categorized into filter and wrapper methods. Wrapper methods usually achieve better performance than filter methods; however, in most cases, they are computationally intensive, and the feature subset obtained is usually optimized only for the specific learning algorithm.

Objective

This study aimed to provide a feature selection method for motion recognition of upper-limb exercises and improve the recognition performance.

Methods

Motion data from 5 types of upper-limb exercises performed by 21 participants were collected by a customized inertial measurement unit (IMU) node. A total of 60 time-domain and frequency-domain features were extracted from the original sensor data. A hybrid feature selection method by combining filter and wrapper methods (FESCOM) was proposed to eliminate irrelevant features for motion recognition of upper-limb exercises. In the filter stage, candidate features were first selected from the original feature set according to the significance for motion recognition. In the wrapper stage, k-nearest neighbors (kNN), Naïve Bayes (NB), and random forest (RF) were evaluated as the wrapping components to further refine the features from the candidate feature set. The performance of the proposed FESCOM method was verified using experiments on motion recognition of upper-limb exercises and compared with the traditional wrapper method.

Results

Using kNN, NB, and RF as the wrapping components, the classification error rates of the proposed FESCOM method were 1.7%, 8.9%, and 7.4%, respectively, and the feature selection time in each iteration was 13 seconds, 71 seconds, and 541 seconds, respectively.

Conclusions

The experimental results demonstrated that, in the case of 5 motion types performed by 21 healthy participants, the proposed FESCOM method using kNN and NB as the wrapping components achieved better recognition performance than the traditional wrapper method. The FESCOM method dramatically reduces the search time in the feature selection process. The results also demonstrated that the optimal number of features depends on the classifier. This approach serves to improve feature selection and classification algorithm selection for upper-limb motion recognition based on wearable sensor data, which can be extended to motion recognition of more motion types and participants.

Prediction of Myoelectric Biomarkers in Post-Stroke Gait

Electromyography (EMG) is sensitive to neuromuscular changes resulting from ischemic stroke and is considered a potential predictive tool of post-stroke gait and rehabilitation management. This study aimed to evaluate the potential myoelectric biomarkers for the classification of stroke-impaired muscular activity of the stroke patient group and the muscular activity of the control healthy adult group. We also proposed an EMG-based gait monitoring system consisting of a portable EMG device, cloud-based data processing, data analytics, and a health advisor service. This system was investigated with 48 stroke patients (mean age 70.6 years, 65% male) admitted into the emergency unit of a hospital and 75 healthy elderly volunteers (mean age 76.3 years, 32% male). EMG was recorded during walking using the portable device at two muscle positions: the bicep femoris muscle and the lateral gastrocnemius muscle of both lower limbs. The statistical result showed that the mean power frequency (MNF), median power frequency (MDF), peak power frequency (PKF), and mean power (MNP) of the stroke group differed significantly from those of the healthy control group. In the machine learning analysis, the neural network model showed the highest classification performance (precision: 88%, specificity: 89%, accuracy: 80%) using the training dataset and highest classification performance (precision: 72%, specificity: 74%, accuracy: 65%) using the testing dataset. This study will be helpful to understand stroke-impaired gait changes and decide post-stroke rehabilitation.

Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept

Introduction: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. Methods: This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. Results: Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. Conclusion: The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation.

Changes in Balance, Gait and Electroencephalography Oscillations after Robot-Assisted Gait Training: An Exploratory Study in People with Chronic Stroke

Robot-assisted gait training (RAGT) systems offer the advantages of standard rehabilitation and provide precise and quantifiable control of therapy. We examined the clinical outcome of RAGT and analyzed the correlations between gait analysis data and event-related desynchronization (ERD) and event-related synchronization (ERS) in patients with chronic stroke. We applied the Berg balance scale (BBS) and analyzed gait parameters and the ERD and ERS of self-paced voluntary leg movements performed by patients with chronic stroke before and after undergoing RAGT. A significant change was observed in BBS (p = 0.011). We also showed preliminary outcomes of changes in gait cycle duration (p = 0.015) and in ipsilesional ERS in the low-beta (p = 0.033) and high-beta (p = 0.034) frequency bands before and after RAGT. In addition, correlations were observed between BBS and ipsilesional ERS in the alpha and low-beta bands (r = −0.52, p = 0.039; r = −0.52, p = 0.040). The study demonstrated that RAGT can improve balance and provided an idea of the possible role of brain oscillation and clinical outcomes in affecting stroke rehabilitation.

Role of machine learning in gait analysis: a review

Human biomechanics and gait form an integral part of life. The gait analysis involves a large number of interdependent parameters that were difficult to interpret due to a vast amount of data and their inter-relations. To simplify evaluation, the integration of machine learning (ML) with biomechanics is a promising solution. The purpose of this review is to familiarise the readers with key directions of implementation of ML techniques for gait analysis and gait rehabilitation. An extensive literature survey was based on research articles from nine databases published from 1980 to 2019. With over 943 studies identified, finally, 43 studies met the inclusion criteria. The outcome reported illustrates that supervised ML techniques showed accuracies above 90% in the identified gait analysis domain. The statistical results revealed support vector machine (SVM) as the best classifier (mean-score = 0.87 ± 0.07) with remarkable generalisation capability even on small to medium datasets. It has also been analysed that the control strategies for gait rehabilitation are benefitted from reinforcement learning and (deep) neural-networks due to their ability to capture participants' variability. This review paper shows the success of ML techniques in detecting disorders, predicting rehabilitation length, and control of rehabilitation devices which make them suitable for clinical diagnosis.

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review

The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

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.

Appropriate Mother Wavelets for Continuous Gait Event Detection Based on Time-Frequency Analysis for Hemiplegic and Healthy Individuals

Gait event detection is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. Recently, the continuous wavelet transform (CWT) based methods have been increasingly proposed for gait event detection due to their robustness. However, few investigations on determining the appropriate mother wavelet with proper selection criteria have been performed, especially for hemiplegic patients. In this study, the performances of commonly used mother wavelets in detecting gait events were systematically investigated. The acceleration signals from the tibialis anterior muscle of both healthy and hemiplegic subjects were recorded during ground walking and the two core gait events of heel strike (HS) and toe off (TO) were detected from the signal recordings by a CWT algorithm with different mother wavelets. Our results showed that the overall performance of the CWT algorithm in detecting the two gait events was significantly different when using various mother wavelets. By using different wavelet selection criteria, we also found that the accuracy criteria based on time-error minimization and F1-score maximization could provide the appropriate mother wavelet for gait event detection. The findings from this study will provide an insight on the selection of an appropriate mother wavelet for gait event detection and facilitate the development of adequate rehabilitation aids.