Abnormal Gait Detection Research Articles

Vibration Signal Based Abnormal Gait Detection and Recognition

Vibration Signal Based Abnormal Gait Detection and Recognition

Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living: A 3D Vision Approach.

Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable for everyday environments. We explore various configurations, including multi-camera setups placed at different distances and angles, as well as performing daily activities in different directions. An integral component of our study involves combining gait analysis with the monitoring of activities of daily living (ADLs), given the paramount relevance of this integration in the context of Ambient Assisted Living. To achieve this, we investigate cutting-edge Deep Neural Network approaches, such as the Temporal Convolutional Network, Gated Recurrent Unit, and Long Short-Term Memory Autoencoder. Additionally, we scrutinize different data representation formats, including Euclidean-based representations, angular adjacency matrices, and rotation matrices. Our system's performance evaluation leverages both publicly available datasets and data we collected ourselves while accounting for individual variations and environmental factors. The results underscore the effectiveness of our proposed configurations in accurately classifying abnormal gait, thus shedding light on the optimal setup for non-invasive and efficient data collection.

Integration of force and IMU sensors for developing low-cost portable gait measurement system in lower extremities

Gait analysis is the method to accumulate walking data. It is useful in diagnosing diseases, follow-up of symptoms, and rehabilitation post-treatment. Several techniques have been developed to assess human gait. In the laboratory, gait parameters are analyzed by using a camera capture and a force plate. However, there are several limitations, such as high operating costs, the need for a laboratory and a specialist to operate the system, and long preparation time. This paper presents the development of a low-cost portable gait measurement system by using the integration of flexible force sensors and IMU sensors in outdoor applications for early detection of abnormal gait in daily living. The developed device is designed to measure ground reaction force, acceleration, angular velocity, and joint angles of the lower extremities. The commercialized device, including the motion capture system (Motive-OptiTrack) and force platform (MatScan), is used as the reference system to validate the performance of the developed system. The results of the system show that it has high accuracy in measuring gait parameters such as ground reaction force and joint angles in lower limbs. The developed device has a strong correlation coefficient compared with the commercialized system. The percent error of the motion sensor is below 8%, and the force sensor is lower than 3%. The low-cost portable device with a user interface was successfully developed to measure gait parameters for non-laboratory applications to support healthcare applications.

Abnormal Gait and Tremor Detection in the Elderly Ambulatory Behavior Using an IoT Smart Cane Device

In this paper, a novel approach for abnormal gait and tremor detection using a smart walking cane is introduced. Periodic muscle movement associated with Parkinson’s disease, such as arm shaking, vibrating arm, trembling fingers, rhythmic wrist movements, normal and abnormal walking pattern, was learned and classified with linear discriminant analysis. Although detecting symptoms related to disease with walking sticks might look trivial at first, throughout history, a cane or walking stick has been used as an assistive device to aid in ambulating, especially in the elderly and disabled, so embedding smart devices (that can learn ambulating pattern and detect anomalies associated with it) in the cane will help in early detection of diseases and facilitate early intervention. This approach is non-intrusive, and privacy issues being experienced in visual models do not arise, as users do not need to wear any special bracelet or wrist monitoring, and they only need to pick up the cane when they wish to move. The simplicity and efficient usage of a technique for detecting ambulatory anomalies is also demonstrated in this research. We extracted step counts, fall data and other valuable features from the cane, and detected anomalies by using isolation forest and one-class support vector machine (SVM) methods. Falls were detected easily and naturally with the cane, which had different alert modes (a soft alert when the cane lost equilibrium and was picked up within 15 s, and a strong alert otherwise). Intervention systems are proposed to forestall and limit the possibility of a type 2 error.

SAIL: A Deep-Learning-Based System for Automatic Gait Assessment From TUG Videos

Gait disorders are common in the elderly people, seriously hinder patients&#x2019; mobility and sometimes indicate underlying severe neurological diseases. Timely and automatic diagnosis of gait disorders is greatly desired. Existing methods with wearable devices put burdens on patients. We establish a video-based algorithm named <i>SAIL</i> to perform contactless gait assessment automatically. The SAIL contains three parts, namely, <i>skeleton detector</i>, <i>parameter extractor</i>, and <i>gait classifier</i>. Using a pose estimation algorithm, the skeleton detector converts RGB videos to a human skeleton sequence. Then, the parameter extractor extracts gait parameters from skeletons with a signal detection technique. Finally, a trained Support vector machine is used as a gait classifier to detect abnormal gait. The SAIL achieves 86.2&#x0025; sensitivity and 98.5&#x0025; specificity for abnormal gait detection on our <i>SAIL-TUG</i> dataset, outperforming general clinic doctors with 76.4&#x0025; and 97.4&#x0025;, respectively. Nine gait parameters and the binary gait classification result are included in the final gait report. We implement an automatic gait assessment system based on SAIL and deployed the user-interface software in more than 60 hospitals for practical applications. More than 30 000 gait reports have been automatically generated. Moreover, we establish a publicly available dataset named <i>SAIL-TUG</i> including 404 annotated Timed &#x201C;Up &amp; Go&#x201D; videos.

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

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

Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis.

Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat®-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person.

MCDCD: Multi-Source Unsupervised Domain Adaptation for Abnormal Human Gait Detection.

For gait analysis, especially for the detection of subtle gait abnormalities, the collected datasets involve high variability across subjects due to inherent biometric traits and movement behaviors, leading to limited detection accuracy and poor generalizability. To address this, we propose a novel deep multi-source Unsupervised Domain Adaptation (UDA) approach, namely Maximum Cross-Domain Classifier Discrepancy (MCDCD), which aims to improve the classification performance on the test subject (target domain) by leveraging the information from multiple labelled training subjects (source domains). Specifically, the proposed model consists of a feature extractor and a domain-specific category classifier per source domain. The former feature extractor learns to generate discriminative gait features. For the latter classifiers, we minimize the cross-entropy loss to accurately classify source samples, and simultaneously maximize a novel cross-domain discrepancy loss between any two category classifiers to minimize domain shift between multiple sources and the target domain. To validate the proposed MCDCD for detecting gait abnormalities on novel subjects, we collected both high-quality Motion capture (Mocap) and noisy Electromyography (EMG) data from eighteen subjects with both normal and imitated abnormal gaits. Experiment results using both data modalities demonstrate that the proposed approach can achieve superior performance in abnormal gait classification compared to baseline deep models and state-of-the-art UDA methods.

3-D Canonical Pose Estimation and Abnormal Gait Recognition With a Single RGB-D Camera

Assistive robots play an important role in improving the quality of life of patients at home. Among all the monitoring tasks, gait disorders are prevalent in elderly and people with neurological conditions and this increases the risk of fall. Therefore, the development of mobile systems for gait monitoring at home in normal living conditions is important. Here, we present a mobile system that is able to track humans and analyze their gait in canonical coordinates based on a single RGB-D camera. First, view-invariant three-dimensional (3-D) lower limb pose estimation is achieved by fusing information from depth images along with 2-D joints derived in RGB images. Next, both the 6-D camera pose and the 3-D lower limb skeleton are real-time tracked in a canonical coordinate system based on simultaneously localization and mapping (SLAM). A mask-based strategy is exploited to improve the re-localization of the SLAM in dynamic environments. Abnormal gait is detected by using the support vector machine and the bidirectional long-short term memory network with respect to a set of extracted gait features. To evaluate the robustness of the system, we collected multi-cameras, ground truth data from 16 healthy volunteers performing 6 gait patterns that mimic common gait abnormalities. The experiment results demonstrate that our proposed system can achieve good lower limb pose estimation and superior recognition accuracy compared to previous abnormal gait detection methods.

Measurement of Human Gait Symmetry using Body Surface Normals Extracted from Depth Maps.

In this paper, we introduce an approach for measuring human gait symmetry where the input is a sequence of depth maps of subject walking on a treadmill. Body surface normals are used to describe 3D information of the walking subject in each frame. Two different schemes for embedding the temporal factor into a symmetry index are proposed. Experiments on the whole body, as well as the lower limbs, were also considered to assess the usefulness of upper body information in this task. The potential of our method was demonstrated with a dataset of 97,200 depth maps of nine different walking gaits. An ROC analysis for abnormal gait detection gave the best result () compared with other related studies. The experimental results provided by our method confirm the contribution of upper body in gait analysis as well as the reliability of approximating average gait symmetry index without explicitly considering individual gait cycles for asymmetry detection.

Physiological Function Assessment Based on Kinect V2

This paper presented a framework based on fog computing for convenient and efficient Physiological Function Assessment, which consists of three parts: 1)measuring the degree of joint mobility; 2) investigating the abnormality of actions of upper limbs; and 3) abnormal gait detection for lower limbs. Especially, we introduced semi-automatic Rapid Upper Limb Assessment (RULA) using Kinect v2 for the upper limb motion evaluation. Since a specific action can be described by action sequences of different length, we used dynamic time warping (DTW) to find the similarity between action sequences with different length. Traditional DTW algorithm does not work well when the action sequences are long and complex. To address this problem, we improved the DTW method by modifying the mapping relationship and limiting the computation space. Our modified DTW algorithm was evaluated on a standard 3D action dataset (SYSU 3D HOI) and Human Upper Action dataset (HUA), achieving the accuracy of 83.75%, 89.50%, respectively. The result is significantly better than the traditional DTW and the reported methods. In our previous work, we described the framework and how to make physiological function assessment. The goal of this paper is to 1) enrich the experiments of previous work and 2) introduce the framework of using RULA for physiological function assessment. All the tests have been done in this framework based on fog computing.

Shoes and Leg Braces Integrated System for Abnormal Gait Detection

In this paper, an integrated system, which includes an intelligent shoe and a refined leg brace, is introduced and is used to detect one's gait abnormalities. Four force sensing resistors (FSRs) are installed under the insole to sense the force distribution patterns of the foot. An inertial measurement unit (IMU) consisting of a three-dimensional accelerometer and a three-dimensional gyroscope, plus a bend sensor are both installed on the leg brace to sense the leg motion and the bend angle of knee, respectively. The proposed method for abnormal gait detection is based on the calculations of CoP (center of pressure) for feature generation and SVM (supporting vector machine) for multi-pattern classification. Testing results show that the system is qualified in the classification of four different types of gaits: tiptoe walking, toe-in walking, toe-out walking as well as normal walking.

Development of Abnormal Gait Detection and Vibratory Stimulation System on Lower Limbs to Improve Gait Stability

The purpose of this study is to develop an abnormal gait detection algorithm and a vibratory stimulation system on a lower limb to improve gait stability and prevent falls. The system consists of a gait measurement module, an abnormal gait detection module, and a vibratory stimulation module. The gait measurement module measures the vertical acceleration of the ankle during walking using an accelerometer. The measured acceleration values are sent to a portable microcontroller, which controls vibratory stimulations to the ankles based on an algorithm that detects the peak acceleration values. If the acceleration peaks are found to occur irregularly, the abnormal gait detection algorithm activates the vibratory stimulation module. To determine the effect of vibratory stimulations under dynamic condition, this study investigated the contribution of ankle muscle proprioception on the control of dynamic stability and lower limb kinematics while walking using vibratory stimulation to alter the muscle spindle output of individuals' left lower limb. Vibrators were attached to the left ankle joint (tibialis anterior, triceps surae). Participants were required to walk along a travel path and step over an obstacle placed in their way. There were four task conditions; an obstacle (10%, 20%, and 30% of the participants' height) was positioned at the midpoint of the walkway, or the participants' walking path remained clear. For each obstacle condition, participants experienced either no vibration, or vibration of the tibialis anterior muscle and the triceps surae muscle of the left lower limb. Vibration began upon detection of an abnormal gait and continued for one second. Vibrating the ankle muscles of the left lower limb while stepping over an obstacle resulted in significant changes in COM behavior on both the anterior/posterior (A/P) and medial/lateral (M/L) planes. The results provide strong evidence that the primary endings of the ankle muscle spindles play a significant role in the control of posture and balance during the swing phase of locomotion by providing information on the movement of the body's COM with respect to the support foot.

Automatic detection of abnormal gait

Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. Understanding the detection of unusual movement patterns as a two-class problem suggests using support vector machines for classification. We present a homeomorphisms between 2D lattices and binary shapes that provides a robust vector space embedding of segmented body silhouettes. Experimental results demonstrate that feature vectors obtained from this scheme are well suited to detect abnormal gait. Wavering, faltering, and falling can be detected reliably across individuals without tracking or recognising limbs or body parts.