Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

<div><div><div><p>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.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a 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 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label 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 in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

<div><div><div><p>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.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a 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 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label 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 in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

Survey of Human Gait Analysis and Recognition for Different Sensor Modalities

Gait is a behavioural biometric which sometimes changes due to diseases but it is still a strong identification metric that is widely used in forensic works, state biometric preserve sectors, and medical laboratories. Gait analysis sometimes helps to identify person’s present mental state which reflects on physiological therapy for improved biological system. There are various gait measurement forms which expand the research area from crime detection to medical enhancement. Many research works have been done so far for gait recognition. Many researchers focused on skeleton image of people to extract gait features and many worked on stride length. Various sensors have been used to detect gait in various light forms. This paper is a brief survey of works on gait recognition, collected from various sources of science and technology literature. We have discussed few efficient models that worked best as well as we have discussed about few data sets available.

Insole Gait Acquisition System Based on Wearable Sensors

Human gait analysis is a growing field of research interest in medical treatment, sports training and structural health monitoring. In our study, we propose a low-cost insole design with wearable sensors based on piezoelectric discs (PZT) and an inertial measurement unit (IMU) to acquire the human gait. The sensors are placed at three points of a shoe sole: toe, metatarsal and heel. The human gait obtained from such an insole layout is significantly affected by plantar pressure distribution and alignment of the feet. The PZT sensors give an insight into the pressure map under the feet, and the IMUs record projection and orientation of the feet.

ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments

Mobile robotic platforms have made inroads in the rehabilitation area as gait assistance devices. They have rarely been used for human gait monitoring and analysis. The integration of mobile robots in this field offers the potential to develop multiple medical applications and achieve new discoveries. This study proposes the use of a mobile robotic platform based on depth cameras to perform the analysis of human gait in practical scenarios. The aim is to prove the validity of this robot and its applicability in clinical settings. The mechanical and software design of the system is presented, as well as the design of the controllers of the lane-keeping, person-following, and servoing systems. The accuracy of the system for the evaluation of joint kinematics and the main gait descriptors was validated by comparison with a Vicon-certified system. Some tests were performed in practical scenarios, where the effectiveness of the lane-keeping algorithm was evaluated. Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns. The results demonstrate that the system can perform gait analysis with high accuracy. In the curved sections of the paths, the knee joint is affected by occlusion and the deviation of the person in the camera reference system. This issue was greatly improved by adjusting the servoing system and the following distance. The control strategy of this robot was specifically designed for the analysis of human gait from the frontal part of the participant, which allows one to capture the gait properly and represents one of the major contributions of this study in clinical practice.

Athlete Rehabilitation Evaluation System Based on Internet of Health Things and Human Gait Analysis Algorithm

In order to improve the effect of athlete’s injury recognition and rehabilitation evaluation, this paper studies the traditional rehabilitation evaluation method and proposes a new athlete rehabilitation evaluation system combining the Internet of Health Things technology and human gait analysis algorithm. Moreover, this paper combines sports characteristics to improve the algorithm of human gait analysis. In addition, through the study of the athlete’s human body modeling and movement process, a human gait analysis algorithm that can be applied to multiple sports is proposed, and the gait parameter analysis and algorithm reliability research are carried out through simulation analysis. After confirming that the algorithm is effective, this paper combines the Internet of Health Things technology to construct a system model, obtains the system function module architecture with the support of the Internet of Health Things technology, and conducts experiments to verify the system performance. From the experimental research, it can be seen that the model constructed in this paper meets the theoretical and practical needs, and the system in this paper can be applied to practice in the future. The human gait recognition algorithm constructed in this article has a good effect and can play an important role in sports rehabilitation of athletes. At the same time, the system constructed in this article has certain advantages over traditional sports rehabilitation systems with the support of algorithms.