scholarly journals Correlations of Gait Phase Kinematics and Cortical EEG: Modelling Human Gait with Data from Sensors

2020 ◽  
Author(s):  
Chaitanya Nutakki ◽  
Sandeep Bodda ◽  
Shyam Diwakar
Keyword(s):  
Human Gait ◽  
Gait Phase

scholarly journals Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 789
Author(s):  
David Kreuzer ◽  
Michael Munz
Keyword(s):  
Gait Analysis ◽  
Time Series Data ◽  
Low Cost ◽  
Analysis Data ◽  
Gait Disorders ◽  
Human Gait ◽  
Series Data ◽  
Ageing Society ◽  
Time Motion ◽  
Gait Phase

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

scholarly journals An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection

2020 ◽  
Vol 17 (16) ◽  
pp. 5633 ◽  
Author(s):  
Tao Zhen ◽  
Lei Yan ◽  
Jian-lei Kong
Keyword(s):  
Deep Learning ◽  
Information Acquisition ◽  
Gait Recognition ◽  
Medical Rehabilitation ◽  
Identification Accuracy ◽  
Sensor Data ◽  
Human Gait ◽  
Phase Detection ◽  
Feature Extractor ◽  
Gait Phase

Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation—while ignoring the inherent correlation in high-dimensional spaces—which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.

FSR and IMU sensors-based human gait phase detection and its correlation with EMG signal for different terrain walk

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin Negi ◽  
Shiru Sharma ◽  
Neeraj Sharma
Keyword(s):  
Gait Analysis ◽  
Real Time ◽  
Medial Gastrocnemius ◽  
Human Gait ◽  
Phase Detection ◽  
Zero Crossing ◽  
Content Type ◽  
Open Environment ◽  
Emg Signal ◽  
Gait Phase

Purpose The purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent. Design/methodology/approach Gait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities. All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes. In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors. The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis. For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data. In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis. Findings The effectiveness of the gait phase detection was obtained in an open environment. Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors. Both sensors showed their usefulness for detecting the gait events in real-time, i.e. within 10 ms. The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains. Practical implications This study leads to an understanding of human gait analysis for different types of terrains. A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains. Originality/value The noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver. Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains.

scholarly journals Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Yan ◽  
Tao Zhen ◽  
Jian-Lei Kong ◽  
Lian-Ming Wang ◽  
Xiao-Lei Zhou
Keyword(s):  
Neural Network ◽  
Information Acquisition ◽  
Classification Accuracy ◽  
Back Propagation ◽  
Disease Diagnosis ◽  
Human Gait ◽  
Phase Detection ◽  
Efficient Design ◽  
Gait Phase ◽  
Acceleration Signals

Human gait phase recognition is a significant technology for rehabilitation training robot, human disease diagnosis, artificial prosthesis, and so on. The efficient design of the recognition method for gait information is the key issue in the current gait phase division and eigenvalues extraction research. In this paper, a novel voting-weighted integrated neural network (VWI-DNN) is proposed to detect different gait phases from multidimensional acceleration signals. More specifically, it first employs a gait information acquisition system to collect different IMU sensors data fixed on the human lower limb. Then, with dimensionality reduction and four-phase division preprocessing, key features are selected and merged as unified vectors to learn common and domain knowledge in time domain. Next, multiple refined DNNs are transferred to design a multistream integrated neural network, which utilizes the mixture-granularity information to exploit high-dimensional feature representative. Finally, a voting-weighted function is developed to fuse different submodels as a unified representation for distinguishing small discrepancy among different gait phases. The end-to-end implementation of the VWI-DNN model is fine-tuned by the loss optimization of gradient back-propagation. Experimental results demonstrate the outperforming performance of the proposed method with higher classification accuracy compared with the other methods, of which classification accuracy and macro-F1 is up to 99.5%. More discussions are provided to indicate the potential applications in combination with other works.

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