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Efficient Gait Analysis Using Deep Learning Techniques

K. M. Monica, R. Parvathi*

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600117, India

* Corresponding Author: R. Parvathi. Email: email

Computers, Materials & Continua 2023, 74(3), 6229-6249. https://doi.org/10.32604/cmc.2023.032273

Abstract

Human Activity Recognition (HAR) has always been a difficult task to tackle. It is mainly used in security surveillance, human-computer interaction, and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things (IoT). Human Activity Recognition data can be recorded with the help of sensors, images, or smartphones. Recognizing daily routine-based human activities such as walking, standing, sitting, etc., could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network (2D CNN) MODEL, Long Short Term Memory (LSTM) Model, Bidirectional long short-term memory (Bi-LSTM) are used for the classification. It has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate, with almost all activities accurately getting recognized over 90% of the time. Furthermore, because all the examples are generated from only 20 s of data, these actions can be recognised fast. Apart from classification, the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy (CP) for the evaluation of chosen Spatio-temporal features based on 3D foot trajectory. Case-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 kg. Optical Motion Capture (OMC) equipment was used as the referral method to assess the functionality and quality of the foot-worn device. The average accuracy precision for stride length, cadence, and step length was 3.5 ± 4.3, 4.1 ± 3.8, and 0.6 ± 2.7 cm respectively. For cadence, stride length, swing, and step length, people with CP had considerably high inter-stride variables. Foot-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.

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APA Style
Monica, K.M., Parvathi, R. (2023). Efficient gait analysis using deep learning techniques. Computers, Materials & Continua, 74(3), 6229-6249. https://doi.org/10.32604/cmc.2023.032273
Vancouver Style
Monica KM, Parvathi R. Efficient gait analysis using deep learning techniques. Comput Mater Contin. 2023;74(3):6229-6249 https://doi.org/10.32604/cmc.2023.032273
IEEE Style
K.M. Monica and R. Parvathi, "Efficient Gait Analysis Using Deep Learning Techniques," Comput. Mater. Contin., vol. 74, no. 3, pp. 6229-6249. 2023. https://doi.org/10.32604/cmc.2023.032273



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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