S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Implementation of Machine Learning and Deep Learning Algorithms with Dimensionality Reduction Methods for IoT Gait Analysis and Monitoring Systems

Passara Chanchotisatien and Chanvichet Vong

(Received June 21, 2021; Accepted August 24, 2021)

Keywords: HAFR, foot and ankle monitoring system, gyroscope, IMU, accelerometer, gait monitoring

In this paper, we present an end-to-end monitoring system, which is used for patients who have foot or ankle impairments. This system has been created to help orthopedic doctors optimize treatment for patients recovering from foot and ankle injuries. The system consists of three main parts: a wearable controlled ankle motion (CAM) boot equipped with inertial and load sensors, a web application that provides visual feedback obtained from sensors, and the implementation of machine learning and deep learning to analyze walking activity and gait. Sensors used on the CAM boot include an accelerometer, a gyroscope, and load cells. Values from sensors attached to the CAM boot are sent wirelessly to the database. The web application takes sensor values from the database and returns visual feedback on the patient’s walking patterns in the form of different graphs. The graphs can be used to analyze and determine abnormalities in the patient’s gait and serve as a visual aid for patients during rehabilitation. Sensor values obtained from the database are used to train machine learning and deep learning models to recognize and differentiate between seven activities performed by the patient. We study and compare three dimensionality reduction methods and six classifiers. As a result, we find that the joint incorporation of the dimensionality reduction method of sparse principal component analysis (PCA) and the classifier random forest (RF) gives the best result with an accuracy of 99.5%.

Corresponding author: Passara Chanchotisatien




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