WEARABLE GAIT DEVICE FOR LONG-TERM MONITORING
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Published:2023-09-30
Issue:3
Volume:23
Page:791-802
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ISSN:2068-3049
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Container-title:Journal of Science and Arts
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language:en
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Short-container-title:J. Sci. Arts
Author:
CACIULA ION1, IONITA GIORGIAN MARIUS1, COANDA HENRI GEORGE1, COLTUC DINU1, ANGELESCU NICOLETA1, ALBU FELIX1, HAGIESCU DANIELA2
Affiliation:
1. Valahia University of Targoviste, Department of Electronics, Telecommunications and Energy Engineering, 130004 Targoviste, Romania. 2. Advanced Slisys SRL, 060104 Bucuresti, Romania.
Abstract
This study describes a low-cost and easy to deploy gait monitoring system that uses an ESP32 microcontroller and an ICM-20948 module. The ESP32 microcontroller collects data from the ICM-20948 module and these data are used to train a convolutional neural network (CNN) to classify gait patterns into two categories: normal and pathological. The results show that the system can achieve a high accuracy for binary gait classification, being able to correctly classify 97.05% of the normal gait samples and 84.54% of the pathological gait samples. The power consumption of the devive was measured using a calibrated and dual-acquisition digital multimeter. The estimated operating time was around 12 hours, with a battery capacity of 1800 mAh LiPo type. Therefore, it could be used to track the gait of patients with neurological disorders or to assess the effectiveness of gait rehabilitation treatments.
Publisher
Valahia University of Targoviste - Journal of Science and Arts
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference16 articles.
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