WEARABLE GAIT DEVICE FOR LONG-TERM MONITORING

被引:1
作者
Caciula, Ion [1 ]
Ionita, Giorgian Marius [1 ]
Coanda, Henri George [1 ]
Coltuc, Dinu [1 ]
Angelescu, Nicoleta [1 ]
Albu, Felix [1 ]
Hagiescu, Daniela [2 ]
机构
[1] Valahia Univ Targoviste, Dept Elect Telecommun & Energy Engn, Targoviste 130004, Romania
[2] Adv Slisys SRL, Bucharest 060104, Romania
关键词
Gait monitoring; data acquisition; ESP32; microcontroller; ICM-20948; module; power consumption; convolutional neural networks;
D O I
10.46939/J.Sci.Arts-23.3-c01
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:791 / 802
页数:12
相关论文
共 50 条
[31]   Analyzing gait symmetry with automatically synchronized wearable sensors in daily life [J].
Steinmetzer, Tobias ;
Wilberg, Sandro ;
Boenninger, Ingrid ;
Travieso, Carlos M. .
MICROPROCESSORS AND MICROSYSTEMS, 2020, 77
[32]   Abnormal Gait Detection Using Wearable Hall-Effect Sensors [J].
Chheng, Courtney ;
Wilson, Denise .
SENSORS, 2021, 21 (04) :1-23
[33]   Feasibility and basic acoustic characteristics of intelligent long-term bowel sound analysis in term neonates [J].
Zhou, Ping ;
Lu, Meiling ;
Chen, Ping ;
Wang, Danlei ;
Jin, Zhenchao ;
Zhang, Lian .
FRONTIERS IN PEDIATRICS, 2022, 10
[34]   Long-term & short-term bike sharing demand predictions using contextual data [J].
Tabandeh, Mirfamam ;
Antoniou, Constantinos ;
Cantelmo, Guido .
2023 8TH INTERNATIONAL CONFERENCE ON MODELS AND TECHNOLOGIES FOR INTELLIGENT TRANSPORTATION SYSTEMS, MT-ITS, 2023,
[35]   A prototype of a wearable health device for mobile telemonitoring applications [J].
De Vito, Luca ;
Picariello, Enrico ;
Picariello, Francesco ;
Rapuano, Sergio ;
Tudosa, Ioan .
2022 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA 2022), 2022,
[36]   Design of a blood pump for a wearable artificial kidney device [J].
Markovic, Miroslav ;
Rapin, Michael ;
Correvon, Marc ;
Perriard, Yves .
2012 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), 2012, :3649-3654
[37]   Hybrid Integration of Wearable Devices for Physiological Monitoring [J].
Zhang, Yu ;
Zheng, Xin Ting ;
Zhang, Xiangyu ;
Pan, Jieming ;
Thean, Aaron Voon-Yew .
CHEMICAL REVIEWS, 2024, 124 (18) :10386-10434
[38]   "Smart clothing" wearable for vital signs monitoring [J].
Nowosielski, Leszek ;
Dudzinski, Bartosz ;
Slubowska, Aleksandra Maria .
PRZEGLAD ELEKTROTECHNICZNY, 2022, 98 (05) :62-66
[39]   Prototype of a luxmeter with high sensitivity suitable for long-term data recording [J].
Hrbac, Roman ;
Kolar, Vaclav ;
Novak, Tomas .
2015 INTERNATIONAL CONFERENCE ON APPLIED ELECTRONICS (AE), 2015, :71-74
[40]   A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting [J].
Liu, Fuqiang ;
Wang, Jiawei ;
Tian, Jingbo ;
Zhuang, Dingyi ;
Miranda-Moreno, Luis ;
Sun, Lijun .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (10) :19064-19075