Design of an Energy Efficient Sensor Node for Wearable Applications

被引:1
作者
Adawy, Abdallah [1 ]
Djemal, Achraf [1 ,2 ]
Wang, Lidu [1 ]
Bouattour, Ghada [3 ]
Fakhfakh, Ahmed [2 ]
Kanoun, Olfa [1 ]
机构
[1] Tech Univ Chemnitz, Fac Elect Engn & Informat Technol, Measurement & Sensor Technol, Reichenhainer Str 70, D-09126 Chemnitz, Germany
[2] Natl Sch Elect & Telecommun Sfax, Digital Res Ctr Sfax, Lab Signals Syst Artificial Intelligence & Networ, Technopole Sfax, Ons City 3021, Tunisia
[3] Leuphana Univ, Measurement & Sensor Technol Prod Engn, Luneburg, Germany
来源
2024 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, I2MTC 2024 | 2024年
关键词
Low power sensor node (SN); Epilepsy diagnosis; Inertial Measurement Unit (IMU); Electromyography EMG); ACTIVITY RECOGNITION;
D O I
10.1109/I2MTC60896.2024.10560674
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The demand for compact and low-power sensor nodes (SNs) as wearable devices has increased significantly as they contribute significantly to the monitoring of people's health status, providing valuable information for the treatment of various diseases. To address this issue for epilepsy diagnosis, we propose an energy-efficient SN design. The SN incorporates both accelerometer and gyroscope sensors monitoring movements and can be easily attached to an electromyography (EMG) sensor monitoring muscle activities. An internal contact socket allows seamless integration of both devices. The designed system has been tested on a healthy subject and has demonstrated high-quality data measurement. The SN uses low-power Bluetooth 5.0 to communicate with the mobile application, which is designed to modify the built-in sensor parameters and monitor sensor output in real-time. It has an average current consumption of 14.3 mA. In addition, all data from the developed system can be stored on an attached SD card, providing a reliable storage solution.
引用
收藏
页数:6
相关论文
共 50 条
[31]   Energy-Efficient, Heterogeneous Sensor Selection for Physical Activity Detection in Wireless Body Area Networks [J].
Zois, Daphney-Stavroula ;
Levorato, Marco ;
Mitra, Urbashi .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2013, 61 (07) :1581-1594
[32]   A Survey on Energy Expenditure Estimation Using Wearable Devices [J].
Alvarez-Garcia, Juan A. ;
Cvetkovic, Bozidara ;
Lustrek, Mitja .
ACM COMPUTING SURVEYS, 2020, 53 (05)
[33]   Ambient and wearable sensor fusion for activity recognition in healthcare monitoring systems [J].
Pansiot, Julien ;
Stoyanov, Danail ;
McIlwraith, Douglas ;
Lo, Benny P. L. ;
Yang, G. Z. .
4TH INTERNATIONAL WORKSHOP ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN 2007), 2007, 13 :208-+
[34]   Wearable Body Sensor Networks: State-of-the-Art and Research Directions [J].
Gravina, Raffaele ;
Fortino, Giancarlo .
IEEE SENSORS JOURNAL, 2021, 21 (11) :12511-12522
[35]   Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis [J].
Chen, Kun-Hui ;
Chen, Po-Chao ;
Liu, Kai-Chun ;
Chan, Chia-Tai .
SENSORS, 2015, 15 (02) :4193-4211
[36]   Emotional Feeling Evaluation Model in Underwater Environment Based on Wearable Sensor [J].
Han, Xiao ;
Yan, Hongwei ;
Liu, Baojian ;
Liu, Wen .
MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
[37]   Dynamic Segmentation for Physical Activity Recognition Using a Single Wearable Sensor [J].
Alhammad, Nora ;
Al-Dossari, Hmood .
APPLIED SCIENCES-BASEL, 2021, 11 (06)
[38]   Cost-Effective Multitask Active Learning in Wearable Sensor Systems [J].
Arefeen, Asiful ;
Ghasemzadeh, Hassan .
SENSORS, 2025, 25 (05)
[39]   A Review on Applied Machine learning in Wearable Technology and its Applications [J].
Randhawa, Princy ;
Shanthagiri, Vijay ;
Kumar, Ajay .
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SUSTAINABLE SYSTEMS (ICISS 2017), 2017, :347-354
[40]   Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams [J].
Lim, Lipyeow ;
Misra, Archan ;
Mo, Tianli .
DISTRIBUTED AND PARALLEL DATABASES, 2013, 31 (02) :321-351