Recognizing and Counting Freehand Exercises Using Ubiquitous Cellular Signals
| Autor: | Jianbo Qi, Chao Liu, Ruobing Jiang, Feng Hong, Zhongwen Guo, Yue Xu, Guanlong Teng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
cellular signal
Computer science Rest Real-time computing 02 engineering and technology Interval (mathematics) TP1-1185 Biochemistry Signal Session (web analytics) Article Analytical Chemistry Base station Wearable Electronic Devices freehand exercise 0202 electrical engineering electronic engineering information engineering Humans Electrical and Electronic Engineering Instrumentation Exercise wireless sensing Wearable technology Monitoring Physiologic Repetition (rhetorical device) business.industry Chemical technology 020206 networking & telecommunications 020207 software engineering Atomic and Molecular Physics and Optics cellular sensing Exercise Therapy Software deployment Spectrogram business mobile sensing |
| Zdroj: | Sensors, Vol 21, Iss 4581, p 4581 (2021) Sensors Volume 21 Issue 13 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | Freehand exercises help improve physical fitness without any requirements for devices or places. Existing fitness assistant systems are typically restricted to wearable devices or exercising at specific positions, compromising the ubiquitous availability of freehand exercises. In this paper, we develop MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver placed on the ground. MobiFit passively monitors the ubiquitous cellular signals sent by the base station, which frees users from the space constraints and deployment overheads and provides accurate repetition counting, exercise type recognition and workout quality assessment without any attachments to the human body. The design of MobiFit faces new challenges of the uncertainties not only on cellular signal payloads but also on signal propagations because the sender (base station) is beyond the control of MobiFit and located far away. To tackle these challenges, we conducted experimental studies to observe the received cellular signal sequence during freehand exercises. Based on the observations, we constructed the analytic model of the received signals. Guided by the insights derived from the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis and extracts low-frequency features from each repetition for type recognition. Extensive experiments were conducted in both indoor and outdoor environments, which collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1% and low repetition duration estimation error within 0.3 s. Besides, the experiments show that MobiFit works both indoors and outdoors and supports multiple users exercising together. |
| Databáze: | OpenAIRE |
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