Novel IoT-Based Privacy-Preserving Yoga Posture Recognition System Using Low-Resolution Infrared Sensors and Deep Learning

Munkhjargal Gochoo, Tan Hsu Tan, Shih Chia Huang*, Tsedevdorj Batjargal, Jun-Wei Hsieh, Fady S. Alnajjar, Yung Fu Chen

*此作品的通信作者

研究成果: Article同行評審

48 引文 斯高帕斯(Scopus)

摘要

In recent years, the number of yoga practitioners has been drastically increased and there are more men and older people practice yoga than ever before. Internet of Things (IoT)-based yoga training system is needed for those who want to practice yoga at home. Some studies have proposed RGB/Kinect camera-based or wearable device-based yoga posture recognition methods with a high accuracy; however, the former has a privacy issue and the latter is impractical in the long-term application. Thus, this paper proposes an IoT-based privacy-preserving yoga posture recognition system employing a deep convolutional neural network (DCNN) and a low-resolution infrared sensor-based wireless sensor network (WSN). The WSN has three nodes ( {x} , {y} , and {z} -axes) where each integrates 8\times 8 pixels' thermal sensor module and a Wi-Fi module for connecting the deep learning server. We invited 18 volunteers to perform 26 yoga postures for two sessions each lasted for 20 s. First, recorded sessions are saved as.csv files, then preprocessed and converted to grayscale posture images. Totally, 93 200 posture images are employed for the validation of the proposed DCNN models. The tenfold cross-validation results revealed that F1-scores of the models trained with xyz (all 3-axes) and {y} (only {y} -axis) posture images were 0.9989 and 0.9854, respectively. An average latency for a single posture image classification on the server was 107 ms. Thus, we conclude that the proposed IoT-based yoga posture recognition system has a great potential in the privacy-preserving yoga training system.

原文English
文章編號8707064
頁(從 - 到)7192-7200
頁數9
期刊IEEE Internet of Things Journal
6
發行號4
DOIs
出版狀態Published - 8月 2019

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