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학술대회 Sensor Positioning and Data Acquisition for Activity Recognition using Deep Learning
Cited 6 time in scopus Download 14 time Share share facebook twitter linkedin kakaostory
저자
정승은, 임지연, 노경주, 김가규, 정현태
발행일
201810
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.154-159
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539473
협약과제
18ZS1100, 자율성장형 AI 핵심원천기술 연구, 이윤근
초록
In this paper, we perform a study on the sensor positioning and data acquisition details for the HAR system. We develop a framework to support training and evaluation of a deep learning model on human activity data. The activity data is collected in both real-world and lab environments using our testbed system that consists of on-body IMU sensors and an Android mobile device. From the experiment results, we identify that low-frequency (e.g., 10 Hz) activity data is effective for the activity recognition. We verify that four sensors at both sides of wrists, right ankle, and waist can achieve 91.2% recognition accuracy in recognizing ADLs including eating and driving activity. Also, we recognize that two sensors on the left wrist and right ankle are sufficient to present reasonable performance without incurring discomfort in everyday life.
키워드
deep learning, deep neural networks, human activity recognition, Mobile sensing, wearable computing
KSP 제안 키워드
Android mobile device, Data Acquisition(DAQ), Deep neural network(DNN), Experiment results, Human activity recognition(HAR), IMU sensor, Learning model, Mobile Sensing, Real-world, Recognition Accuracy, Training and evaluation