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Conference Paper Sensor Positioning and Data Acquisition for Activity Recognition using Deep Learning
Cited 11 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Seungeun Chung, Jiyoun Lim, Kyoung Ju Noh, Ga Gue Kim, Hyun Tae Jeong
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.154-159
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539473
Abstract
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.
KSP Keywords
Activity Recognition, Android mobile device, Data Acquisition(DAQ), Experiment results, Human activity, IMU sensor, Low frequency, On-Body, Real-world, Training and evaluation, deep learning(DL)