JRM Vol.30 No.5 pp. 706-716
doi: 10.20965/jrm.2018.p0706


Feature Selection for Work Recognition and Working Motion Measurement

Saori Miyajima*, Takayuki Tanaka*, Natsuki Miyata**, Mitsunori Tada**, Masaaki Mochimaru**, and Hiroyuki Izumi***

*Graduate School of Information Science and Technology, Hokkaido University
Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan

**National Institute of Advanced Industrial Science and Technology (AIST)
2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan

***University of Occupational and Environmental Health, Japan
1-1 Iseigaoka, Yahatashi-ku, Kitakyushu-shi, Fukuoka 807-0804, Japan

May 21, 2018
August 9, 2018
October 20, 2018
motion measurement, digital human model, work recognition, support vector machine, feature selection

As the demand for nursing care services is growing, the physical burden involved in caregiving has drawn widespread attention. To mitigate the physical burden in caregiving, we have to recognize what kind of work and problems are involved in each caregiving task. To identify the problems involved in caregiving, we need to recognize the work and analyze its workload. Aiming to reduce the burden on the waist during caregiving tasks, we are developing inertial sensor suits for measuring the working motions. With the developed method, the burden on the waist is estimated from the waist posture. Considering its use in practical caregiving sites, the number of inertial sensors should be the minimum necessary, which depends on the number of body parts where to measure the posture. In this study, we select the body parts to achieve the two above-mentioned goals: to recognize the work involved in caregiving and capture the waist posture. A support vector machine (SVM) is used to recognize the work. Its conventional method of selecting the features on which to recognize the work only considers the recognition accuracy and does not sufficiently meet the needs for measuring the postures. Therefore, we propose a new feature-selection method, which can evaluate the waist-posture measuring accuracy and can make forward feature selections in the same manner as the conventional wrapper method. We have verified the effectiveness of the proposed method by measuring simple simulated work motions.

Measured and recognized 3 works: W1, W2, W3

Measured and recognized 3 works: W1, W2, W3

Cite this article as:
S. Miyajima, T. Tanaka, N. Miyata, M. Tada, M. Mochimaru, and H. Izumi, “Feature Selection for Work Recognition and Working Motion Measurement,” J. Robot. Mechatron., Vol.30 No.5, pp. 706-716, 2018.
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Last updated on Sep. 21, 2023