TY - GEN
T1 - Accident prediction based on motion data for perception-assist with a power-assist robot
AU - Kiguchi, Kazuo
AU - Matsuo, Ryosuke
N1 - Funding Information:
ACKNOWLEDGMENT This research was partially supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (B) JP16H04305.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/2
Y1 - 2018/2/2
N2 - Power-assist robots are expected to help facilitate the daily living motion of physically weak persons. Perception-assist has been studied to secure the safety of robot users whose sensory abilities are deteriorated or limited. The interaction between the human and environment must be carefully observed by the robot to determine the possibility of an accident in perception-assist. When the robot detects a potential accident during this interaction, it tries to avoid the accident by modifying the user's motion automatically using perception-assist. Therefore, it is important for the robot to predict potential accidents, such as falling, as soon as possible. In this paper, an accident prediction method for lower-limb perception-assist is proposed and evaluated for effectiveness. In the proposed method, the possibility of accident is predicted based on the lower-limb motion and zero-moment point of the robot user as well as information from the surrounding environment. A multilayer artificial neural network is applied in the proposed method.
AB - Power-assist robots are expected to help facilitate the daily living motion of physically weak persons. Perception-assist has been studied to secure the safety of robot users whose sensory abilities are deteriorated or limited. The interaction between the human and environment must be carefully observed by the robot to determine the possibility of an accident in perception-assist. When the robot detects a potential accident during this interaction, it tries to avoid the accident by modifying the user's motion automatically using perception-assist. Therefore, it is important for the robot to predict potential accidents, such as falling, as soon as possible. In this paper, an accident prediction method for lower-limb perception-assist is proposed and evaluated for effectiveness. In the proposed method, the possibility of accident is predicted based on the lower-limb motion and zero-moment point of the robot user as well as information from the surrounding environment. A multilayer artificial neural network is applied in the proposed method.
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U2 - 10.1109/SSCI.2017.8285408
DO - 10.1109/SSCI.2017.8285408
M3 - Conference contribution
AN - SCOPUS:85046131068
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 5
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -