TY - GEN
T1 - Wearable Accelerometer Optimal Positions for Human Motion Recognition
AU - Xia, Chengshuo
AU - Sugiura, Yuta
N1 - Funding Information:
The authors would like to acknowledge the support of JST PRESTO program (JPMJPR17J4).
PY - 2020/3
Y1 - 2020/3
N2 - An intelligent human activity recognition system is influenced to some extent by sensor placement. In this paper, the number, and placement positions, of wearable accelerometers have been investigated to determine their influence on a human activity recognition system. Given 17 possible human sensor placements, we developed a multi-stage and multi-swarm discrete particle swarm optimization algorithm to explore the optimal sensor combination for various required sensor amounts. Relevant experimentation involved 10 different human daily activities, achieving an average prediction accuracy for a 4-sensor optimal combination of 95.12% via support vector machine classifier. The number and corresponding placement of sensors required for activity recognition have also been provided in this paper.
AB - An intelligent human activity recognition system is influenced to some extent by sensor placement. In this paper, the number, and placement positions, of wearable accelerometers have been investigated to determine their influence on a human activity recognition system. Given 17 possible human sensor placements, we developed a multi-stage and multi-swarm discrete particle swarm optimization algorithm to explore the optimal sensor combination for various required sensor amounts. Relevant experimentation involved 10 different human daily activities, achieving an average prediction accuracy for a 4-sensor optimal combination of 95.12% via support vector machine classifier. The number and corresponding placement of sensors required for activity recognition have also been provided in this paper.
KW - amount and position
KW - motion recognition
KW - particle swarm optimization
KW - wearable accelerometer
UR - http://www.scopus.com/inward/record.url?scp=85085219068&partnerID=8YFLogxK
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U2 - 10.1109/LifeTech48969.2020.1570618961
DO - 10.1109/LifeTech48969.2020.1570618961
M3 - Conference contribution
AN - SCOPUS:85085219068
T3 - LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
SP - 19
EP - 20
BT - LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020
Y2 - 10 March 2020 through 12 March 2020
ER -