Investigating the capitalize effect of sensor position for training type recognition in a body weight training support system

Masashi Takata, Manato Fujimoto, Keiichi Yasumoto, Yugo Nakamura, Yutaka Arakawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A body weight training (BWT) means the training which utilizes the self-weight instead of the weight machine. The feedback of form and proper training menu recommendation is important for maximizing the effect of BWT. The objective of this study is to realize a novel support system which allows beginners to perform effective BWT alone, under wearable computing environment. To make an effective feedback, it is necessary to recognize BWT type with high accuracy. However, since the accuracy is greatly affected by the position of wearable sensors, we need to know the sensor position which achieves the high accuracy in recognizing the BWT type. We investigated 10 types BWT recognition accuracy for each sensor position. We found that waist is the best position when only 1 sensor is used. When 2 sensors are used, we found that the best combination is of waist and wrist. We conducted an evaluation experiment to show the effectiveness of sensor position. As a result of leave-one-person-out cross-validation from 13 subjects to confirm validity, we calculated the F-measure of 93.5% when sensors are placed on both wrist and waist.

Original languageEnglish
Title of host publicationUbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers
PublisherAssociation for Computing Machinery, Inc
Pages1404-1408
Number of pages5
ISBN (Electronic)9781450359665
DOIs
Publication statusPublished - Oct 8 2018
Externally publishedYes
Event2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018 - Singapore, Singapore
Duration: Oct 8 2018Oct 12 2018

Publication series

NameUbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers

Other

Other2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018
CountrySingapore
CitySingapore
Period10/8/1810/12/18

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Information Systems

Cite this

Takata, M., Fujimoto, M., Yasumoto, K., Nakamura, Y., & Arakawa, Y. (2018). Investigating the capitalize effect of sensor position for training type recognition in a body weight training support system. In UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers (pp. 1404-1408). (UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers). Association for Computing Machinery, Inc. https://doi.org/10.1145/3267305.3267504