Physical activity group classification algorithm using triaxial acceleration and heart rate

Motofumi Nakanishi; Shintaro Izumi; Sho Nagayoshi; Hironori Sato; Hiroshi Kawaguchi; Masahiko Yoshimoto; Takafumi Ando; Satoshi Nakae; Chiyoko Usui; Tomoko Aoyama; Shigeho Tanaka

doi:10.1109/EMBC.2015.7318411

Physical activity group classification algorithm using triaxial acceleration and heart rate

Motofumi Nakanishi, Shintaro Izumi, Sho Nagayoshi, Hironori Sato, Hiroshi Kawaguchi, Masahiko Yoshimoto, Takafumi Ando, Satoshi Nakae, Chiyoko Usui, Tomoko Aoyama, Shigeho Tanaka

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Citations (Scopus)

Abstract

As described in this paper, a physical activity classification algorithm is proposed for energy expenditure estimation. The proposed algorithm can improve the classification accuracy using both the triaxial acceleration and heart rate. The optimal classification also contributes to improvement of the accuracy of the energy expenditures estimation. The proposed algorithm employs three indices: the heart rate reserve (%HRreserve), the filtered triaxial acceleration, and the ratio of filtered and unfiltered acceleration. The percentage HRreserve is calculated using the heart rate at rest condition and the maximum heart rate, which is calculated using Karvonen Formula. Using these three indices, a decision tree is constructed to classify physical activities into five classes: sedentary, household, moderate (excluding locomotive), locomotive, and vigorous. Evaluation results show that the average classification accuracy for 21 activities is 91%.

Original language	English
Title of host publication	2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	510-513
Number of pages	4
ISBN (Electronic)	9781424492718
DOIs	https://doi.org/10.1109/EMBC.2015.7318411
Publication status	Published - 2015 Nov 4
Externally published	Yes
Event	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: 2015 Aug 25 → 2015 Aug 29

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2015-November
ISSN (Print)	1557-170X

Other

Other	37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/Territory	Italy
City	Milan
Period	15/8/25 → 15/8/29

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

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10.1109/EMBC.2015.7318411

Cite this

Nakanishi, M., Izumi, S., Nagayoshi, S., Sato, H., Kawaguchi, H., Yoshimoto, M., Ando, T., Nakae, S., Usui, C., Aoyama, T., & Tanaka, S. (2015). Physical activity group classification algorithm using triaxial acceleration and heart rate. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (pp. 510-513). [7318411] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7318411

Physical activity group classification algorithm using triaxial acceleration and heart rate. / Nakanishi, Motofumi; Izumi, Shintaro; Nagayoshi, Sho et al.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 510-513 7318411 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2015-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nakanishi, M, Izumi, S, Nagayoshi, S, Sato, H, Kawaguchi, H, Yoshimoto, M, Ando, T, Nakae, S, Usui, C, Aoyama, T & Tanaka, S 2015, Physical activity group classification algorithm using triaxial acceleration and heart rate. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015., 7318411, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., pp. 510-513, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 15/8/25. https://doi.org/10.1109/EMBC.2015.7318411

Nakanishi M, Izumi S, Nagayoshi S, Sato H, Kawaguchi H, Yoshimoto M et al. Physical activity group classification algorithm using triaxial acceleration and heart rate. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 510-513. 7318411. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2015.7318411

Nakanishi, Motofumi ; Izumi, Shintaro ; Nagayoshi, Sho et al. / Physical activity group classification algorithm using triaxial acceleration and heart rate. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 510-513 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "As described in this paper, a physical activity classification algorithm is proposed for energy expenditure estimation. The proposed algorithm can improve the classification accuracy using both the triaxial acceleration and heart rate. The optimal classification also contributes to improvement of the accuracy of the energy expenditures estimation. The proposed algorithm employs three indices: the heart rate reserve (%HRreserve), the filtered triaxial acceleration, and the ratio of filtered and unfiltered acceleration. The percentage HRreserve is calculated using the heart rate at rest condition and the maximum heart rate, which is calculated using Karvonen Formula. Using these three indices, a decision tree is constructed to classify physical activities into five classes: sedentary, household, moderate (excluding locomotive), locomotive, and vigorous. Evaluation results show that the average classification accuracy for 21 activities is 91%.",

author = "Motofumi Nakanishi and Shintaro Izumi and Sho Nagayoshi and Hironori Sato and Hiroshi Kawaguchi and Masahiko Yoshimoto and Takafumi Ando and Satoshi Nakae and Chiyoko Usui and Tomoko Aoyama and Shigeho Tanaka",

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AU - Nakanishi, Motofumi

AU - Izumi, Shintaro

AU - Nagayoshi, Sho

AU - Sato, Hironori

AU - Kawaguchi, Hiroshi

AU - Yoshimoto, Masahiko

AU - Ando, Takafumi

AU - Nakae, Satoshi

AU - Usui, Chiyoko

AU - Aoyama, Tomoko

AU - Tanaka, Shigeho

PY - 2015/11/4

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N2 - As described in this paper, a physical activity classification algorithm is proposed for energy expenditure estimation. The proposed algorithm can improve the classification accuracy using both the triaxial acceleration and heart rate. The optimal classification also contributes to improvement of the accuracy of the energy expenditures estimation. The proposed algorithm employs three indices: the heart rate reserve (%HRreserve), the filtered triaxial acceleration, and the ratio of filtered and unfiltered acceleration. The percentage HRreserve is calculated using the heart rate at rest condition and the maximum heart rate, which is calculated using Karvonen Formula. Using these three indices, a decision tree is constructed to classify physical activities into five classes: sedentary, household, moderate (excluding locomotive), locomotive, and vigorous. Evaluation results show that the average classification accuracy for 21 activities is 91%.

AB - As described in this paper, a physical activity classification algorithm is proposed for energy expenditure estimation. The proposed algorithm can improve the classification accuracy using both the triaxial acceleration and heart rate. The optimal classification also contributes to improvement of the accuracy of the energy expenditures estimation. The proposed algorithm employs three indices: the heart rate reserve (%HRreserve), the filtered triaxial acceleration, and the ratio of filtered and unfiltered acceleration. The percentage HRreserve is calculated using the heart rate at rest condition and the maximum heart rate, which is calculated using Karvonen Formula. Using these three indices, a decision tree is constructed to classify physical activities into five classes: sedentary, household, moderate (excluding locomotive), locomotive, and vigorous. Evaluation results show that the average classification accuracy for 21 activities is 91%.

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ER -

Physical activity group classification algorithm using triaxial acceleration and heart rate

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