Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

Muhammad Ahmad; Adil Khan; Manuel Mazzara; Salvatore Distefano

doi:10.1007/978-3-030-17798-0_19

Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

Muhammad Ahmad^*, Adil Khan, Manuel Mazzara, Salvatore Distefano

^*Corresponding author for this work

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

7 Scopus citations

Abstract

Physical activity recognition using wearable devices can provide valued information regarding an individual’s degree of functional ability and lifestyle. Smartphone-based physical activity recognition is a well-studied area. However, research on smartwatch-based physical activity recognition, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based physical activity recognition domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based physical activity recognition system for both personal and impersonal models in real life scenarios. To further validate our hypothesis for both personal and impersonal models, we tested single subject out cross validation process for smartwatch-based physical activity recognition.

Original language	English
Title of host publication	Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC
Editors	Kohei Arai, Supriya Kapoor
Publisher	Springer Verlag
Pages	220-233
Number of pages	14
ISBN (Print)	9783030177973
DOIs	https://doi.org/10.1007/978-3-030-17798-0_19
State	Published - 2020
Externally published	Yes
Event	Computer Vision Conference, CVC 2019 - Las Vegas, United States Duration: 25 Apr 2019 → 26 Apr 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	944
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Computer Vision Conference, CVC 2019
Country/Territory	United States
City	Las Vegas
Period	25/04/19 → 26/04/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Accelerometer
Gyroscope
Health care services
Machine learning
Magnetometer
Physical activity recognition
Smartwatch

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-030-17798-0_19

Cite this

Ahmad, M., Khan, A., Mazzara, M., & Distefano, S. (2020). Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches. In K. Arai, & S. Kapoor (Eds.), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC (pp. 220-233). (Advances in Intelligent Systems and Computing; Vol. 944). Springer Verlag. https://doi.org/10.1007/978-3-030-17798-0_19

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title = "Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches",

abstract = "Physical activity recognition using wearable devices can provide valued information regarding an individual{\textquoteright}s degree of functional ability and lifestyle. Smartphone-based physical activity recognition is a well-studied area. However, research on smartwatch-based physical activity recognition, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based physical activity recognition domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based physical activity recognition system for both personal and impersonal models in real life scenarios. To further validate our hypothesis for both personal and impersonal models, we tested single subject out cross validation process for smartwatch-based physical activity recognition.",

keywords = "Accelerometer, Gyroscope, Health care services, Machine learning, Magnetometer, Physical activity recognition, Smartwatch",

author = "Muhammad Ahmad and Adil Khan and Manuel Mazzara and Salvatore Distefano",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Computer Vision Conference, CVC 2019 ; Conference date: 25-04-2019 Through 26-04-2019",

year = "2020",

doi = "10.1007/978-3-030-17798-0\_19",

language = "English",

isbn = "9783030177973",

series = "Advances in Intelligent Systems and Computing",

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}

Ahmad, M, Khan, A, Mazzara, M & Distefano, S 2020, Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches. in K Arai & S Kapoor (eds), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. Advances in Intelligent Systems and Computing, vol. 944, Springer Verlag, pp. 220-233, Computer Vision Conference, CVC 2019, Las Vegas, United States, 25/04/19. https://doi.org/10.1007/978-3-030-17798-0_19

Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches. / Ahmad, Muhammad; Khan, Adil; Mazzara, Manuel et al.
Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. ed. / Kohei Arai; Supriya Kapoor. Springer Verlag, 2020. p. 220-233 (Advances in Intelligent Systems and Computing; Vol. 944).

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

TY - GEN

T1 - Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

AU - Ahmad, Muhammad

AU - Khan, Adil

AU - Mazzara, Manuel

AU - Distefano, Salvatore

PY - 2020

Y1 - 2020

N2 - Physical activity recognition using wearable devices can provide valued information regarding an individual’s degree of functional ability and lifestyle. Smartphone-based physical activity recognition is a well-studied area. However, research on smartwatch-based physical activity recognition, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based physical activity recognition domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based physical activity recognition system for both personal and impersonal models in real life scenarios. To further validate our hypothesis for both personal and impersonal models, we tested single subject out cross validation process for smartwatch-based physical activity recognition.

AB - Physical activity recognition using wearable devices can provide valued information regarding an individual’s degree of functional ability and lifestyle. Smartphone-based physical activity recognition is a well-studied area. However, research on smartwatch-based physical activity recognition, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based physical activity recognition domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based physical activity recognition system for both personal and impersonal models in real life scenarios. To further validate our hypothesis for both personal and impersonal models, we tested single subject out cross validation process for smartwatch-based physical activity recognition.

KW - Accelerometer

KW - Gyroscope

KW - Health care services

KW - Machine learning

KW - Magnetometer

KW - Physical activity recognition

KW - Smartwatch

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DO - 10.1007/978-3-030-17798-0_19

M3 - Conference contribution

AN - SCOPUS:85065483507

SN - 9783030177973

T3 - Advances in Intelligent Systems and Computing

SP - 220

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BT - Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC

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ER -

Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this