research-article

Understanding activity segmentation for multi-sport competitions

Authors:

Justin Whitlock,

Shubham JainAuthors Info & Claims

WearSys '18: Proceedings of the 4th ACM Workshop on Wearable Systems and Applications

Pages 16 - 20

https://doi.org/10.1145/3211960.3211972

Published: 10 June 2018 Publication History

Abstract

Despite the advances in activity detection, their applications in the sports domain are limited. Athletic environments are fast and challenging. Athletes often perform more than one activity in a single workout, especially if they are training for a multi-sport competition, such as a triathlon. These competitions require an athlete to transition from one activity to another quickly. Current logging applications require the user to select the activity they are about to perform, and start and stop the timer for each activity. This could increase the athlete's transition time, and it would not give the athlete an estimate of how much time they spent in transition between the activities.

This paper explores activity segmentation for multi-sport scenarios. Our goal is to identify the activities and segment a user's workout trace into constituent activities, including the transition periods. We use an Apple Watch to gather inertial sensor data and validate our system in the context of a triathlon. The system was trained and tested on 3 activities (running, biking, and swimming), as well as simple actions performed in transition, from 5 different participants. Our system achieves 91% accuracy in detecting the activity, and can accurately identify the start and stop times for each. We also validate our results with data collected from a volunteer at a triathlon.

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Cited By

Baraka AMohd Noor M(2024)Deep similarity segmentation model for sensor-based activity recognitionMultimedia Tools and Applications10.1007/s11042-024-18933-2Online publication date: 4-May-2024
https://doi.org/10.1007/s11042-024-18933-2
Kanbetsunawa YKatsuma R(2023)A Method to Estimate Obstacle Presence on Narrow Roads Using Smartwatch-Based Multi- Cycling DataProceedings of the 2023 11th International Conference on Information Technology: IoT and Smart City10.1145/3638985.3639025(247-254)Online publication date: 14-Dec-2023
https://dl.acm.org/doi/10.1145/3638985.3639025
Baraka AMohd Noor M(2023)Similarity Segmentation Approach for Sensor-Based Activity RecognitionIEEE Sensors Journal10.1109/JSEN.2023.329577823:17(19704-19716)Online publication date: 1-Sep-2023
https://doi.org/10.1109/JSEN.2023.3295778

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cover image ACM Conferences

WearSys '18: Proceedings of the 4th ACM Workshop on Wearable Systems and Applications

June 2018

64 pages

ISBN:9781450358422

DOI:10.1145/3211960

Program Chairs:
Kaspar Jansen
TU Delft, Netherlands
,
Ashwin Ashok
Georgia State University

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 10 June 2018

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MobiSys '18: The 16th Annual International Conference on Mobile Systems, Applications, and Services

June 10, 2018

Munich, Germany

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Cited By

Baraka AMohd Noor M(2024)Deep similarity segmentation model for sensor-based activity recognitionMultimedia Tools and Applications10.1007/s11042-024-18933-2Online publication date: 4-May-2024
https://doi.org/10.1007/s11042-024-18933-2
Kanbetsunawa YKatsuma R(2023)A Method to Estimate Obstacle Presence on Narrow Roads Using Smartwatch-Based Multi- Cycling DataProceedings of the 2023 11th International Conference on Information Technology: IoT and Smart City10.1145/3638985.3639025(247-254)Online publication date: 14-Dec-2023
https://dl.acm.org/doi/10.1145/3638985.3639025
Baraka AMohd Noor M(2023)Similarity Segmentation Approach for Sensor-Based Activity RecognitionIEEE Sensors Journal10.1109/JSEN.2023.329577823:17(19704-19716)Online publication date: 1-Sep-2023
https://doi.org/10.1109/JSEN.2023.3295778

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