research-article

Multi-sensor physical activity recognition in free-living

Authors:

Katherine Ellis,

Jacqueline Kerr,

Suneeta Godbole,

Gert LanckrietAuthors Info & Claims

UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

Pages 431 - 440

https://doi.org/10.1145/2638728.2641673

Published: 13 September 2014 Publication History

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Abstract

Physical activity monitoring in free-living populations has many applications for public health research, weight-loss interventions, context-aware recommendation systems and assistive technologies. We present a system for physical activity recognition that is learned from a free-living dataset of 40 women who wore multiple sensors for seven days. The multi-level classification system first learns low-level codebook representations for each sensor and uses a random forest classifier to produce minute-level probabilities for each activity class. Then a higher-level HMM layer learns patterns of transitions and durations of activities over time to smooth the minute-level predictions.

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Abdullayev JZingoni A(2023)Data Engineering Techniques for Efficient and Accurate Human Physical Activities Data Collection: a Summary of the State-of-the-art2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)10.1109/MetroXRAINE58569.2023.10405616(138-143)Online publication date: 25-Oct-2023
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Index Terms

Multi-sensor physical activity recognition in free-living
1. Computing methodologies
  1. Machine learning

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Published In

UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

September 2014

1409 pages

ISBN:9781450330473

DOI:10.1145/2638728

General Chairs:
AJ Brush
Microsoft Research
,
Adrian Friday
Lancaster University, UK
,
Program Chairs:
Julie Kientz
University of Washington
,
James Scott
Microsoft Research, UK
,
Junehwa Song
Korea Advanced Institute of Science and Technology (KAIST), KR

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

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Published: 13 September 2014

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UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing

September 13 - 17, 2014

Washington, Seattle

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Shang MDedeyne LDupont JVercauteren LAmini NLapauw LGielen EVerschueren SVaron CDe Raedt WVanrumste B(2024)Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning ModelsIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2024.335529932(462-471)Online publication date: 2024
https://doi.org/10.1109/TNSRE.2024.3355299
Shang MDedeyne LDupont JVercauteren LAmini NLapauw LGielen EVerschueren SVaron CDe Raedt WVanrumste B(2024)DS-MS-TCN: Otago Exercises Recognition With a Dual-Scale Multi-Stage Temporal Convolutional NetworkIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.345542628:12(7138-7150)Online publication date: Dec-2024
https://doi.org/10.1109/JBHI.2024.3455426
Abdullayev JZingoni A(2023)Data Engineering Techniques for Efficient and Accurate Human Physical Activities Data Collection: a Summary of the State-of-the-art2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)10.1109/MetroXRAINE58569.2023.10405616(138-143)Online publication date: 25-Oct-2023
https://doi.org/10.1109/MetroXRAINE58569.2023.10405616
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Abstract

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Cited By

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Physical activity recognition in free-living from body-worn sensors

Using wearable activity type detection to improve physical activity energy expenditure estimation

Smartphone-based monitoring system for activities of daily living for elderly people and their relatives etc.

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