poster

Detecting and differentiating leg bouncing behaviour from everyday movements using tri-axial accelerometer data

Authors:

Hashini Senaratne,

Glenn MelvinAuthors Info & Claims

UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

Pages 127 - 130

https://doi.org/10.1145/3410530.3414388

Published: 12 September 2020 Publication History

Abstract

Leg bouncing is assumed to be related to anxiety, engrossment, boredom, excitement, fatigue, impatience, and disinterest. Objective detection of this behaviour would enable researching its relation to different mental and emotional states. However, differentiating this behaviour from other movements is less studied. Also, it is less known which sensor placements are best for such detection. We collected recordings of everyday movements, including leg bouncing, from six leg bouncers using tri-axial accelerometers at three leg positions. Using a Random Forest Classifier and data collected at the ankle, we could obtain a 90% accuracy in the classification of the recorded everyday movements. Further, we obtained a 94% accuracy in classifying four types of leg bouncing. Based on the subjects' opinion on leg bouncing patterns and experience with wearables, we discuss future research opportunities in this domain.

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Cited By

Ghose DGitelson OScassellati B(2024)Integrating Multimodal Affective Signals for Stress Detection from Audio-Visual DataProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685717(22-32)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685717
Lambert TGazi AHarrison AGharehbaghi SChan MObideen MAlavi PMurrah NShallenberger LDriggers EAlvarado Ortega RWashington BWalton KTang YGupta RNye JWelsh JVaccarino VShah ABremner JInan O(2022)Leveraging Accelerometry as a Prognostic Indicator for Increase in Opioid Withdrawal SymptomsBiosensors10.3390/bios1211092412:11(924)Online publication date: 26-Oct-2022
https://doi.org/10.3390/bios12110924

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Published In

cover image ACM Conferences

UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

September 2020

732 pages

ISBN:9781450380768

DOI:10.1145/3410530

General Chairs:
Monica Tentori
CICESE, Mexico
,
Nadir Weibel
UC San Diego
,
Kristof Van Laerhoven
University of Siegen, Germany
,
Program Chairs:
Gregory Abowd
Georgia Tech
,
Flora Salim
RMIT, Australia

Copyright © 2020 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 12 September 2020

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UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers

September 12 - 17, 2020

Virtual Event, Mexico

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

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Cited By

Ghose DGitelson OScassellati B(2024)Integrating Multimodal Affective Signals for Stress Detection from Audio-Visual DataProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685717(22-32)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685717
Lambert TGazi AHarrison AGharehbaghi SChan MObideen MAlavi PMurrah NShallenberger LDriggers EAlvarado Ortega RWashington BWalton KTang YGupta RNye JWelsh JVaccarino VShah ABremner JInan O(2022)Leveraging Accelerometry as a Prognostic Indicator for Increase in Opioid Withdrawal SymptomsBiosensors10.3390/bios1211092412:11(924)Online publication date: 26-Oct-2022
https://doi.org/10.3390/bios12110924

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