Detection of eating and drinking arm gestures using inertial body-worn sensors

O.D. Amft; H. Junker; G. Tröster

doi:10.1109/ISWC.2005.17

Detection of eating and drinking arm gestures using inertial body-worn sensors

O.D. Amft
, H. Junker
, G. Tröster

Signal Processing Systems

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

166 Citations (Scopus)

600 Downloads (Pure)

Abstract

We propose a two-stage recognition system for detecting arm gestures related to human meal intake. Information retrieved from such a system can be used for automatic dietary monitoring in the domain of behavioural medicine. We demonstrate that arm gestures can be clustered and detected using inertial sensors. To validate our method, experimental results including 384 gestures from two subjects are presented. Using isolated discrimination based on HMMs an accuracy of 94% can be achieved. When spotting the gestures in continous movement data, an accuracy of up to 87% is reached. © 2005 IEEE.

Original language	English
Title of host publication	Proceedings 9th IEEE International Symposium on Wearable Computers, ISWC 2005, 18 October 2005 through 21 October 2005, Osaka
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	160-163
ISBN (Print)	0-7695-2419-2
DOIs	https://doi.org/10.1109/ISWC.2005.17
Publication status	Published - 2005

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SDG 3 Good Health and Well-being

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10.1109/ISWC.2005.17

Metis248471Final published version, 166 KB

Cite this

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title = "Detection of eating and drinking arm gestures using inertial body-worn sensors",

abstract = "We propose a two-stage recognition system for detecting arm gestures related to human meal intake. Information retrieved from such a system can be used for automatic dietary monitoring in the domain of behavioural medicine. We demonstrate that arm gestures can be clustered and detected using inertial sensors. To validate our method, experimental results including 384 gestures from two subjects are presented. Using isolated discrimination based on HMMs an accuracy of 94\% can be achieved. When spotting the gestures in continous movement data, an accuracy of up to 87\% is reached. {\textcopyright} 2005 IEEE.",

author = "O.D. Amft and H. Junker and G. Tr{\"o}ster",

year = "2005",

doi = "10.1109/ISWC.2005.17",

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Detection of eating and drinking arm gestures using inertial body-worn sensors. / Amft, O.D.; Junker, H.; Tröster, G.
Proceedings 9th IEEE International Symposium on Wearable Computers, ISWC 2005, 18 October 2005 through 21 October 2005, Osaka. Piscataway: Institute of Electrical and Electronics Engineers, 2005. p. 160-163.

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

TY - GEN

T1 - Detection of eating and drinking arm gestures using inertial body-worn sensors

AU - Amft, O.D.

AU - Junker, H.

AU - Tröster, G.

PY - 2005

Y1 - 2005

N2 - We propose a two-stage recognition system for detecting arm gestures related to human meal intake. Information retrieved from such a system can be used for automatic dietary monitoring in the domain of behavioural medicine. We demonstrate that arm gestures can be clustered and detected using inertial sensors. To validate our method, experimental results including 384 gestures from two subjects are presented. Using isolated discrimination based on HMMs an accuracy of 94% can be achieved. When spotting the gestures in continous movement data, an accuracy of up to 87% is reached. © 2005 IEEE.

AB - We propose a two-stage recognition system for detecting arm gestures related to human meal intake. Information retrieved from such a system can be used for automatic dietary monitoring in the domain of behavioural medicine. We demonstrate that arm gestures can be clustered and detected using inertial sensors. To validate our method, experimental results including 384 gestures from two subjects are presented. Using isolated discrimination based on HMMs an accuracy of 94% can be achieved. When spotting the gestures in continous movement data, an accuracy of up to 87% is reached. © 2005 IEEE.

U2 - 10.1109/ISWC.2005.17

DO - 10.1109/ISWC.2005.17

M3 - Conference contribution

SN - 0-7695-2419-2

SP - 160

EP - 163

BT - Proceedings 9th IEEE International Symposium on Wearable Computers, ISWC 2005, 18 October 2005 through 21 October 2005, Osaka

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -

Detection of eating and drinking arm gestures using inertial body-worn sensors

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