poster

Activity recognition and nutrition monitoring in every day situations with a textile capacitive neckband

Authors:

Jingyuan Cheng,

Bo Zhou,

Kai Kunze,

Carl Christian Rheinländer,

Sebastian Wille,

Norbert Wehn,

Jens Weppner,

Paul LukowiczAuthors Info & Claims

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

Pages 155 - 158

https://doi.org/10.1145/2494091.2494143

Published: 08 September 2013 Publication History

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Abstract

We build on previous work [5] that demonstrated, in simple isolated experiments, how head and neck related events (e.g. swallowing, head motion) can be detected using an unobtrusive, textile capacitive sensor integrated in a collar like neckband. We have now developed a 2nd generation that allows long term recording in real life environments in conjunction with a low power Bluetooth enabled smart phone. It allows the system to move from the detection of individual swallows which is too unreliable for practical applications to an analysis of the statistical distribution of swallow frequency. Such an analysis allows the detection of "nutrition events" such as having lunch or breakfast. It also allows us to see the general level of activity and distinguish between just being absolutely quiet (no motion) and sleeping. The neckband can be useful in a variety of applications such as cognitive disease monitoring and elderly care.

References

[1]

Amft, O., Junker, H., and Troster, G. Detection of eating and drinking arm gestures using inertial body-worn sensors. In Proc. IEEE ISWC (2005), 160--163.

Digital Library

Google Scholar

[2]

Amft, O., Stäger, M., Lukowicz, P., and Tröster, G. Analysis of chewing sounds for dietary monitoring. In UbiComp 2005: Ubiquitous Computing. Springer, 2005, 56--72.

Digital Library

Google Scholar

[3]

Amft, O., and Troster, G. On-body sensing solutions for automatic dietary monitoring. Pervasive Computing, IEEE 8, 2 (2009), 62--70.

Digital Library

Google Scholar

[4]

Arab, L., Estrin, D., Kim, D. H., Burke, J., and Goldman, J. Feasibility testing of an automated image-capture method to aid dietary recall. European journal of clinical nutrition 65, 10 (2011), 1156--1162.

Google Scholar

[5]

Cheng, J., Amft, O., and Lukowicz, P. Active capacitive sensing: Exploring a new wearable sensing modality for activity recognition. In Pervasive Computing. Springer, 2010, 319--336.

Digital Library

Google Scholar

[6]

Liu, J., Johns, E., Atallah, L., Pettitt, C., Lo, B., Frost, G., and Yang, G.-Z. An intelligent food-intake monitoring system using wearable sensors. In Proc. BSN 2012, IEEE (2012), 154--160.

Digital Library

Google Scholar

[7]

Stellar, E., and Shrager, E. E. Chews and swallows and the microstructure of eating. The American Journal of Clinical Nutrition 42, 5 (1985), 973--982.

Crossref

Google Scholar

[8]

Tacconi, D., Mayora, O., Lukowicz, P., Arnrich, B., Setz, C., Troster, G., and Haring, C. Activity and emotion recognition to support early diagnosis of psychiatric diseases. In Proc. PervasiveHealth 2008, IEEE (2008), 100--102.

Google Scholar

Cited By

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Bian SLiu MZhou BLukowicz PMagno M(2024)Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435558:1(1-49)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643555
Liu MZhou BRey VBian SLukowicz P(2024)iEat: automatic wearable dietary monitoring with bio-impedance sensingScientific Reports10.1038/s41598-024-67765-514:1Online publication date: 2-Aug-2024
https://doi.org/10.1038/s41598-024-67765-5
Bello HGeißler DSuh SZhou BLukowicz P(2024)TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing LinesPattern Recognition10.1007/978-3-031-78389-0_14(201-216)Online publication date: 5-Dec-2024
https://doi.org/10.1007/978-3-031-78389-0_14
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Index Terms

Activity recognition and nutrition monitoring in every day situations with a textile capacitive neckband
1. Computing methodologies
  1. Artificial intelligence

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Published In

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

September 2013

1608 pages

ISBN:9781450322157

DOI:10.1145/2494091

General Chairs:
Friedemann Mattern
ETH Zurich, CH
,
Silvia Santini
TU Darmstadt, DE
,
Program Chairs:
John F. Canny
UC Berkeley, US
,
Marc Langheinrich
Università della Svizzera italiana, CH
,
Jun Rekimoto
University of Tokyo, JP

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2013

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UbiComp '13

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

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UbiComp '13 Adjunct Paper Acceptance Rate 254 of 399 submissions, 64%;

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

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Cited By

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Bian SLiu MZhou BLukowicz PMagno M(2024)Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435558:1(1-49)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643555
Liu MZhou BRey VBian SLukowicz P(2024)iEat: automatic wearable dietary monitoring with bio-impedance sensingScientific Reports10.1038/s41598-024-67765-514:1Online publication date: 2-Aug-2024
https://doi.org/10.1038/s41598-024-67765-5
Bello HGeißler DSuh SZhou BLukowicz P(2024)TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing LinesPattern Recognition10.1007/978-3-031-78389-0_14(201-216)Online publication date: 5-Dec-2024
https://doi.org/10.1007/978-3-031-78389-0_14
Hiraguchi HPerone PToet ACamps GBrouwer A(2023)Technology to Automatically Record Eating Behavior in Real Life: A Systematic ReviewSensors10.3390/s2318775723:18(7757)Online publication date: 8-Sep-2023
https://doi.org/10.3390/s23187757
Wu YChiang HShirmohammadi SHsu CZimmermann RShirmohammadi SGriwodz CHefeeda MWang M(2023)A Dataset of Food Intake Activities Using Sensors with Heterogeneous Privacy Sensitivity LevelsProceedings of the 14th Conference on ACM Multimedia Systems10.1145/3587819.3592553(416-422)Online publication date: 7-Jun-2023
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Zhou BGeissler DFaulhaber MGleiss CZahn ERay LGamarra DRey VSuh SBian SJoost GLukowicz P(2023)MoCaPoseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808837:1(1-40)Online publication date: 28-Mar-2023
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Yu TArakawa RMcCann JGoel M(2023)uKnit: A Position-Aware Reconfigurable Machine-Knitted Wearable for Gestural Interaction and Passive Sensing using Electrical Impedance TomographyProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580692(1-17)Online publication date: 19-Apr-2023
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Khare ANene M(2023)A Comprehensive Survey on Artificial Intelligence Based Methods and Technologies Used to Detect Nutrition Level (Malnourishment)2023 IEEE International Carnahan Conference on Security Technology (ICCST)10.1109/ICCST59048.2023.10726846(1-6)Online publication date: 11-Oct-2023
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Vicente-Martínez JAlmaida-Pagan PMartinez-Nicolas AMadrid JRol MBonmatí-Carrión M(2023)Integral Analysis of Circadian RhythmsStatistical Methods at the Forefront of Biomedical Advances10.1007/978-3-031-32729-2_9(185-236)Online publication date: 10-Aug-2023
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Lacanlale JIsayan PMkrtchyan KNahapetian A(2022)Sensoring the Neck: Classifying Movements and Actions with a Neck-Mounted Wearable DeviceSensors10.3390/s2212431322:12(4313)Online publication date: 7-Jun-2022
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