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Design and implementation of algorithm for estimation of elevator travel distance using smartphone accelerometer

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UbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers

Pages 1489 - 1497

https://doi.org/10.1145/2800835.2801620

Published: 07 September 2015 Publication History

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Abstract

Since the advent of smartphones equipped with sophisticated sensing hardware, human activity recognition research has moved from utilizing dedicated sensing devices to using commercial smartphones. This paper presents the design of an algorithm to recognize and estimate travel distance when riding an elevator and its corresponding implementation within an app for a smartphone running Apple's mobile operating system (iOS). The algorithm receives solely the signal of the smartphone's accelerometer, recognizes that it belongs to an elevator ride, and proceeds to calculate the distance traveled by the rider. The algorithm has been designed in a way that simplifies the necessary operations to calculate the travel distance with the objective of minimizing processing power, while keeping the corresponding estimations highly accurate. This is an initial attempt towards the building of a robust, but simple and fast, real-time human activity recognition service on a wearable platform.

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Brezmes, T., Gorricho, J. L., and Cotrina, J. 2009. Activity Recognition from accelerometer data on mobile phones, IWANN '09: Proceedings of the 10th International Workshop Conference on Artificial Neural Networks, 796--799.

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Tianhui Yang, Katsuhiko Kaji, and Nobuo Kawaguchi. 2013. Elevator acceleration sensing: Design and estimation recognition algorithm using crowdsourcing. CDS 2013: The 1st IEEE International Workshop on Consumer Devices and Systems.

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Cited By

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Zhu Y(2022)Lift Activities Monitoring by using Low-cost Motion Sensors2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA)10.1109/ICIEA54703.2022.10006190(1360-1363)Online publication date: 16-Dec-2022
https://doi.org/10.1109/ICIEA54703.2022.10006190
Gligoric NKrco SDrajic D(2021)Digital transformation in Industry 4.0 Using Vibration Sensors and Machine Learning2021 International Balkan Conference on Communications and Networking (BalkanCom)10.1109/BalkanCom53780.2021.9593121(148-151)Online publication date: 20-Sep-2021
https://doi.org/10.1109/BalkanCom53780.2021.9593121
Bajaj GSingh P(2017)MewProceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications10.1145/3139243.3139246(19-24)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3139243.3139246

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Design and implementation of algorithm for estimation of elevator travel distance using smartphone accelerometer

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Information & Contributors

Information

Published In

September 2015

1626 pages

ISBN:9781450335751

DOI:10.1145/2800835

General Chairs:
Kenji Mase
Nagoya University, JP
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2015

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UbiComp '15

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Yahoo! Japan
SIGMOBILE
FX Palo Alto Laboratory, Inc.
ACM
Rakuten Institute of Technology
Microsoft
Bell Labs
SIGCHI
Panasonic
Telefónica
ISTC-PC

UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

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

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Cited By

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Zhu Y(2022)Lift Activities Monitoring by using Low-cost Motion Sensors2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA)10.1109/ICIEA54703.2022.10006190(1360-1363)Online publication date: 16-Dec-2022
https://doi.org/10.1109/ICIEA54703.2022.10006190
Gligoric NKrco SDrajic D(2021)Digital transformation in Industry 4.0 Using Vibration Sensors and Machine Learning2021 International Balkan Conference on Communications and Networking (BalkanCom)10.1109/BalkanCom53780.2021.9593121(148-151)Online publication date: 20-Sep-2021
https://doi.org/10.1109/BalkanCom53780.2021.9593121
Bajaj GSingh P(2017)MewProceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications10.1145/3139243.3139246(19-24)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3139243.3139246

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Index Terms

Recommendations

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

UIWear: Easily Adapting User Interfaces for Wearable Devices

Indoor-outdoor activity recognition by a smartphone