research-article

The Mobile Sensing Platform: An Embedded Activity Recognition System

Authors:

Predrag "Pedja" Klasnja,

Adam Rea, and

Danny WyattAuthors Info & Claims

IEEE Pervasive Computing, Volume 7, Issue 2

Pages 32 - 41

https://doi.org/10.1109/MPRV.2008.39

Published: 01 April 2008 Publication History

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Abstract

The Mobile Sensing Platform (MSP) is a small-form-factor wearable device designed for embedded activity recognition. The MSP aims broadly to support context-aware ubiquitous computing applications. It incorporates multimodal sensing, data processing and inference, storage, all-day battery life, and wireless connectivity into a single 4 oz (115 g) wearable unit. Several design iterations and real-world deployments over the last four years have identified a set of core hardware and software requirements for a mobile inference system. This article presents findings and lessons learned in the course of designing, improving and using this system. This article is part of a special issue on activity-based computing.

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Assi KMeegahapola LDroz WKun PDe Götzen ABidoglia MStares SGaskell GChagnaa AGanbold AZundui TCaprini CMiorandi DZarza JHume ACernuzzi LBison IRodas Britez MBusso MChenu-Abente RGiunchiglia FGatica-Perez D(2023)Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UKProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581190(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581190
Gomaa WKhamis M(2023)A perspective on human activity recognition from inertial motion dataNeural Computing and Applications10.1007/s00521-023-08863-935:28(20463-20568)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.1007/s00521-023-08863-9
Xu XZhang HSefidgar YRen YLiu XSeo WBrown JKuehn KMerrill MNurius PPatel SAlthoff TMorris MRiskin EMankoff JDey AKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)GLOBEM datasetProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602060(24655-24692)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602060
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Index Terms

The Mobile Sensing Platform: An Embedded Activity Recognition System
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems
2. Computing methodologies
  1. Machine learning

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Published In

IEEE Pervasive Computing Volume 7, Issue 2

April 2008

95 pages

ISSN:1536-1268

Issue’s Table of Contents

Publisher

IEEE Educational Activities Department

United States

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Published: 01 April 2008

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Assi KMeegahapola LDroz WKun PDe Götzen ABidoglia MStares SGaskell GChagnaa AGanbold AZundui TCaprini CMiorandi DZarza JHume ACernuzzi LBison IRodas Britez MBusso MChenu-Abente RGiunchiglia FGatica-Perez D(2023)Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UKProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581190(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581190
Gomaa WKhamis M(2023)A perspective on human activity recognition from inertial motion dataNeural Computing and Applications10.1007/s00521-023-08863-935:28(20463-20568)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.1007/s00521-023-08863-9
Xu XZhang HSefidgar YRen YLiu XSeo WBrown JKuehn KMerrill MNurius PPatel SAlthoff TMorris MRiskin EMankoff JDey AKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)GLOBEM datasetProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602060(24655-24692)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602060
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Gu FChung MChignell MValaee SZhou BLiu X(2021)A Survey on Deep Learning for Human Activity RecognitionACM Computing Surveys10.1145/347229054:8(1-34)Online publication date: 4-Oct-2021
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Recommendations

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