Toward seamless wearable sensing: Automatic on-body sensor localization for physical activity monitoring

Ramyar Saeedi; Janet Purath; Krishna Venkatasubramanian; Hassan Ghasemzadeh

doi:10.1109/EMBC.2014.6944843

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Toward seamless wearable sensing: Automatic on-body sensor localization for physical activity monitoring

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Toward seamless wearable sensing: Automatic on-body sensor localization for physical activity monitoring

Ramyar Saeedi, Janet Purath, Krishna Venkatasubramanian and Hassan Ghasemzadeh

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol.2014, pp.5385-5388

08/2014

DOI: https://doi.org/10.1109/EMBC.2014.6944843

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https://hdl.handle.net/2376/114858

PMID: 25571211

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Abstract

Legged locomotion

Accuracy

Signal processing algorithms

Feature extraction

Biomedical monitoring

Wearable sensors

Monitoring

Mobile wearable sensors have demonstrated great potential in a broad range of applications in healthcare and wellness. These technologies are known for their potential to revolutionize the way next generation medical services are supplied and consumed by providing more effective interventions, improving health outcomes, and substantially reducing healthcare costs. Despite these potentials, utilization of these sensor devices is currently limited to lab settings and in highly controlled clinical trials. A major obstacle in widespread utilization of these systems is that the sensors need to be used in predefined locations on the body in order to provide accurate outcomes such as type of physical activity performed by the user. This has reduced users' willingness to utilize such technologies. In this paper, we propose a novel signal processing approach that leverages feature selection algorithms for accurate and automatic localization of wearable sensors. Our results based on real data collected using wearable motion sensors demonstrate that the proposed approach can perform sensor localization with 98.4% accuracy which is 30.7% more accurate than an approach without a feature selection mechanism. Furthermore, utilizing our node localization algorithm aids the activity recognition algorithm to achieve 98.8% accuracy (an increase from 33.6% for the system without node localization).

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Title: Toward seamless wearable sensing: Automatic on-body sensor localization for physical activity monitoring
Creators: Ramyar Saeedi - Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
Janet Purath - Coll. of Nursing, Washington State Univ., Spokane, WA, USA
Krishna Venkatasubramanian - Comput. Sci. Dept., Worcester Polytech. Inst., Worcester, MA, USA
Hassan Ghasemzadeh - Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
Publication Details: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol.2014, pp.5385-5388
Academic Unit: Nursing, College of; Electrical Engineering and Computer Science, School of
Publisher: IEEE
Identifiers: 99900547574301842
Language: English
Resource Type: Conference proceeding