Impact of sensor misplacement on estimating metabolic equivalent of task with wearables

Parastoo Alinia, Ramyar Saeedi, Bobak Mortazavi, Ali Rokni, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Metabolic equivalent of task (MET) indicates the intensity of physical activities. This measurement is used in providing physical activity intervention in many chronic illnesses such as coronary heart disease, type-2 diabetes, and cancer. Due to the small size, portability, low power consumption, and low cost, wearable motion sensors are widely used to estimate MET values. However, one major obstacle in widespread adoption of current wearable monitoring systems is that the sensors must be worn on predefined locations on the body. This imposes much discomfort for users as they are not allowed to wear the sensors on their own desired body locations. In addition, non-adherence to the predefined location of the sensors results in significant reduction in the accuracy of physical activity monitoring. In this paper, we propose a framework for sensor location-independent MET estimation. We introduce a sensor localization approach that allows users to wear the sensors on different body locations without having to adhere to a specific installation protocol. We study how such an algorithm impacts the performance of MET estimation algorithms. Using daily physical activity data, we demonstrate that an automatic sensor localization algorithm decreases the estimation error of the MET calculation by a factor of 2.3 compared to the case without sensor localization. Furthermore, our sensor localization algorithm achieves an accuracy of 90.8% in detecting on-body locations of wearable sensors. The integration of sensor localization and MET estimation achieves an accuracy of 80% in calculating the MET values of daily physical activities.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467372015
DOIs
StatePublished - Oct 15 2015
Externally publishedYes
Event12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015 - Cambridge, United States
Duration: Jun 9 2015Jun 12 2015

Publication series

Name2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015

Conference

Conference12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
Country/TerritoryUnited States
CityCambridge
Period6/9/156/12/15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Health Informatics
  • Instrumentation
  • Signal Processing

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