Wearable sensor-based activity recognition for housekeeping task

Kai Chun Liu, Chien Yi Yen, Li Han Chang, Chia Yeh Hsieh, Chia Tai Chan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

In order to improve healthcare services and support clinical professionals, it is important to develop the unobstructive and automatic ADLs monitoring system for healthcare applications. Currently, various works have been developed for the monitoring of daily activities, such as ambulation, kitchen task, food and fluid intake, dressing, and medication intake while only few works paid attention to the housekeeping task. Housekeeping activity is a complex task, generally important for the several clinical assessment tools. In this work, we design and develop a wearable sensor-based activity recognition system recognize housekeeping tasks and classify the activity level. The proposed system achieves 90.67% accuracy for housekeeping tasks recognition, and 94.35% accuracy for activity level classification, respectively. The results of the experiment demonstrate that the system is reliable and fulfills the requirements of the unobstructive, objective, and long-Term monitoring system.

Original languageEnglish
Title of host publication2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages67-70
Number of pages4
ISBN (Electronic)9781509062447
DOIs
StatePublished - 30 May 2017
Event14th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017 - Eindhoven, Netherlands
Duration: 9 May 201712 May 2017

Publication series

Name2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017

Conference

Conference14th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017
Country/TerritoryNetherlands
CityEindhoven
Period9/05/1712/05/17

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