Data Mining and Fusion of Unobtrusive Sensing Solutions for Indoor Activity Recognition

Idongesit Ekerete, Matias Garcia-Constantino, Yohanca Diaz, Oonagh Giggins, Mustafa Mustafa, Alexandros Konios, Pierre Pouliet, James McLaughlin

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

Abstract

This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen: (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of: (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.
Original languageEnglish
Title of host publication2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
PublisherIEEE Xplore
Pages5357-5361
Number of pages5
ISBN (Electronic)978-1-7281-1990-8
ISBN (Print)978-1-7281-1991-5
DOIs
Publication statusPublished - 27 Aug 2020
Event2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) - Montreal, QC, Canada, Montreal, Canada
Duration: 20 Jul 202024 Jul 2020
Conference number: 42

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2020-July
ISSN (Print)1557-170X

Conference

Conference2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Abbreviated titleEMBC2020
CountryCanada
CityMontreal
Period20/07/2024/07/20

Keywords

  • Thermal Sensors
  • Sensor Fusion
  • Monitoring
  • Data Mining
  • Activity Recognition
  • Accelerometers

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