Unobtrusive recognition of activities of daily living using thermal sensor data for monitoring a smart environment

Student thesis: Doctoral Thesis


With the global population constantly increasing, including the population of elderly people, there is an increasing need for facilitating independent and comfortable living. The degree of at-home monitoring that is necessary to deliver sufficient independence can be provided using automated sensor technology built within smart environments. Sensors can be used to monitor a home by analysing the activities of the environment’s inhabitant. It is important, however, to consider the preservation of privacy and so the unobtrusiveness of such a system should be considered as vital a characteristic as its accuracy.

This thesis proposes an unobtrusive approach for detecting and recognising Activities of Daily Living (ADLs) performed within a smart environment. The research that is presented involved the use of low-resolution thermal sensors as the only means of data capture. The sensors have been used to develop original datasets for training and testing the various components of the proposed approach. The four primary components of the proposed approach are presented as four complementary chapters in this thesis. Firstly, an unobtrusive approach to recognising full body poses with thermal imagery is presented. Several machine learning algorithms were tested and their performances are compared. The second study introduces the concept of subactivities and how they can be inferred from the thermal data using only the predicted pose and the object estimated to be closest to the inhabitant. Additional sensors are considered in the third study where the effect of deep learning is investigated in order to improve upon the pose recognition performance. Lastly, the various components are combined to present the approach to detecting and recognising ADLs in a manner which exhibits both accuracy and privacy.
Date of AwardJan 2022
Original languageEnglish
SponsorsDepartment for the Economy
SupervisorChristopher Nugent (Supervisor), Sally McClean (Supervisor) & Philip Morrow (Supervisor)


  • CNN
  • Pose recognition
  • Machine learning
  • Deep learning
  • Computer vision
  • infrared
  • Activity recognition

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