Dementia Wandering Recognition using Classical Machine Learning and Deep Learning Techniques with Skeletal Trajectories

Bulat Khaertdinov*, Yusuf Can Semerci, Stelios Asteriadis

*Corresponding author for this work

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


Wandering is considered to be one of the most common behavioral symptoms of dementia. Designing robust models which are capable of detecting wandering episodes in people living with dementia would allow the prevention of the consequences of this behavior. To tackle this problem, this study proposes a framework where the skeletal trajectories are used to extract patterns from the movements of the participants. These patterns are utilized to classify a movement between wandering and non-wandering behavior using three machine learning methods. The proposed models were assessed based on two datasets collected in different environments consisting of trajectories that are associated with lapping, pacing, and random movements that represent wandering episodes. The predictive model based on the LSTM network achieved the best classification results in terms of macro F1-scores on both datasets with an overall accuracy higher than 70%. The findings of this study present the potential of LSTM-based predictive models in addressing the wandering recognition problem in a real-world scenario with patients suffering from dementia.
Original languageEnglish
Title of host publicationPETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference
PublisherAssociation for Computing Machinery
Number of pages7
ISBN (Electronic)978-1-4503-8792-7
Publication statusPublished - 29 Jun 2021
EventThe 14th ACM International Conference on PErvasive Technologies Related to Assistive Environments - Virtual Conference (University of Texas at Arlington, Texas, USA
Duration: 29 Jun 20211 Jul 2021


ConferenceThe 14th ACM International Conference on PErvasive Technologies Related to Assistive Environments
Abbreviated titlePETRA 2021
Internet address

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