Dementia Wandering Recognition using Classical Machine Learning and Deep Learning Techniques with Skeletal Trajectories
- Resource Type
- Authors
- Bulat Khaertdinov; Stylianos Asteriadis; Yusuf Can Semerci
- Source
- PETRA
PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference, 446-452
STARTPAGE=446;ENDPAGE=452;TITLE=PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference
- Subject
- Movement (music)
Computer science
business.industry
Deep learning
02 engineering and technology
medicine.disease
Machine learning
computer.software_genre
03 medical and health sciences
0302 clinical medicine
0202 electrical engineering, electronic engineering, information engineering
medicine
Dementia
020201 artificial intelligence & image processing
Trajectory analysis
Artificial intelligence
Macro
business
computer
030217 neurology & neurosurgery
- Language
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.