Classification of wandering patterns in the elderly using machine learning and time series analysis
Keywords:
Dementia, wandering patterns, feature extraction, discrete wavelet transform, machine learningAbstract
Dementia has emerged as a significant health concern due to global aging trends. A degenerative brain disorder, dementia leads to cognitive decline, memory loss, impaired communication skills, reduced abilities, and shifts in personality and mood. Dementia lacks a definitive cure, but accurate diagnosis and treatment can improve the quality of life for those affected. Wandering behavior is common in patients, and a link between wandering patterns and the severity of the disease has been established. This work addresses the challenge of detecting dementia-related wandering behaviors. The proposed strategy utilizes data imputation methods and feature extraction with the Discrete Wavelet Transformation applied to a recently developed and comprehensive dataset. Machine learning algorithms are used to perform the final detection, and hyperparameter optimization is also evaluated.
Experiments show that performance achieves an accuracy of approximately 98\% using the Random Forest classifier. Results are competitive with the state-of-the-art in time series classification, with improved efficiency. The proposed methodology can be used for the development of applications for dementia related research and care.
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