Learning ADL Daily Routines with Spatiotemporal Neural Networks

Activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person's habits, lifestyle, and wellbeing. Consequently, learning an individual's ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and inter-ADL pattern learning problems have been studied extensively in the past couple of decades. However, discovering the patterns of ADLs performed in a day and clustering them into ADL daily routines has been a relatively unexplored research area. In this paper, a self-organizing neural network model, called the Spatiotemporal ADL Adaptive Resonance Theory (STADLART), is proposed for learning ADL daily routines. STADLART integrates multimodal contextual information that involves the time and space wherein the ADLs are performed. By encoding spatiotemporal information explicitly as input features, STADLART enables the learning of time-sensitive knowledge. Moreover, a STADLART variation named STADLART-NC is proposed to normalize and customize ADL weighting for daily routine learning. A weighting assignment scheme is presented that facilitates the assignment of weighting according to ADL importance in specific domains. Empirical experiments using both synthetic and real-world public data sets validate the performance of STADLART and STADLART-NC when compared with alternative pattern discovery methods. The results show that STADLART could cluster ADL routines with better performance than baseline algorithms.