Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery
Dingrong Wang,u00a0Deep Shankar Pandey,u00a0Krishna Prasad Neupane,u00a0Zhiwei Yu,u00a0Ervine Zheng,u00a0Zhi Zheng,u00a0Qi Yu
Machine learning-driven human behavior analysis is gaining attention in behavioral/mental healthcare, due to its potential to identify behavioral patterns that cannot be recognized by traditional assessments. Real-life applications, such as digital behavioral biomarker identification, often require the discovery of complex spatiotemporal patterns in multimodal data, which is largely under-explored. To fill this gap, we propose a novel model that integrates uniquely designed Deep Temporal Sets (DTS) with Evidential Reinforced Attentions (ERA). DTS captures complex temporal relationships in the input and generates a set-based representation, while ERA captures the policy networku2019s uncertainty and conducts evidence-aware exploration to locate attentive regions in behavioral data. Using child-computer interaction data as a testing platform, we demonstrate the effectiveness of DTS-ERA in differentiating children with Autism Spectrum Disorder and typically developing children based on sequential multimodal visual and touch behaviors. Comparisons with baseline methods show that our model achieves superior performance and has the potential to provide objective, quantitative, and precise analysis of complex human behaviors.
