Spike Memory Transformer: An Energy-Efficient Model in Distributed Learning Framework for Autonomous Depression Detection

Objective depression assessments based on physiological data offer new opportunities for clinical diagnostic support, but their usage is constrained by the limitations of data collection devices. Ubiquitous digital devices, now deeply integrated into everyday life, have the potential to effectively capture and represent digital phenotypes of depression. However, existing research in this field faces fundamental problems, such as device thresholds and insufficient utilization of distributed computational power, which have hindered significant research and application efforts in this area. In this article, we proposed the Computation-Oriented Hierarchical Depression Detection Internet of Things (IoT) Framework, which allows IoT devices to collaborate in a layered and distributed manner for psychological data collection and depression detection. In addition, we developed a depression detection model, i.e., spike memory transformer (SMT), which significantly reduces inference energy consumption, facilitating the deployment of depression detection capabilities across various IoT terminal devices. Experimental results demonstrate that our model achieved up to 70% accuracy on the D-Vlog dataset while reducing inference power consumption by an average of 38% compared to classical deep learning methods, thus validating the feasibility of proposed method.