Consumer-grade wearable devices generate vast streams of high-frequency physiological and behavioural data, yet this raw information is not directly interpretable or actionable within clinical mental healthcare workflows. This creates a significant gap between the potential of remote monitoring technology and its practical application. To address this challenge, we present a novel, multi-stage automated pipeline that transforms raw sensor data into longitudinal, clinically relevant insights. The pipeline integrates a deep learning model for Human Activity Recognition (HAR), a temporal “bout” analysis to contextualise physiological and behavioural events, and a Retrieval-Augmented Generation (RAG) enhanced Large Language Model (LLM) to ensure outputs are grounded in an external clinical knowledge base. We demonstrate the pipeline’s efficacy through a case study of a volunteer with panic disorder. The system successfully performed a week-over-week comparative analysis, identifying a nuanced shift from frequent, short bouts of physiological arousal to fewer, but more sustained and intense, episodes. From this analysis, the pipeline generated three distinct, audience-appropriate reports: a data-driven summary with treatment considerations for a psychologist, an empathetic summary for the volunteer, and a comprehensive analytical report. This work presents a viable end-to-end system for translating complex, continuous sensor data into actionable insights that can support and enhance mental healthcare.