Major Depressive Disorder (MDD) requires continuous, longitudinal monitoring, yet current diagnostic methods rely on intermittent clinical visits. Ambient Assisted Living (AAL) environments offer a solution by enabling passive screening via acoustic biomarkers. However, a critical research gap exists in the deployability of state-of-the-art detection models within these environments. Existing Machine Learning (ML) approaches often rely on heavy GPU acceleration, making them computationally prohibitive for widespread deployment on standard medical or home-server infrastructure. To address this, this paper proposes a lightweight, service-oriented system architecture designed for integration into privacy-centric on-premise environments. We present a low-latency inference microservice that prioritizes engineering constraints, specifically CPU efficiency and transparency, over marginal accuracy gains. Leveraging the eGeMAPSv02 feature set and an optimized Random Forest classifier (F1=0.696), our system achieves an end-to-end inference latency of approximately 7.3s on standard commodity hardware, with the core inference engine executing in less than 120ms. This efficient architecture allows the service to be deployed as a containerized local network service, enabling scalable, privacy-conscious depression screening without the reliance on specialized hardware accelerators or cloud connectivity.
Oleh et al. (Thu,) studied this question.