Explainable AI for Wearable Health Data: Making Multivariate Time‑Series Models Transparent

A new study from our research network introduces an attention‑based xAI framework that classifies—and, more importantly, explains—multivariate data from wearables. By combining self‑attention with graph attention layers (GAT), the model captures both temporal patterns and cross‑sensor relationships, turning black‑box predictions into clinician‑ready insights. Key Takeaways At Centralive, we empower researchers to collect rich wearable data […]