Wearable system decides when to act based on personal signals
Affective Agent: On-Device Personalized Intervention Reasoning for Wearable Systems
Artificial IntelligenceMachine Learning
Summary
Wearable devices can sense how we feel but figuring out when and how to help or alert us is tricky, especially without relying on the cloud. The authors created Affective Agent, a system that uses a small built-in language model combined with body signals, surroundings, and past behavior to make smart decisions on the device itself. It learns about each person by updating stored memories instead of retraining the whole system. They tested it in simulated indoor environments and found it improves when and how the device suggests interventions.
What this means in practice
- •For wearable device engineers: Build wearable systems that decide personalized interventions without cloud reliance or retraining for each user.$Commercial implications: Enables companies to sell smart wearables that act on personal data privately on-device with adaptable, user-specific decisions.
- •For smart building managers: Use wearable-based feedback to adjust indoor environmental quality interventions tailored to individuals over time.
Tested on simulated data.
Authors
Reina Mun, Zishen Wan, Vijay Janapa Reddi
Abstract
Affective computing has advanced wearable state inference, but on-device reasoning about whether, when, and how to intervene remains challenging. We present Affective Agent, a three-layer reference architecture for personalized intervention reasoning under uncertainty on wearable-class hardware. It combines a compact sub-billion-parameter language model with physiological evidence, context, and user history to decide whether, when, and how to intervene, without cloud dependency or per-user retraining. The architecture is organized into three interacting layers (perception, personalization, and reasoning), adapting to individual users through host-managed structured memory evolution rather than per-user weight updates. We instantiate Affective Agent in indoor environmental quality control and evaluate it on held-out, simulator-generated longitudinal scenarios spanning physiological variation, context, signal quality, and intervention history. Results show that memory-driven personalization and two-pass structured reasoning improve intervention decisions within this synthetic evaluation. By moving the decision layer on-device, this work demonstrates a path from wearable state inference toward closed-loop, personalized intervention on wearable-class hardware.