PAL for Behavior Change
Habit change is the future of health care, self-care, and self-change
- Create an evidence-based and context-aware platform for in-the-wild behavior monitoring and behavior change interventions in the real world.
- Implement reinforcement learning-based closed-loop behavior change interventions for personalized behavior change support.
- Provide interpretable human-in-the-loop models for low-shot and personalized context detection, context prediction, and behavior change interventions.
Healthy behaviors can help us not only prevent and manage health problems, but also achieve self-fulfillment and self-actualization. Behavior change is hard, however, as humans are constrained by their cognitive and emotional constraints. Humans have two types of thinking [Thinking, Fast and Slow]: system I is automatic and effortless, and system II is thoughtful and effortful. Many of our everyday actions are automatic and when automatic actions are repeated in stable contexts, they become habits, i.e., automatic and efficient responses to stable contexts. Successful behavior change, thus, requires context-aware habit change, but traditional behavior change resources, e.g., self-help books and therapists, are not present with us in our everyday lives.
- Enable, not enforce: PAL supports a range of behavior change goals and leverages the intrinsic motivation of users to pick their goals, interventions, contextual triggers, and self-tracking sensors. PAL’s self-tracking, goal planning, and contextual reminders aim to assist and empower everyday self-change.
- Supplement, not substitute: PAL supports habit formation by anchoring new behaviors in existing routines or by breaking old habits using context-aware reminders. Healthy habits enable long-term and sustainable behavior change so the users train their behavior change muscle and are not dependent on PAL.
- Connect, not confine: PAL makes behavior change an inclusive, not an isolated, process. Using PAL, users can exchange audio/text behavior support messages to serve as behavior change reminders and can also share their behavior change progress and goals. PAL aims to be an extension of ourselves and our support system to provide real-time behavior change support.
PAL’s behavior change platform has three key components:
- Wearable Device: The wearable device (Fig. 1) consists of self-tracking sensors (camera, heart-rate, and Inertial Measurement Unit), user input (microphone, button, and tap), user output (open-ear audio), and on-device deep learning accelerator (Google Coral USB Accelerator).