Get Out of Bed (GOOB): A Proactive, AI-Enabled Somatic Intervention for Heavy Mornings

PERSONAL PROJECT (IN PROGRESS)

 

Get Out of Bed (GOOB): A Proactive, AI-Enabled Somatic Intervention for Heavy Mornings

PERSONAL PROJECT (IN PROGRESS)

THE CHALLENGE

Explore how an AI system can help people initiate action during emotionally difficult mornings—when motivation, executive function, and self-regulation are impaired—without relying on willpower, screens, or intrusive prompts.

ROLE

Founder (doing Conversation Design, User Research, & Prototyping)

THE OUTCOME

A proactive, voice-based AI companion that guides users through a staged transition from lying in bed to embodied action—sitting up, standing, and completing one small, grounding morning task. The system uses emotionally attuned language and somatic micro-interventions to reduce friction and help users begin their day without overwhelm, dread, or stress.

For people experiencing burnout, depression, grief, or overwhelm, the hardest part of the day is often the first 60–90 seconds after waking. Traditional alarms ignore emotional state, rely on screens, and assume cognitive motivation—failing precisely when the nervous system is dysregulated. Existing mental-health tools require users to initiate help, articulate feelings, or engage cognitively at moments when that capacity is lowest.

Get Out of Bed (GOOB) is a voice-first, emotionally attuned AI companion designed to help users transition from waking to action through gentle somatic guidance and contextual emotional support. Rather than therapy or motivation, the system focuses on embodied activation: move from lying in bed to sitting up, standing, and ultimately completing one small, meaningful morning action (e.g., drinking water, opening blinds) when they feel “stuck.”

How It Works
At wake time, GOOB initiates a short voice interaction that:

  • checks in on how the user is feeling

  • classifies emotional friction into one of six categories (burnout, grief, overwhelm, depression, stress, or dread)

  • delivers a brief, trauma-informed somatic micro-sequence appropriate to that state to guide the user from lying → sitting → standing

  • supports completion of a single morning action

The system combines a structured state machine, rule-based NLU for safety-critical detection (e.g., crisis language), and an LLM for empathetic, context-sensitive responses. Tone, pacing, and boundaries are deliberately constrained to avoid over-dependence, clinical framing, or toxic positivity.

This work is still in progress, but I will be measuring: morning activation rate, morning engagement rate, emption classification coverage, completion rate, interruption handling accuracy, session duration, safety trigger rate, and return rate—designed to evaluate whether gentle, proactive AI can reliably support initiation and follow-through without increasing cognitive load. Early prototyping demonstrates feasibility of low-friction voice interaction, emotion classification coverage, and safe handling of distress and interruptions within a 3–5 minute session.

Why this work matters: GOOB explores a critical but under-studied moment in human–AI interaction: when people lack the capacity to seek help but still need support. The project contributes a novel design paradigm for proactive, emotionally responsible AI—one that combines somatic psychology, conversational interaction, and strict ethical boundaries to support human agency rather than replace it. This work directly informs research on contextual inference, embodied AI, and human flourishing, with implications for mental health, wellbeing, and everyday human–AI collaboration.