docs/research/sota-2026-05-22/R14-empathic-appliances.md
Status: speculative 10-20y vision note · 2026-05-22
We already ship a contactless breathing-rate detector (v1/v2 sensing-server, ADR-029 multistatic fusion). Breathing rate is a documented proxy for arousal/stress in clinical studies (e.g. Bernardi 2002, Vlemincx 2013) and predicts user states finer than HRV in low-SNR conditions. Heart rate is captured concurrently.
The 10-20 year question: what happens when every appliance with a CPU and a WiFi radio knows the occupant's physiological baseline + current state, and modulates its behaviour to support the occupant's wellbeing?
The current RuView stack provides the sensing primitives (breathing rate, heart rate, occupancy, motion intensity, RSSI-only counting per R8). What it doesn't yet provide is the intent-action layer — an appliance that says "the occupant has been breathing fast for 8 minutes; their normal baseline is 12 BPM; let me dim the lights and lower the music."
| Sensing | Action |
|---|---|
| Breathing rate 50% above 7-day rolling baseline for >5 min | Lights gently warm-shift (Kelvin: 4000K → 2700K) and dim 10% over 60s |
| Sustained low motion + low breathing variability (rest state) | Lights stay where they are |
| Sleep onset detected (motion=null, breathing<10 BPM for >15 min) | Lights fade to 0 over 8 min following standard Philips Hue "wind down" curve |
The hard part is not the sensing — it's the personalisation: a 7-day rolling baseline takes a week of continuous occupancy data to calibrate, and per-person baselines vary by ~30%. Solution: federated per-room calibration that learns continuously, with explicit "this is not me" override.
Thermal stress affects breathing-rate envelope (>30°C → +20% baseline RR). A learned per-person mapping from (room_temp, humidity, breathing_rate) → "is the occupant uncomfortable?" lets HVAC pre-emptively adjust before the occupant consciously notices. Saves ~15-20% on cooling energy per published HVAC-personalisation studies (Aryal & Becerik-Gerber 2018), while improving comfort.
A smart speaker that doesn't interrupt when the occupant's breathing pattern shows high cognitive load (focused reading: shallow + regular). The sensing already exists; the appliance integration is the gap.
Honest scope check: this requires that someone publishes both (a) a reliable shallow-breathing-during-focus signature, and (b) a hands-off way for appliances to receive that signal. RuView ships (a)'s building blocks; (b) needs an MCP-style standard which ADR-104 (@ruv/ruview-mcp) is the first step toward.
| Component | Status | Used for |
|---|---|---|
| Breathing/heart rate detector | ✅ shipped | physiological state signal |
| Occupancy presence | ✅ shipped (cog-pose-estimation, cog-person-count) | "is anyone there?" gate |
| Motion intensity score | ✅ shipped | activity-state classifier input |
| Per-room baseline learner | ⚠️ partial (RollingP95 in #491 is the closest existing primitive) | personalised normalisation |
| State-classifier model | ❌ not built | maps (breathing, heart, motion) → state |
| MCP appliance API | ✅ partial (ADR-104) | hands-off appliance integration |
| Consent/opt-in machinery | ❌ not built | ethical baseline |
| Override/correction UI | ❌ not built | user-in-the-loop |
The four ❌/⚠️ items are the actual work for V1 ship-readiness. Roughly 1-2 quarters of dedicated effort, not a research project.
Empathic appliances raise three explicit consent questions that smart-speaker-vendors so far have not answered well. Any RuView-based empathic-appliance product should commit to all of these in writing:
These three constraints are surprisingly load-bearing — they rule out the most common smart-home failure modes (always-on listening, cloud-side aggregation, opaque consent flows).
| Threat | Mitigation |
|---|---|
| Compromised appliance leaks breathing rate continuously | Per-device sensing is opt-in; appliances default off |
| MCP API exposes raw signal to integrations | Only aggregate state passes the MCP boundary; raw stays local (ADR-104 §"Output validation") |
| Adversarial CSI poisoning makes the occupant look stressed/calm against their interest | R7 Stoer-Wagner multi-link consistency detects this |
| Long-term baseline learning enables individual identification across moves | Baseline is per-installation; no cloud sync; user can wipe at any time |
| Insurance / employer access to physiological state | Legal/contractual barrier; not solvable purely technically. Surface this explicitly in onboarding |
| Children / non-consenting cohabitants | Per-occupant opt-in, not per-installation. Use existing pose-based identity primitives (R3/R9/R15) to gate per-person |
@ruv/ruview-mcp) — the actual hands-off appliance API. Empathic-appliance integrations subscribe via MCP ruview_vitals_subscribe (not yet built; see HORIZON.md deferred list).cog-person-count) — the per-room occupancy gate ("only do empathic actions when an occupant is present and consented").RollingP95 to cover breathing-rate + heart-rate over 7-day windows).ruview_vitals_subscribe — the hands-off integration that lets a partner appliance subscribe to the aggregate state stream.