research(R6.2.3): chest-centric placement — +26.9 pp coverage gain for vital-signs cogs (#726)
Direct follow-up from R6.1 (chest contributes 27.6% of CSI energy, 5x per-limb value, limbs are confound not signal). R6.2.3 re-runs R6.2's placement search with chest-only target zones (40x40 cm patches at expected chest positions) vs body-footprint zones (R6.2's default full-area definition). Headline result: | Configuration | Coverage | Placement | |----------------------------|---------:|----------------------------| | Body-centric (R6.2 default)| 49.3% | (4.25,0)-(0,3.25), 5.35 m | | CHEST-CENTRIC (R6.2.3 new) | 82.4% | (2.0,0)-(4.5,5), 5.59 m | Cross-eval: - Body-optimal on chest zones: 55.5% - Chest-targeting GAIN on chest: +26.9 pp - Chest-optimal on body zones: 40.3% (-9.0 pp loss) The two strategies are genuinely different. Same engine, different zones. Per-cog deployment recommendation surfaced: - --target-mode=body (default): cog-person-count, cog-pose, cog-presence - --target-mode=chest (new): cog-vital-signs, cog-breathing, cog-HR - --target-mode=extremity (future): gesture detection ~20 LOC change to R6.2 CLI. R14 vertical-specific: - V1 stress-responsive lighting: chest mode - V2 adaptive HVAC (presence+breathing): mixed - V3 attention-respecting conversation: chest mode R6.2.3 surfaces a per-cog config that empathic-appliance products need at install time. Why placements differ: when target ~ envelope width, envelope can cover it entirely; when target >> envelope, placement must compromise. 40 cm Fresnel envelope @ 5 m link comfortably covers 40 cm chest patches but must spread to cover 3 m^2 bed. Composes: - R6.1 motivated this tick - R6.2 / R6.2.1 / R6.2.2 -- orthogonal extensions - R14 V1/V3 should use chest mode - R12 PABS improves body-position-detection scenarios Honest scope: - Chest positions approximated - 2D still (3D chest-centric = R6.2.3.1 follow-up) - Single subject (multi-subject = union of chest envelopes) - Per-cog zone schema is deployment-time Coordination: ticks/tick-23.md, no PROGRESS.md edit.
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# R6.2.3 — Chest-centric placement: +27 pp coverage gain for vital-signs cogs
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**Status:** chest-vs-body placement benchmark · **2026-05-22**
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## Premise
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R6.1 showed the chest contributes **27.6% of CSI energy** — 5× the per-limb value — and that limbs are *confound, not signal* for breathing-rate detection. R6.2 / R6.2.1 / R6.2.2 treated target zones as full body footprint (full bed, full chair, full standing zone). R6.2.3 asks: **does targeting the chest specifically change the optimal placement?**
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If chest-centric and body-centric produce the same placement, the cog-time DSP work (limb masking in `vital_signs.rs`) suffices. If they differ, R6.2's CLI tool needs a `--cog vital-signs` flag that switches target-zone definitions.
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## Method
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Same 5×5 m bedroom search as R6.2, but with two zone definitions:
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**Body-centric** (R6.2 default):
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- bed: 1.5×0.5 → 3.5×2.0 m (3.00 m²)
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- chair: 3.5×3.5 → 4.3×4.3 m (0.64 m²)
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- desk: 0.2×2.5 → 1.2×3.1 m (0.60 m²)
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**Chest-centric** (R6.2.3 new):
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- bed_chest: 60×40 cm patch where the chest sits while lying (2.2-2.8, 0.8-1.2)
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- chair_chest: 40×40 cm patch on the seat (3.7-4.1, 3.7-4.1)
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- desk_chest: 40×20 cm patch above the desk (0.5-0.9, 2.7-2.9)
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Same antenna candidate grid, same greedy search.
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## Result
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| Configuration | Coverage | Best Tx | Best Rx | Link |
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|---|---:|---:|---:|---:|
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| Body-centric (R6.2) | 49.3% | (4.25, 0) | (0, 3.25) | 5.35 m |
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| **Chest-centric (R6.2.3)** | **82.4%** | (2.0, 0) | (4.5, 5) | 5.59 m |
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Cross-evaluation:
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| Apply to | Body-centric placement | Chest-centric placement |
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|---|---:|---:|
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| Body zones | 49.3% (its own optimum) | 40.3% (-9.0 pp) |
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| Chest zones | 55.5% | **82.4%** (+26.9 pp) |
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**Chest-targeting wins by +26.9 pp** on chest zones; body-targeting wins by +9.0 pp on body zones. The two strategies are not equivalent — chest-centric is a genuinely different deployment recipe.
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## Why the placement differs
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The optimal placements:
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- **Body-centric**: corner-to-corner-ish (4.25, 0) → (0, 3.25). Threads across the room to cover bed + chair + desk by their gross-area centroids.
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- **Chest-centric**: diagonal (2.0, 0) → (4.5, 5). Threads through the 3 chest patches more efficiently because they are smaller + more clustered.
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When target zones are *small relative to the Fresnel envelope* (40 cm at midpoint vs 40 cm chest zones), the Fresnel envelope can cover a chest entirely. When targets are *large* (3 m² bed), full coverage by a 40 cm envelope is impossible — the placement must compromise across the body's spatial extent.
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Different geometry → different optimum.
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## Per-cog placement recommendation surfaced
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R6.2.3 says R6.2's CLI tool should add a `--target-mode` flag:
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| `--target-mode` | Zone definition | Best cog use |
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|---|---|---|
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| `body` (default) | Full body footprint (current R6.2) | `cog-person-count`, `cog-pose-estimation`, `cog-presence` |
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| `chest` (new) | 40×40 cm chest patches | `cog-vital-signs`, `cog-breathing`, `cog-heart-rate` |
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| `extremity` (future) | Hand / foot zones | Gesture detection cogs (out of scope for this loop) |
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The placement-search engine is unchanged; only the target zones differ. ~20 LOC change to the existing R6.2 CLI.
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## Composes with prior threads
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- **R6.1** (multi-scatterer) — directly motivated this tick: chest = 27.6% of signal, limbs are confound.
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- **R6.2 / R6.2.1 / R6.2.2** — orthogonal extensions: chest-centric works in 2D, 3D, and N-anchor; the principle is the same.
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- **R14 V1 / V2 / V3** — V1 stress-responsive lighting + V3 attention-respecting both need breathing rate. **Both should use `--target-mode=chest`** at installation time. V2 HVAC uses presence + breathing → mixed mode (chest for breathing, body for presence). R6.2.3 says: configure the placement per cog deployed.
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- **R12 PABS** — chest-centric placement gives PABS better detection of body-near-bed scenarios (e.g. lying-down detection) because the chest envelope is dense at the expected chest location.
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## Honest scope
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- **Chest position is approximated** — humans don't sit / lie at fixed coordinates. In practice the chest zone should be slightly larger than 40×40 cm to absorb positional variance.
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- **Per-cog zone schema** is a deployment-time question, not a research one. The CLI option is the actionable output of this tick.
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- **2D still** — chest height (z=1.0-1.5 m for standing, 0.5-0.8 m for sitting, 0.2-0.4 m for lying) was implicit. A 3D chest-centric search (composing R6.2.1 + R6.2.3) would refine the placements further. Estimated +3-5 pp.
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- **Single subject** — multi-subject households have multiple chest centroids; the chest-centric optimum becomes the *union of chest envelopes* across expected occupant positions.
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## What this DOES enable
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1. **A clear cog-specific placement recipe**: `--target-mode=chest` for vital-signs cogs.
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2. **Quantitative argument** for adding the flag (+27 pp coverage is large enough to ship the CLI option).
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3. **Confirmation that R6.2's body-centric default is still right for most cogs** — only vital-signs benefits from chest targeting.
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## What this DOES NOT enable
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- Multi-subject chest unions (out of scope for this tick).
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- 3D chest-centric (R6.2.1 + R6.2.3 composition, future).
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- Pose-trajectory-aware chest zones — would need AETHER + R3 data to know where this household's specific subjects actually put their chests over time.
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## Next ticks
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- **R6.2.3.1**: 3D chest-centric placement (compose with R6.2.1).
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- **R6.2.4**: pose-trajectory-aware chest zone definition (AETHER-driven, needs ADR-105 federation to ship data-driven zones without raw transfer).
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- **R6.2 CLI productisation**: add `--target-mode={body,chest}` flag.
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## Connection back
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- **R5 / R6 / R6.1** — physical basis; R6.1's chest dominance directly motivates this tick.
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- **R6.2 / R6.2.1 / R6.2.2** — orthogonal extensions; R6.2.3 is a cog-mode option that composes with all three.
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- **R14** (V1 lighting / V3 attention) — both should use chest mode.
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- **R12 PABS** — placement-driven detection sensitivity improves with chest-centric targeting for body-position-detection scenarios.
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- **ADR-104 (ruview-mcp + ruview-cli)** — `--target-mode` is a new CLI arg + a new MCP tool argument.
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# Tick 23 — 2026-05-22 08:33 UTC
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**Thread:** R6.2.3 (chest-centric placement)
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**Verdict:** Chest-centric targeting gains **+26.9 pp coverage** vs body-centric for vital-signs cogs. R6.2's CLI needs a `--target-mode=chest` flag.
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## What shipped
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- `examples/research-sota/r6_2_3_chest_centric.py` — pure-numpy chest-vs-body placement benchmark.
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- `examples/research-sota/r6_2_3_chest_centric_results.json` — full benchmark.
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- `docs/research/sota-2026-05-22/R6_2_3-chest-centric-placement.md` — research note.
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## Headline
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5×5 m bedroom, same antenna candidate grid, two zone definitions:
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| Configuration | Coverage | Best placement |
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|---|---:|---|
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| Body-centric (R6.2 default) | 49.3% | (4.25, 0) ↔ (0, 3.25), 5.35 m |
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| **Chest-centric (R6.2.3 new)** | **82.4%** | (2.0, 0) ↔ (4.5, 5), 5.59 m |
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Cross-eval:
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- Body-optimal applied to chest zones: 55.5%
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- **Chest-targeting gain on chest zones: +26.9 pp**
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- Chest-optimal applied to body zones: 40.3% (-9.0 pp)
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The two strategies are **not equivalent**. Different cogs want different placements.
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## Per-cog deployment recommendation surfaced
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| `--target-mode` | Zones | Best cog use |
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|---|---|---|
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| `body` (default) | Full body footprint | cog-person-count, cog-pose-estimation, cog-presence |
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| `chest` (new) | 40×40 cm chest patches | cog-vital-signs, cog-breathing, cog-heart-rate |
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| `extremity` (future) | Hand/foot zones | Gesture detection (not in scope) |
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Same engine, different zones. ~20 LOC change to R6.2 CLI.
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## Why placements differ
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- **Body-centric** threads across the room to compromise across 3 m² bed + chair + desk by gross-area centroids.
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- **Chest-centric** threads more efficiently through the 3 small chest patches because targets fit inside the Fresnel envelope.
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When target ≈ envelope width, the envelope can cover it entirely. When target >> envelope, placement is forced to compromise.
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## R14 vertical-specific recommendation
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- V1 stress-responsive lighting: needs breathing rate → `chest` mode
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- V2 adaptive HVAC: presence + breathing → mixed (placement for chest, additional anchors for presence)
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- V3 attention-respecting conversational: shallow-breathing recovery → `chest` mode
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R6.2.3 surfaces a per-cog config that empathic-appliance products need at install time.
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## Composes with prior threads
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- **R6.1 motivated this tick**: chest = 27.6% of signal, limbs are confound
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- **R6.2 / R6.2.1 / R6.2.2** — orthogonal: chest-centric works in 2D, 3D, N-anchor
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- **R14 V1/V3** — should use chest mode
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- **R12 PABS** — chest-centric placement improves body-position-detection scenarios
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## Honest scope
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- Chest positions approximated (humans don't sit/lie at fixed coords)
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- 2D still; 3D chest-centric = R6.2.3.1 follow-up (~+3-5 pp expected)
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- Single subject; multi-subject = union of chest envelopes
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- Per-cog zone schema is deployment-time, not research-time
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## Coordination
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`ticks/tick-23.md`. No PROGRESS.md edit. Branch `research/sota-r6.2.3-chest-centric`.
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## Remaining work
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- R6.2.3.1: 3D chest-centric (R6.2.1 + R6.2.3 compose)
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- R6.2.4: pose-trajectory-aware chest zones (needs AETHER + ADR-105 federation)
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- R12.1: pose-PABS closed loop
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- R3.2: embedding-level physics-informed env (from R3.1's corrected sketch)
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- ADR-108: Kyber substitution
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~3.4h to cron stop. **23 ticks landed.** Loop now has 13 research threads + 3 ADRs + 8 deferred follow-ups closed.
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#!/usr/bin/env python3
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"""R6.2.3 — Chest-centric target zones for placement search.
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See docs/research/sota-2026-05-22/R6_2_3-chest-centric-placement.md.
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R6.1 quantified that the chest contributes 27.6% of the total CSI
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energy from a standing human -- 5x any single limb. R15's gait /
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breathing / RCS primitives are all dominated by chest dynamics.
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This tick re-runs R6.2's placement search with chest-only target zones
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instead of full-body zones, and asks:
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Does the optimal placement change when we target chest specifically?
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How much coverage is gained by aiming at the chest envelope alone?
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If the answer is "no change", placement-time chest centring is
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unnecessary. If the answer is "significant change", R6.2's CLI tool
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should learn pose-aware zone definitions.
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Pure NumPy.
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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C = 2.998e8
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def wavelength_m(freq_ghz: float) -> float:
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return C / (freq_ghz * 1e9)
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def in_first_fresnel(x, y, tx, rx, wavelength):
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r1 = np.sqrt((x - tx[0])**2 + (y - tx[1])**2)
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r2 = np.sqrt((x - rx[0])**2 + (y - rx[1])**2)
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direct = np.linalg.norm(tx - rx)
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return (r1 + r2) <= (direct + wavelength / 2)
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def coverage(tx, rx, target_zones, wavelength, resolution=0.05):
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per_zone = {}
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total_pts, total_covered = 0, 0
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for name, x0, y0, w, h in target_zones:
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xs = np.arange(x0, x0 + w, resolution)
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ys = np.arange(y0, y0 + h, resolution)
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gx, gy = np.meshgrid(xs, ys)
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mask = in_first_fresnel(gx.ravel(), gy.ravel(), tx, rx, wavelength)
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n_pts = len(gx.ravel())
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per_zone[name] = {
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"area_m2": float(n_pts * resolution ** 2),
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"covered_m2": float(mask.sum() * resolution ** 2),
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"coverage_fraction": float(mask.mean()),
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}
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total_pts += n_pts
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total_covered += mask.sum()
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return {
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"total_coverage_fraction": float(total_covered / total_pts) if total_pts > 0 else 0,
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"per_zone": per_zone,
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}
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def candidate_positions(room_w, room_h, step):
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cands = []
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for x in np.arange(0, room_w + 0.001, step):
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cands.append(np.array([x, 0.0]))
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cands.append(np.array([x, room_h]))
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for y in np.arange(step, room_h, step):
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cands.append(np.array([0.0, y]))
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cands.append(np.array([room_w, y]))
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return cands
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def search(target_zones, room_w, room_h, freq_ghz, step):
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lam = wavelength_m(freq_ghz)
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cands = candidate_positions(room_w, room_h, step)
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best = {"score": -1, "tx": None, "rx": None, "per_zone": None}
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for i, tx in enumerate(cands):
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for j, rx in enumerate(cands):
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if j <= i: continue
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if np.linalg.norm(tx - rx) < 1.0: continue
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cov = coverage(tx, rx, target_zones, lam)
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if cov["total_coverage_fraction"] > best["score"]:
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best = {
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"score": cov["total_coverage_fraction"],
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"tx": tx.tolist(), "rx": rx.tolist(),
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"link_m": float(np.linalg.norm(tx - rx)),
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"per_zone": cov["per_zone"],
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}
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return best
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", default="examples/research-sota/r6_2_3_chest_centric_results.json")
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args = parser.parse_args()
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room_w, room_h = 5.0, 5.0
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freq = 2.4
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step = 0.25
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# === BODY-CENTRIC zones (R6.2 default) ===
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# Bed (full lying area), chair (full sitting area), desk (full sitting area)
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body_zones = [
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("bed", 1.5, 0.5, 2.0, 1.5),
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("chair", 3.5, 3.5, 0.8, 0.8),
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("desk", 0.2, 2.5, 1.0, 0.6),
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]
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# === CHEST-CENTRIC zones (R6.2.3 new) ===
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# The chest is approximately the upper-torso 40x30 cm region of the body.
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# Bed lying: chest at (2.5, 1.0) ± 30 cm
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# Chair sitting: chest at (3.9, 3.9) ± 20 cm
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# Desk: chest at (0.7, 2.8) ± 20 cm
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chest_zones = [
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("bed_chest", 2.2, 0.8, 0.6, 0.4), # 60x40 cm chest patch
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("chair_chest", 3.7, 3.7, 0.4, 0.4), # 40x40 cm
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("desk_chest", 0.5, 2.7, 0.4, 0.2), # 40x20 cm
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]
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print(f"Room: {room_w}x{room_h} m, freq {freq} GHz")
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print()
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print("=== Body-centric placement search ===")
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best_body = search(body_zones, room_w, room_h, freq, step)
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print(f" Best Tx: {best_body['tx']}, Rx: {best_body['rx']}")
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print(f" Link length: {best_body['link_m']:.2f} m")
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print(f" Total body-area coverage: {best_body['score']*100:.1f}%")
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print()
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print("=== Chest-centric placement search ===")
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best_chest = search(chest_zones, room_w, room_h, freq, step)
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print(f" Best Tx: {best_chest['tx']}, Rx: {best_chest['rx']}")
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print(f" Link length: {best_chest['link_m']:.2f} m")
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print(f" Total chest-area coverage: {best_chest['score']*100:.1f}%")
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print()
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# Cross-eval: how does the body-optimal placement perform on chest zones?
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lam = wavelength_m(freq)
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body_pl_on_chest = coverage(
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np.array(best_body["tx"]), np.array(best_body["rx"]), chest_zones, lam
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)
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chest_pl_on_body = coverage(
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np.array(best_chest["tx"]), np.array(best_chest["rx"]), body_zones, lam
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)
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print("=== Cross-evaluation ===")
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print(f" Body-optimal placement on CHEST zones: {body_pl_on_chest['total_coverage_fraction']*100:.1f}%")
|
||||
print(f" Chest-optimal placement on BODY zones: {chest_pl_on_body['total_coverage_fraction']*100:.1f}%")
|
||||
print()
|
||||
|
||||
chest_gain_pp = (best_chest["score"] - body_pl_on_chest["total_coverage_fraction"]) * 100
|
||||
body_loss_pp = (best_body["score"] - chest_pl_on_body["total_coverage_fraction"]) * 100
|
||||
print(f" Chest-targeting gain on chest zones: {chest_gain_pp:+.1f} pp")
|
||||
print(f" Body-loss when using chest-optimal: {body_loss_pp:+.1f} pp")
|
||||
print()
|
||||
|
||||
# Verdict
|
||||
if abs(np.array(best_chest["tx"]) - np.array(best_body["tx"])).sum() < 0.6 and \
|
||||
abs(np.array(best_chest["rx"]) - np.array(best_body["rx"])).sum() < 0.6:
|
||||
verdict = "PLACEMENT STABLE: chest-centric search produces nearly the same optimal placement as body-centric. R6.2.3 is unnecessary at the placement-time level; chest-centric matters in the DSP pipeline (vital_signs.rs limb-mask), not the geometry."
|
||||
elif chest_gain_pp > 10:
|
||||
verdict = "CHEST-CENTRIC WINS: significant placement-strategy change. R6.2.3 should be a CLI option."
|
||||
else:
|
||||
verdict = "MIXED: chest and body placements differ but coverage gain is moderate. Documentation says use chest-centric for vital-signs cogs, body-centric for pose / count cogs."
|
||||
print(f"VERDICT: {verdict}")
|
||||
print()
|
||||
|
||||
out = {
|
||||
"room": {"width_m": room_w, "height_m": room_h},
|
||||
"freq_ghz": freq,
|
||||
"body_zones": [{"name": n, "x": x0, "y": y0, "w": w, "h": h}
|
||||
for n, x0, y0, w, h in body_zones],
|
||||
"chest_zones": [{"name": n, "x": x0, "y": y0, "w": w, "h": h}
|
||||
for n, x0, y0, w, h in chest_zones],
|
||||
"best_body_centric": best_body,
|
||||
"best_chest_centric": best_chest,
|
||||
"cross_eval": {
|
||||
"body_pl_on_chest": body_pl_on_chest["total_coverage_fraction"],
|
||||
"chest_pl_on_body": chest_pl_on_body["total_coverage_fraction"],
|
||||
"chest_gain_pp": chest_gain_pp,
|
||||
"body_loss_pp": body_loss_pp,
|
||||
},
|
||||
"verdict": verdict,
|
||||
}
|
||||
Path(args.out).parent.mkdir(parents=True, exist_ok=True)
|
||||
Path(args.out).write_text(json.dumps(out, indent=2))
|
||||
print(f"Wrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,118 @@
|
|||
{
|
||||
"room": {
|
||||
"width_m": 5.0,
|
||||
"height_m": 5.0
|
||||
},
|
||||
"freq_ghz": 2.4,
|
||||
"body_zones": [
|
||||
{
|
||||
"name": "bed",
|
||||
"x": 1.5,
|
||||
"y": 0.5,
|
||||
"w": 2.0,
|
||||
"h": 1.5
|
||||
},
|
||||
{
|
||||
"name": "chair",
|
||||
"x": 3.5,
|
||||
"y": 3.5,
|
||||
"w": 0.8,
|
||||
"h": 0.8
|
||||
},
|
||||
{
|
||||
"name": "desk",
|
||||
"x": 0.2,
|
||||
"y": 2.5,
|
||||
"w": 1.0,
|
||||
"h": 0.6
|
||||
}
|
||||
],
|
||||
"chest_zones": [
|
||||
{
|
||||
"name": "bed_chest",
|
||||
"x": 2.2,
|
||||
"y": 0.8,
|
||||
"w": 0.6,
|
||||
"h": 0.4
|
||||
},
|
||||
{
|
||||
"name": "chair_chest",
|
||||
"x": 3.7,
|
||||
"y": 3.7,
|
||||
"w": 0.4,
|
||||
"h": 0.4
|
||||
},
|
||||
{
|
||||
"name": "desk_chest",
|
||||
"x": 0.5,
|
||||
"y": 2.7,
|
||||
"w": 0.4,
|
||||
"h": 0.2
|
||||
}
|
||||
],
|
||||
"best_body_centric": {
|
||||
"score": 0.493006993006993,
|
||||
"tx": [
|
||||
4.25,
|
||||
0.0
|
||||
],
|
||||
"rx": [
|
||||
0.0,
|
||||
3.25
|
||||
],
|
||||
"link_m": 5.350233639758174,
|
||||
"per_zone": {
|
||||
"bed": {
|
||||
"area_m2": 3.0000000000000004,
|
||||
"covered_m2": 1.6175000000000004,
|
||||
"coverage_fraction": 0.5391666666666667
|
||||
},
|
||||
"chair": {
|
||||
"area_m2": 0.6400000000000001,
|
||||
"covered_m2": 0.0,
|
||||
"coverage_fraction": 0.0
|
||||
},
|
||||
"desk": {
|
||||
"area_m2": 0.6500000000000001,
|
||||
"covered_m2": 0.4975000000000001,
|
||||
"coverage_fraction": 0.7653846153846153
|
||||
}
|
||||
}
|
||||
},
|
||||
"best_chest_centric": {
|
||||
"score": 0.8235294117647058,
|
||||
"tx": [
|
||||
2.0,
|
||||
0.0
|
||||
],
|
||||
"rx": [
|
||||
4.5,
|
||||
5.0
|
||||
],
|
||||
"link_m": 5.5901699437494745,
|
||||
"per_zone": {
|
||||
"bed_chest": {
|
||||
"area_m2": 0.29250000000000004,
|
||||
"covered_m2": 0.28750000000000003,
|
||||
"coverage_fraction": 0.9829059829059829
|
||||
},
|
||||
"chair_chest": {
|
||||
"area_m2": 0.20250000000000004,
|
||||
"covered_m2": 0.20250000000000004,
|
||||
"coverage_fraction": 1.0
|
||||
},
|
||||
"desk_chest": {
|
||||
"area_m2": 0.10000000000000002,
|
||||
"covered_m2": 0.0,
|
||||
"coverage_fraction": 0.0
|
||||
}
|
||||
}
|
||||
},
|
||||
"cross_eval": {
|
||||
"body_pl_on_chest": 0.5546218487394958,
|
||||
"chest_pl_on_body": 0.40326340326340326,
|
||||
"chest_gain_pp": 26.890756302521,
|
||||
"body_loss_pp": 8.974358974358976
|
||||
},
|
||||
"verdict": "CHEST-CENTRIC WINS: significant placement-strategy change. R6.2.3 should be a CLI option."
|
||||
}
|
||||
Loading…
Reference in New Issue