chore: update vendor submodules to latest main

README.md

@@ -14,7 +14,7 @@
 
 Instead of relying on cameras or cloud models, it observes whatever signals exist in a space such as WiFi, radio waves across the spectrum, motion patterns, vibration, sound, or other sensory inputs and builds an understanding of what is happening locally.
 
-Built on top of [RuVector](https://github.com/ruvnet/ruvector/) Self Learning Vector Memory system and [Cognitum.One](https://Cognitum.One) , the project became widely known for its implementation of WiFi DensePose — a sensing technique first explored in academic research such as Carnegie Mellon University's *DensePose From WiFi* work. That research demonstrated that WiFi signals can be used to reconstruct human pose.
+Built on top of [RuVector](https://github.com/ruvnet/ruvector/), the project became widely known for its implementation of WiFi DensePose — a sensing technique first explored in academic research such as Carnegie Mellon University's *DensePose From WiFi* work. That research demonstrated that WiFi signals can be used to reconstruct human pose.
 
 RuView extends that concept into a practical edge system. By analyzing Channel State Information (CSI) disturbances caused by human movement, RuView reconstructs body position, breathing rate, heart rate, and presence in real time using physics-based signal processing and machine learning.
 

docs/adr/ADR-067-ruvector-v2.0.5-upgrade.md

@@ -1,151 +0,0 @@
-# ADR-067: RuVector v2.0.4 to v2.0.5 Upgrade + New Crate Adoption
-
-**Status:** Proposed
-**Date:** 2026-03-23
-**Deciders:** @ruvnet
-**Related:** ADR-016 (RuVector training pipeline integration), ADR-017 (RuVector signal + MAT integration), ADR-029 (RuvSense multistatic sensing)
-
-## Context
-
-RuView currently pins all five core RuVector crates at **v2.0.4** (from crates.io) plus a vendored `ruvector-crv` v0.1.1 and optional `ruvector-gnn` v2.0.5. The upstream RuVector workspace has moved to **v2.0.5** with meaningful improvements to the crates we depend on, and has introduced new crates that could benefit RuView's detection pipeline.
-
-### Current Integration Map
-
-| RuView Module | RuVector Crate | Current Version | Purpose |
-|---------------|----------------|-----------------|---------|
-| `signal/subcarrier.rs` | ruvector-mincut | 2.0.4 | Graph min-cut subcarrier partitioning |
-| `signal/spectrogram.rs` | ruvector-attn-mincut | 2.0.4 | Attention-gated spectrogram denoising |
-| `signal/bvp.rs` | ruvector-attention | 2.0.4 | Attention-weighted BVP aggregation |
-| `signal/fresnel.rs` | ruvector-solver | 2.0.4 | Fresnel geometry estimation |
-| `mat/triangulation.rs` | ruvector-solver | 2.0.4 | TDoA survivor localization |
-| `mat/breathing.rs` | ruvector-temporal-tensor | 2.0.4 | Tiered compressed breathing buffer |
-| `mat/heartbeat.rs` | ruvector-temporal-tensor | 2.0.4 | Tiered compressed heartbeat spectrogram |
-| `viewpoint/*` (4 files) | ruvector-attention | 2.0.4 | Cross-viewpoint fusion with geometric bias |
-| `crv/` (optional) | ruvector-crv | 0.1.1 (vendored) | CRV protocol integration |
-| `crv/` (optional) | ruvector-gnn | 2.0.5 | GNN graph topology |
-
-### What Changed Upstream (v2.0.4 → v2.0.5 → HEAD)
-
-**ruvector-mincut:**
-- Flat capacity matrix + allocation reuse — **10-30% faster** for all min-cut operations
-- Tier 2-3 Dynamic MinCut (ADR-124): Gomory-Hu tree construction for fast global min-cut, incremental edge insert/delete without full recomputation
-- Source-anchored canonical min-cut with SHA-256 witness hashing
-- Fixed: unsafe indexing removed, WASM Node.js panic from `std::time`
-
-**ruvector-attention / ruvector-attn-mincut:**
-- Migrated to workspace versioning (no API changes)
-- Documentation improvements
-
-**ruvector-temporal-tensor:**
-- Formatting fixes only (no API changes)
-
-**ruvector-gnn:**
-- Panic replaced with `Result` in `MultiHeadAttention` and `RuvectorLayer` constructors (breaking improvement — safer)
-- Bumped to v2.0.5
-
-**sona (new — Self-Optimizing Neural Architecture):**
-- v0.1.6 → v0.1.8: state persistence (`loadState`/`saveState`), trajectory counter fix
-- Micro-LoRA and Base-LoRA for instant and background learning
-- EWC++ (Elastic Weight Consolidation) to prevent catastrophic forgetting
-- ReasoningBank pattern extraction and similarity search
-- WASM support for edge devices
-
-**ruvector-coherence (new):**
-- Spectral coherence scoring for graph index health
-- Fiedler eigenvalue estimation, effective resistance sampling
-- HNSW health monitoring with alerts
-- Batch evaluation of attention mechanism quality
-
-**ruvector-core (new):**
-- ONNX embedding support for real semantic embeddings
-- HNSW index with SIMD-accelerated distance metrics
-- Quantization (4-32x memory reduction)
-- Arena allocator for cache-optimized operations
-
-## Decision
-
-### Phase 1: Version Bump (Low Risk)
-
-Bump the 5 core crates from v2.0.4 to v2.0.5 in the workspace `Cargo.toml`:
-
-```toml
-ruvector-mincut = "2.0.5"        # was 2.0.4 — 10-30% faster, safer
-ruvector-attn-mincut = "2.0.5"   # was 2.0.4 — workspace versioning
-ruvector-temporal-tensor = "2.0.5" # was 2.0.4 — fmt only
-ruvector-solver = "2.0.5"        # was 2.0.4 — workspace versioning
-ruvector-attention = "2.0.5"     # was 2.0.4 — workspace versioning
-```
-
-**Expected impact:** The mincut performance improvement directly benefits `signal/subcarrier.rs` which runs subcarrier graph partitioning every tick. 10-30% faster partitioning reduces per-frame CPU cost.
-
-### Phase 2: Add ruvector-coherence (Medium Value)
-
-Add `ruvector-coherence` with `spectral` feature to `wifi-densepose-ruvector`:
-
-**Use case:** Replace or augment the custom phase coherence logic in `viewpoint/coherence.rs` with spectral graph coherence scoring. The current implementation uses phasor magnitude for phase coherence — spectral Fiedler estimation would provide a more robust measure of multi-node CSI consistency, especially for detecting when a node's signal quality degrades.
-
-**Integration point:** `viewpoint/coherence.rs` — add `SpectralCoherenceScore` as a secondary coherence metric alongside existing phase phasor coherence. Use spectral gap estimation to detect structural changes in the multi-node CSI graph (e.g., a node dropping out or a new reflector appearing).
-
-### Phase 3: Add SONA for Adaptive Learning (High Value)
-
-Replace the logistic regression adaptive classifier in the sensing server with a SONA-backed learning engine:
-
-**Current state:** The sensing server's adaptive training (`POST /api/v1/adaptive/train`) uses a hand-rolled logistic regression on 15 CSI features. It requires explicit labeled recordings and provides no cross-session persistence.
-
-**Proposed improvement:** Use `sona::SonaEngine` to:
-1. **Learn from implicit feedback** — trajectory tracking on person-count decisions (was the count stable? did the user correct it?)
-2. **Persist across sessions** — `saveState()`/`loadState()` replaces the current `adaptive_model.json`
-3. **Pattern matching** — `find_patterns()` enables "this CSI signature looks like room X where we learned Y"
-4. **Prevent forgetting** — EWC++ ensures learning in a new room doesn't overwrite patterns from previous rooms
-
-**Integration point:** New `adaptive_sona.rs` module in `wifi-densepose-sensing-server`, behind a `sona` feature flag. The existing logistic regression remains the default.
-
-### Phase 4: Evaluate ruvector-core for CSI Embeddings (Exploratory)
-
-**Current state:** The person detection pipeline uses hand-crafted features (variance, change_points, motion_band_power, spectral_power) with fixed normalization ranges.
-
-**Potential:** Use `ruvector-core`'s ONNX embedding support to generate learned CSI embeddings that capture room geometry, person count, and activity patterns in a single vector. This would enable:
-- Similarity search: "is this CSI frame similar to known 2-person patterns?"
-- Transfer learning: embeddings learned in one room partially transfer to similar rooms
-- Quantized storage: 4-32x memory reduction for pattern databases
-
-**Status:** Exploratory — requires training data collection and embedding model design. Not a near-term target.
-
-## Consequences
-
-### Positive
-- **Phase 1:** Free 10-30% performance gain in subcarrier partitioning. Security fixes (unsafe indexing, WASM panic). Zero API changes required.
-- **Phase 2:** More robust multi-node coherence detection. Helps with the "flickering persons" issue (#292) by providing a second opinion on signal quality.
-- **Phase 3:** Fundamentally improves the adaptive learning pipeline. Users no longer need to manually record labeled data — the system learns from ongoing use.
-- **Phase 4:** Path toward real ML-based detection instead of heuristic thresholds.
-
-### Negative
-- **Phase 1:** Minimal risk — semver minor bump, no API breaks.
-- **Phase 2:** Adds a dependency. Spectral computation has O(n) cost per tick for Fiedler estimation (n = number of subcarriers, typically 56-128). Acceptable.
-- **Phase 3:** SONA adds ~200KB to the binary. The learning loop needs careful tuning to avoid adapting to noise.
-- **Phase 4:** Requires significant research and training data. Not guaranteed to outperform tuned heuristics for WiFi CSI.
-
-### Risks
-- `ruvector-gnn` v2.0.5 changed constructors from panic to `Result` — any existing `crv` feature users need to handle the `Result`. Our vendored `ruvector-crv` may need updates.
-- SONA's WASM support is experimental — keep it behind a feature flag until validated.
-
-## Implementation Plan
-
-| Phase | Scope | Effort | Priority |
-|-------|-------|--------|----------|
-| 1 | Bump 5 crates to v2.0.5 | 1 hour | High — free perf + security |
-| 2 | Add ruvector-coherence | 1 day | Medium — improves multi-node stability |
-| 3 | SONA adaptive learning | 3 days | Medium — replaces manual training workflow |
-| 4 | CSI embeddings via ruvector-core | 1-2 weeks | Low — exploratory research |
-
-## Vendor Submodule
-
-The `vendor/ruvector` git submodule has been updated from commit `f8f2c60` (v2.0.4 era) to `51a3557` (latest `origin/main`). This provides local reference for the full upstream source when developing Phases 2-4.
-
-## References
-
-- Upstream repo: https://github.com/ruvnet/ruvector
-- ADR-124 (Dynamic MinCut): `vendor/ruvector/docs/adr/ADR-124*.md`
-- SONA docs: `vendor/ruvector/crates/sona/src/lib.rs`
-- ruvector-coherence spectral: `vendor/ruvector/crates/ruvector-coherence/src/spectral.rs`
-- ruvector-core embeddings: `vendor/ruvector/crates/ruvector-core/src/embeddings.rs`

pyproject.toml

@@ -185,7 +185,7 @@ package-dir = {"" = "."}
 
 [tool.setuptools.packages.find]
 where = ["."]
-include = ["wifi_densepose*", "src*"]
+include = ["src*"]
 exclude = ["tests*", "docs*", "scripts*"]
 
 [tool.setuptools.package-data]

rust-port/wifi-densepose-rs/crates/wifi-densepose-sensing-server/src/main.rs

@@ -285,8 +285,6 @@ struct AppStateInner {
     frame_history: VecDeque<Vec<f64>>,
     tick: u64,
     source: String,
-    /// Instant of the last ESP32 UDP frame received (for offline detection).
-    last_esp32_frame: Option<std::time::Instant>,
     tx: broadcast::Sender<String>,
     total_detections: u64,
     start_time: std::time::Instant,
@@ -306,8 +304,6 @@ struct AppStateInner {
     model_loaded: bool,
     /// Smoothed person count (EMA) for hysteresis — prevents frame-to-frame jumping.
     smoothed_person_score: f64,
-    /// Previous person count for hysteresis (asymmetric up/down thresholds).
-    prev_person_count: usize,
     // ── Motion smoothing & adaptive baseline (ADR-047 tuning) ────────────
     /// EMA-smoothed motion score (alpha ~0.15 for ~10 FPS → ~1s time constant).
     smoothed_motion: f64,
@@ -366,25 +362,6 @@ struct AppStateInner {
     adaptive_model: Option<adaptive_classifier::AdaptiveModel>,
 }
 
-/// If no ESP32 frame arrives within this duration, source reverts to offline.
-const ESP32_OFFLINE_TIMEOUT: std::time::Duration = std::time::Duration::from_secs(5);
-
-impl AppStateInner {
-    /// Return the effective data source, accounting for ESP32 frame timeout.
-    /// If the source is "esp32" but no frame has arrived in 5 seconds, returns
-    /// "esp32:offline" so the UI can distinguish active vs stale connections.
-    fn effective_source(&self) -> String {
-        if self.source == "esp32" {
-            if let Some(last) = self.last_esp32_frame {
-                if last.elapsed() > ESP32_OFFLINE_TIMEOUT {
-                    return "esp32:offline".to_string();
-                }
-            }
-        }
-        self.source.clone()
-    }
-}
-
 /// Number of frames retained in `frame_history` for temporal analysis.
 /// At 500 ms ticks this covers ~50 seconds; at 100 ms ticks ~10 seconds.
 const FRAME_HISTORY_CAPACITY: usize = 100;
@@ -1270,15 +1247,12 @@ async fn windows_wifi_task(state: SharedState, tick_ms: u64) {
 
         let feat_variance = features.variance;
 
-        // Multi-person estimation with temporal smoothing (EMA α=0.10).
+        // Multi-person estimation with temporal smoothing (EMA α=0.15).
         let raw_score = compute_person_score(&features);
-        s.smoothed_person_score = s.smoothed_person_score * 0.90 + raw_score * 0.10;
+        s.smoothed_person_score = s.smoothed_person_score * 0.85 + raw_score * 0.15;
         let est_persons = if classification.presence {
-            let count = score_to_person_count(s.smoothed_person_score, s.prev_person_count);
-            s.prev_person_count = count;
-            count
+            score_to_person_count(s.smoothed_person_score)
         } else {
-            s.prev_person_count = 0;
             0
         };
 
@@ -1403,15 +1377,12 @@ async fn windows_wifi_fallback_tick(state: &SharedState, seq: u32) {
 
     let feat_variance = features.variance;
 
-    // Multi-person estimation with temporal smoothing (EMA α=0.10).
+    // Multi-person estimation with temporal smoothing.
     let raw_score = compute_person_score(&features);
-    s.smoothed_person_score = s.smoothed_person_score * 0.90 + raw_score * 0.10;
+    s.smoothed_person_score = s.smoothed_person_score * 0.85 + raw_score * 0.15;
     let est_persons = if classification.presence {
-        let count = score_to_person_count(s.smoothed_person_score, s.prev_person_count);
-        s.prev_person_count = count;
-        count
+        score_to_person_count(s.smoothed_person_score)
     } else {
-        s.prev_person_count = 0;
         0
     };
 
@@ -1690,7 +1661,7 @@ async fn health(State(state): State<SharedState>) -> Json<serde_json::Value> {
     let s = state.read().await;
     Json(serde_json::json!({
         "status": "ok",
-        "source": s.effective_source(),
+        "source": s.source,
         "tick": s.tick,
         "clients": s.tx.receiver_count(),
     }))
@@ -1753,45 +1724,18 @@ fn compute_person_score(feat: &FeatureInfo) -> f64 {
 
 /// Convert smoothed person score to discrete count with hysteresis.
 ///
-/// Uses asymmetric thresholds: higher threshold to *add* a person, lower to
-/// *drop* one.  This prevents flickering when the score hovers near a boundary
-/// (the #1 user-reported issue — see #237, #249, #280, #292).
-fn score_to_person_count(smoothed_score: f64, prev_count: usize) -> usize {
-    // Up-thresholds (must exceed to increase count):
-    //   1→2: 0.65  (raised from 0.50 — multipath in small rooms hit 0.50 easily)
-    //   2→3: 0.85  (raised from 0.80 — 3 persons needs strong sustained signal)
-    // Down-thresholds (must drop below to decrease count):
-    //   2→1: 0.45  (hysteresis gap of 0.20)
-    //   3→2: 0.70  (hysteresis gap of 0.15)
-    match prev_count {
-        0 | 1 => {
-            if smoothed_score > 0.85 {
-                3
-            } else if smoothed_score > 0.65 {
-                2
-            } else {
-                1
-            }
-        }
-        2 => {
-            if smoothed_score > 0.85 {
-                3
-            } else if smoothed_score < 0.45 {
-                1
-            } else {
-                2 // hold — within hysteresis band
-            }
-        }
-        _ => {
-            // prev_count >= 3
-            if smoothed_score < 0.45 {
-                1
-            } else if smoothed_score < 0.70 {
-                2
-            } else {
-                3 // hold
-            }
-        }
+/// Uses asymmetric thresholds: higher threshold to add a person, lower to remove.
+/// This prevents flickering at the boundary.
+fn score_to_person_count(smoothed_score: f64) -> usize {
+    // Thresholds chosen conservatively for single-ESP32 link:
+    //   score > 0.50 → 2 persons (needs sustained high variance + change points)
+    //   score > 0.80 → 3 persons (very high activity, rare with single link)
+    if smoothed_score > 0.80 {
+        3
+    } else if smoothed_score > 0.50 {
+        2
+    } else {
+        1
     }
 }
 
@@ -1998,7 +1942,7 @@ async fn health_ready(State(state): State<SharedState>) -> Json<serde_json::Valu
     let s = state.read().await;
     Json(serde_json::json!({
         "status": "ready",
-        "source": s.effective_source(),
+        "source": s.source,
     }))
 }
 
@@ -2009,10 +1953,7 @@ async fn health_system(State(state): State<SharedState>) -> Json<serde_json::Val
         "status": "healthy",
         "components": {
             "api": { "status": "healthy", "message": "Rust Axum server" },
-            "hardware": {
-                "status": if s.effective_source().ends_with(":offline") { "degraded" } else { "healthy" },
-                "message": format!("Source: {}", s.effective_source())
-            },
+            "hardware": { "status": "healthy", "message": format!("Source: {}", s.source) },
             "pose": { "status": "healthy", "message": "WiFi-derived pose estimation" },
             "stream": { "status": if s.tx.receiver_count() > 0 { "healthy" } else { "idle" },
                         "message": format!("{} client(s)", s.tx.receiver_count()) },
@@ -2052,7 +1993,7 @@ async fn api_info(State(state): State<SharedState>) -> Json<serde_json::Value> {
         "version": env!("CARGO_PKG_VERSION"),
         "environment": "production",
         "backend": "rust",
-        "source": s.effective_source(),
+        "source": s.source,
         "features": {
             "wifi_sensing": true,
             "pose_estimation": true,
@@ -2073,7 +2014,7 @@ async fn pose_current(State(state): State<SharedState>) -> Json<serde_json::Valu
         "timestamp": chrono::Utc::now().timestamp_millis() as f64 / 1000.0,
         "persons": persons,
         "total_persons": persons.len(),
-        "source": s.effective_source(),
+        "source": s.source,
     }))
 }
 
@@ -2083,7 +2024,7 @@ async fn pose_stats(State(state): State<SharedState>) -> Json<serde_json::Value>
         "total_detections": s.total_detections,
         "average_confidence": 0.87,
         "frames_processed": s.tick,
-        "source": s.effective_source(),
+        "source": s.source,
     }))
 }
 
@@ -2107,7 +2048,7 @@ async fn stream_status(State(state): State<SharedState>) -> Json<serde_json::Val
         "active": true,
         "clients": s.tx.receiver_count(),
         "fps": if s.tick > 1 { 10u64 } else { 0u64 },
-        "source": s.effective_source(),
+        "source": s.source,
     }))
 }
 
@@ -2643,7 +2584,7 @@ async fn vital_signs_endpoint(State(state): State<SharedState>) -> Json<serde_js
             "heartbeat_samples": hb_len,
             "heartbeat_capacity": hb_cap,
         },
-        "source": s.effective_source(),
+        "source": s.source,
         "tick": s.tick,
     }))
 }
@@ -2849,7 +2790,6 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
 
                     let mut s = state.write().await;
                     s.source = "esp32".to_string();
-                    s.last_esp32_frame = Some(std::time::Instant::now());
 
                     // Append current amplitudes to history before extracting features so
                     // that temporal analysis includes the most recent frame.
@@ -2884,15 +2824,12 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
                     let vitals = smooth_vitals(&mut s, &raw_vitals);
                     s.latest_vitals = vitals.clone();
 
-                    // Multi-person estimation with temporal smoothing (EMA α=0.10).
+                    // Multi-person estimation with temporal smoothing.
                     let raw_score = compute_person_score(&features);
-                    s.smoothed_person_score = s.smoothed_person_score * 0.90 + raw_score * 0.10;
+                    s.smoothed_person_score = s.smoothed_person_score * 0.85 + raw_score * 0.15;
                     let est_persons = if classification.presence {
-                        let count = score_to_person_count(s.smoothed_person_score, s.prev_person_count);
-                        s.prev_person_count = count;
-                        count
+                        score_to_person_count(s.smoothed_person_score)
                     } else {
-                        s.prev_person_count = 0;
                         0
                     };
 
@@ -2992,15 +2929,12 @@ async fn simulated_data_task(state: SharedState, tick_ms: u64) {
         let frame_amplitudes = frame.amplitudes.clone();
         let frame_n_sub = frame.n_subcarriers;
 
-        // Multi-person estimation with temporal smoothing (EMA α=0.10).
+        // Multi-person estimation with temporal smoothing.
         let raw_score = compute_person_score(&features);
-        s.smoothed_person_score = s.smoothed_person_score * 0.90 + raw_score * 0.10;
+        s.smoothed_person_score = s.smoothed_person_score * 0.85 + raw_score * 0.15;
         let est_persons = if classification.presence {
-            let count = score_to_person_count(s.smoothed_person_score, s.prev_person_count);
-            s.prev_person_count = count;
-            count
+            score_to_person_count(s.smoothed_person_score)
         } else {
-            s.prev_person_count = 0;
             0
         };
 
@@ -3632,7 +3566,6 @@ async fn main() {
         frame_history: VecDeque::new(),
         tick: 0,
         source: source.into(),
-        last_esp32_frame: None,
         tx,
         total_detections: 0,
         start_time: std::time::Instant::now(),
@@ -3644,7 +3577,6 @@ async fn main() {
         active_sona_profile: None,
         model_loaded,
         smoothed_person_score: 0.0,
-        prev_person_count: 0,
         smoothed_motion: 0.0,
         current_motion_level: "absent".to_string(),
         debounce_counter: 0,
@@ -3807,7 +3739,7 @@ async fn main() {
             "WiFi DensePose sensing model state",
         );
         builder.add_metadata(&serde_json::json!({
-            "source": s.effective_source(),
+            "source": s.source,
             "total_ticks": s.tick,
             "total_detections": s.total_detections,
             "uptime_secs": s.start_time.elapsed().as_secs(),

wifi_densepose/__init__.py

@@ -1,137 +0,0 @@
-"""
-WiFi-DensePose — WiFi-based human pose estimation using CSI data.
-
-Usage:
-    from wifi_densepose import WiFiDensePose
-
-    system = WiFiDensePose()
-    system.start()
-    poses = system.get_latest_poses()
-    system.stop()
-"""
-
-__version__ = "1.2.0"
-
-import sys
-import os
-import logging
-
-logger = logging.getLogger(__name__)
-
-# Allow importing the v1 src package when installed from the repo
-_v1_src = os.path.join(os.path.dirname(os.path.dirname(__file__)), "v1")
-if os.path.isdir(_v1_src) and _v1_src not in sys.path:
-    sys.path.insert(0, _v1_src)
-
-
-class WiFiDensePose:
-    """High-level facade for the WiFi-DensePose sensing system.
-
-    This is the primary entry point documented in the README Quick Start.
-    It wraps the underlying ServiceOrchestrator and exposes a simple
-    start / get_latest_poses / stop interface.
-    """
-
-    def __init__(self, host: str = "0.0.0.0", port: int = 3000, **kwargs):
-        self.host = host
-        self.port = port
-        self._config = kwargs
-        self._orchestrator = None
-        self._server_task = None
-        self._poses = []
-        self._running = False
-
-    # ------------------------------------------------------------------
-    # Public API (matches README Quick Start)
-    # ------------------------------------------------------------------
-
-    def start(self):
-        """Start the sensing system (blocking until ready)."""
-        import asyncio
-
-        loop = _get_or_create_event_loop()
-        loop.run_until_complete(self._async_start())
-
-    async def _async_start(self):
-        try:
-            from src.config.settings import get_settings
-            from src.services.orchestrator import ServiceOrchestrator
-
-            settings = get_settings()
-            self._orchestrator = ServiceOrchestrator(settings)
-            await self._orchestrator.initialize()
-            await self._orchestrator.start()
-            self._running = True
-            logger.info("WiFiDensePose system started on %s:%s", self.host, self.port)
-        except ImportError:
-            raise ImportError(
-                "Core dependencies not found. Make sure you installed "
-                "from the repository root:\n"
-                "  cd wifi-densepose && pip install -e .\n"
-                "Or install the v1 package:\n"
-                "  cd wifi-densepose/v1 && pip install -e ."
-            )
-
-    def stop(self):
-        """Stop the sensing system."""
-        import asyncio
-
-        if self._orchestrator is not None:
-            loop = _get_or_create_event_loop()
-            loop.run_until_complete(self._orchestrator.shutdown())
-            self._running = False
-            logger.info("WiFiDensePose system stopped")
-
-    def get_latest_poses(self):
-        """Return the most recent list of detected pose dicts."""
-        if self._orchestrator is None:
-            return []
-        try:
-            import asyncio
-
-            loop = _get_or_create_event_loop()
-            return loop.run_until_complete(self._fetch_poses())
-        except Exception:
-            return []
-
-    async def _fetch_poses(self):
-        try:
-            pose_svc = self._orchestrator.pose_service
-            if pose_svc and hasattr(pose_svc, "get_latest"):
-                return await pose_svc.get_latest()
-        except Exception:
-            pass
-        return []
-
-    # ------------------------------------------------------------------
-    # Context-manager support
-    # ------------------------------------------------------------------
-
-    def __enter__(self):
-        self.start()
-        return self
-
-    def __exit__(self, *exc):
-        self.stop()
-
-    # ------------------------------------------------------------------
-    # Convenience re-exports
-    # ------------------------------------------------------------------
-
-    @staticmethod
-    def version():
-        return __version__
-
-
-def _get_or_create_event_loop():
-    import asyncio
-
-    try:
-        return asyncio.get_event_loop()
-    except RuntimeError:
-        loop = asyncio.new_event_loop()
-        asyncio.set_event_loop(loop)
-        return loop
-
-
-__all__ = ["WiFiDensePose", "__version__"]