chore: update vendor submodules to latest main

README.md

@@ -1,20 +1,11 @@
 # π RuView
 
 <p align="center">
-  <a href="https://x.com/rUv/status/2037556932802761004">
+  <a href="https://ruvnet.github.io/RuView/">
     <img src="assets/ruview-small-gemini.jpg" alt="RuView - WiFi DensePose" width="100%">
   </a>
 </p>
 
-> **Alpha Software** — This project is under active development. APIs, firmware behavior, and documentation may change. Known limitations:
-> - Multi-node person counting may show identical output regardless of the number of people (#249)
-> - Training pipeline on MM-Fi dataset may plateau at low PCK (#318) — hyperparameter tuning in progress
-> - No pre-trained model weights are provided; training from scratch is required
-> - ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)
-> - Single ESP32 deployments have limited spatial resolution
->
-> Contributions and bug reports welcome at [Issues](https://github.com/ruvnet/RuView/issues).
-
 ## **See through walls with WiFi + Ai** ##
 
 **Perceive the world through signals.** No cameras. No wearables. No Internet. Just physics.
@@ -23,7 +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-068-per-node-state-pipeline.md

@@ -1,182 +0,0 @@
-# ADR-068: Per-Node State Pipeline for Multi-Node Sensing
-
-| Field      | Value                               |
-|------------|-------------------------------------|
-| Status     | Accepted                            |
-| Date       | 2026-03-27                          |
-| Authors    | rUv, claude-flow                    |
-| Drivers    | #249, #237, #276, #282              |
-| Supersedes | —                                   |
-
-## Context
-
-The sensing server (`wifi-densepose-sensing-server`) was originally designed for
-single-node operation. When multiple ESP32 nodes send CSI frames simultaneously,
-all data is mixed into a single shared pipeline:
-
-- **One** `frame_history` VecDeque for all nodes
-- **One** `smoothed_person_score` / `smoothed_motion` / vital sign buffers
-- **One** baseline and debounce state
-
-This means the classification, person count, and vital signs reported to the UI
-are an uncontrolled aggregate of all nodes' data. The result: the detection
-window shows identical output regardless of how many nodes are deployed, where
-people stand, or how many people are in the room (#249 — 24 comments, the most
-reported issue).
-
-### Root Cause Verified
-
-Investigation of `AppStateInner` (main.rs lines 279-367) confirmed:
-
-| Shared field              | Impact                                     |
-|---------------------------|--------------------------------------------|
-| `frame_history`           | Temporal analysis mixes all nodes' CSI data |
-| `smoothed_person_score`   | Person count aggregates all nodes           |
-| `smoothed_motion`         | Motion classification undifferentiated      |
-| `smoothed_hr` / `br`     | Vital signs are global, not per-node        |
-| `baseline_motion`         | Adaptive baseline learned from mixed data   |
-| `debounce_counter`        | All nodes share debounce state              |
-
-## Decision
-
-Introduce **per-node state tracking** via a `HashMap<u8, NodeState>` in
-`AppStateInner`. Each ESP32 node (identified by its `node_id` byte) gets an
-independent sensing pipeline with its own temporal history, smoothing buffers,
-baseline, and classification state.
-
-### Architecture
-
-```
-                     ┌─────────────────────────────────────────┐
-   UDP frames        │           AppStateInner                  │
-   ───────────►      │                                         │
-   node_id=1    ──►  │  node_states: HashMap<u8, NodeState>    │
-   node_id=2    ──►  │    ├── 1: NodeState { frame_history,    │
-   node_id=3    ──►  │    │      smoothed_motion, vitals, ... }│
-                     │    ├── 2: NodeState { ... }              │
-                     │    └── 3: NodeState { ... }              │
-                     │                                         │
-                     │  ┌── Per-Node Pipeline ──┐               │
-                     │  │ extract_features()     │               │
-                     │  │ smooth_and_classify()  │               │
-                     │  │ smooth_vitals()        │               │
-                     │  │ score_to_person_count()│               │
-                     │  └────────────────────────┘               │
-                     │                                         │
-                     │  ┌── Multi-Node Fusion ──┐               │
-                     │  │ Aggregate person count │               │
-                     │  │ Per-node classification│               │
-                     │  │ All-nodes WebSocket msg│               │
-                     │  └────────────────────────┘               │
-                     │                                         │
-                     │  ──► WebSocket broadcast (sensing_update) │
-                     └─────────────────────────────────────────┘
-```
-
-### NodeState Struct
-
-```rust
-struct NodeState {
-    frame_history: VecDeque<Vec<f64>>,
-    smoothed_person_score: f64,
-    prev_person_count: usize,
-    smoothed_motion: f64,
-    current_motion_level: String,
-    debounce_counter: u32,
-    debounce_candidate: String,
-    baseline_motion: f64,
-    baseline_frames: u64,
-    smoothed_hr: f64,
-    smoothed_br: f64,
-    smoothed_hr_conf: f64,
-    smoothed_br_conf: f64,
-    hr_buffer: VecDeque<f64>,
-    br_buffer: VecDeque<f64>,
-    rssi_history: VecDeque<f64>,
-    vital_detector: VitalSignDetector,
-    latest_vitals: VitalSigns,
-    last_frame_time: Option<std::time::Instant>,
-    edge_vitals: Option<Esp32VitalsPacket>,
-}
-```
-
-### Multi-Node Aggregation
-
-- **Person count**: Sum of per-node `prev_person_count` for active nodes
-  (seen within last 10 seconds).
-- **Classification**: Per-node classification included in `SensingUpdate.nodes`.
-- **Vital signs**: Per-node vital signs; UI can render per-node or aggregate.
-- **Signal field**: Generated from the most-recently-updated node's features.
-- **Stale nodes**: Nodes with no frame for >10 seconds are excluded from
-  aggregation and marked offline (consistent with PR #300).
-
-### Backward Compatibility
-
-- The simulated data path (`simulated_data_task`) continues using global state.
-- Single-node deployments behave identically (HashMap has one entry).
-- The WebSocket message format (`sensing_update`) remains the same but the
-  `nodes` array now contains all active nodes, and `estimated_persons` reflects
-  the cross-node aggregate.
-- The edge vitals path (#323 fix) also uses per-node state.
-
-## Scaling Characteristics
-
-| Nodes | Per-Node Memory | Total Overhead | Notes |
-|-------|----------------|----------------|-------|
-| 1     | ~50 KB         | ~50 KB         | Identical to current |
-| 3     | ~50 KB         | ~150 KB        | Typical home setup |
-| 10    | ~50 KB         | ~500 KB        | Small office |
-| 50    | ~50 KB         | ~2.5 MB        | Building floor |
-| 100   | ~50 KB         | ~5 MB          | Large deployment |
-| 256   | ~50 KB         | ~12.8 MB       | Max (u8 node_id) |
-
-Memory is dominated by `frame_history` (100 frames x ~500 bytes each = ~50 KB
-per node). This scales linearly and fits comfortably in server memory even at
-256 nodes.
-
-## QEMU Validation
-
-The existing QEMU swarm infrastructure (ADR-062, `scripts/qemu_swarm.py`)
-supports multi-node simulation with configurable topologies:
-
-- `star`: Central coordinator + sensor nodes
-- `mesh`: Fully connected peer network
-- `line`: Sequential chain
-- `ring`: Circular topology
-
-Each QEMU instance runs with a unique `node_id` via NVS provisioning. The
-swarm health validator (`scripts/swarm_health.py`) checks per-node UART output.
-
-Validation plan:
-1. QEMU swarm with 3-5 nodes in mesh topology
-2. Verify server produces distinct per-node classifications
-3. Verify aggregate person count reflects multi-node contributions
-4. Verify stale-node eviction after timeout
-
-## Consequences
-
-### Positive
-- Each node's CSI data is processed independently — no cross-contamination
-- Person count scales with the number of deployed nodes
-- Vital signs are per-node, enabling room-level health monitoring
-- Foundation for spatial localization (per-node positions + triangulation)
-- Scales to 256 nodes with <13 MB memory overhead
-
-### Negative
-- Slightly more memory per node (~50 KB each)
-- `smooth_and_classify_node` function duplicates some logic from global version
-- Per-node `VitalSignDetector` instances add CPU cost proportional to node count
-
-### Risks
-- Node ID collisions (mitigated by NVS persistence since v0.5.0)
-- HashMap growth without cleanup (mitigated by stale-node eviction)
-
-## References
-
-- Issue #249: Detection window same regardless (24 comments)
-- Issue #237: Same display for 0/1/2 people (12 comments)
-- Issue #276: Only one can be detected (8 comments)
-- Issue #282: Detection fail (5 comments)
-- PR #295: Hysteresis smoothing (partial mitigation)
-- PR #300: ESP32 offline detection after 5s
-- ADR-062: QEMU Swarm Configurator

firmware/esp32-csi-node/main/edge_processing.c

@@ -41,14 +41,12 @@ static const char *TAG = "edge_proc";
  * ====================================================================== */
 
 static edge_ring_buf_t s_ring;
-static uint32_t s_ring_drops;  /* Frames dropped due to full ring buffer. */
 
 static inline bool ring_push(const uint8_t *iq, uint16_t len,
                              int8_t rssi, uint8_t channel)
 {
     uint32_t next = (s_ring.head + 1) % EDGE_RING_SLOTS;
     if (next == s_ring.tail) {
-        s_ring_drops++;
         return false;  /* Full — drop frame. */
     }
 
@@ -790,13 +788,12 @@ static void process_frame(const edge_ring_slot_t *slot)
 
         if ((s_frame_count % 200) == 0) {
             ESP_LOGI(TAG, "Vitals: br=%.1f hr=%.1f motion=%.4f pres=%s "
-                     "fall=%s persons=%u frames=%lu drops=%lu",
+                     "fall=%s persons=%u frames=%lu",
                      s_breathing_bpm, s_heartrate_bpm, s_motion_energy,
                      s_presence_detected ? "YES" : "no",
                      s_fall_detected ? "YES" : "no",
                      (unsigned)s_latest_pkt.n_persons,
-                     (unsigned long)s_frame_count,
+                     (unsigned long)s_frame_count);
-                     (unsigned long)s_ring_drops);
         }
     }
 
@@ -834,32 +831,18 @@ static void edge_task(void *arg)
 
     edge_ring_slot_t slot;
 
-    /* Maximum frames to process before a longer yield.  On busy LANs
-     * (corporate networks, many APs), the ring buffer fills continuously.
-     * Without a batch limit the task processes frames back-to-back with
-     * only 1-tick yields, which on high frame rates can still starve
-     * IDLE1 enough to trip the 5-second task watchdog.  See #266, #321. */
-    const uint8_t BATCH_LIMIT = 4;
-
     while (1) {
-        uint8_t processed = 0;
+        if (ring_pop(&slot)) {
-
-        while (processed < BATCH_LIMIT && ring_pop(&slot)) {
             process_frame(&slot);
-            processed++;
+            /* Yield after every frame to feed the Core 1 watchdog.
-            /* 1-tick yield between frames within a batch. */
+             * process_frame() is CPU-intensive (biquad filters, Welford stats,
+             * BPM estimation, multi-person vitals) and can take several ms.
+             * Without this yield, edge_dsp at priority 5 starves IDLE1 at
+             * priority 0, triggering the task watchdog. See issue #266. */
             vTaskDelay(1);
-        }
-
-        if (processed > 0) {
-            /* Post-batch yield: 2 ticks (~20 ms at 100 Hz) so IDLE1 can
-             * run and feed the Core 1 watchdog even under sustained load.
-             * This is intentionally longer than the 1-tick inter-frame yield. */
-            vTaskDelay(2);
         } else {
-            /* No frames available — sleep one full tick.
+            /* No frames available — yield briefly. */
-             * NOTE: pdMS_TO_TICKS(5) == 0 at 100 Hz, which would busy-spin. */
+            vTaskDelay(pdMS_TO_TICKS(1));
-            vTaskDelay(1);
         }
     }
 }

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

@@ -16,7 +16,7 @@ mod vital_signs;
 // Training pipeline modules (exposed via lib.rs)
 use wifi_densepose_sensing_server::{graph_transformer, trainer, dataset, embedding};
 
-use std::collections::{HashMap, VecDeque};
+use std::collections::VecDeque;
 use std::net::SocketAddr;
 use std::path::PathBuf;
 use std::sync::Arc;
@@ -275,59 +275,6 @@ struct BoundingBox {
     height: f64,
 }
 
-/// Per-node sensing state for multi-node deployments (issue #249).
-/// Each ESP32 node gets its own frame history, smoothing buffers, and vital
-/// sign detector so that data from different nodes is never mixed.
-struct NodeState {
-    frame_history: VecDeque<Vec<f64>>,
-    smoothed_person_score: f64,
-    prev_person_count: usize,
-    smoothed_motion: f64,
-    current_motion_level: String,
-    debounce_counter: u32,
-    debounce_candidate: String,
-    baseline_motion: f64,
-    baseline_frames: u64,
-    smoothed_hr: f64,
-    smoothed_br: f64,
-    smoothed_hr_conf: f64,
-    smoothed_br_conf: f64,
-    hr_buffer: VecDeque<f64>,
-    br_buffer: VecDeque<f64>,
-    rssi_history: VecDeque<f64>,
-    vital_detector: VitalSignDetector,
-    latest_vitals: VitalSigns,
-    last_frame_time: Option<std::time::Instant>,
-    edge_vitals: Option<Esp32VitalsPacket>,
-}
-
-impl NodeState {
-    fn new() -> Self {
-        Self {
-            frame_history: VecDeque::new(),
-            smoothed_person_score: 0.0,
-            prev_person_count: 0,
-            smoothed_motion: 0.0,
-            current_motion_level: "absent".to_string(),
-            debounce_counter: 0,
-            debounce_candidate: "absent".to_string(),
-            baseline_motion: 0.0,
-            baseline_frames: 0,
-            smoothed_hr: 0.0,
-            smoothed_br: 0.0,
-            smoothed_hr_conf: 0.0,
-            smoothed_br_conf: 0.0,
-            hr_buffer: VecDeque::with_capacity(8),
-            br_buffer: VecDeque::with_capacity(8),
-            rssi_history: VecDeque::new(),
-            vital_detector: VitalSignDetector::new(10.0),
-            latest_vitals: VitalSigns::default(),
-            last_frame_time: None,
-            edge_vitals: None,
-        }
-    }
-}
-
 /// Shared application state
 struct AppStateInner {
     latest_update: Option<SensingUpdate>,
@@ -417,10 +364,6 @@ struct AppStateInner {
     // ── Adaptive classifier (environment-tuned) ──────────────────────────
     /// Trained adaptive model (loaded from data/adaptive_model.json or trained at runtime).
     adaptive_model: Option<adaptive_classifier::AdaptiveModel>,
-    // ── Per-node state (issue #249) ─────────────────────────────────────
-    /// Per-node sensing state for multi-node deployments.
-    /// Keyed by `node_id` from the ESP32 frame header.
-    node_states: HashMap<u8, NodeState>,
 }
 
 /// If no ESP32 frame arrives within this duration, source reverts to offline.
@@ -1021,44 +964,6 @@ fn smooth_and_classify(state: &mut AppStateInner, raw: &mut ClassificationInfo,
     raw.confidence = (0.4 + sm * 0.6).clamp(0.0, 1.0);
 }
 
-/// Per-node variant of `smooth_and_classify` that operates on a `NodeState`
-/// instead of `AppStateInner` (issue #249).
-fn smooth_and_classify_node(ns: &mut NodeState, raw: &mut ClassificationInfo, raw_motion: f64) {
-    ns.baseline_frames += 1;
-    if ns.baseline_frames < BASELINE_WARMUP {
-        ns.baseline_motion = ns.baseline_motion * 0.9 + raw_motion * 0.1;
-    } else if raw_motion < ns.smoothed_motion + 0.05 {
-        ns.baseline_motion = ns.baseline_motion * (1.0 - BASELINE_EMA_ALPHA)
-                           + raw_motion * BASELINE_EMA_ALPHA;
-    }
-
-    let adjusted = (raw_motion - ns.baseline_motion * 0.7).max(0.0);
-
-    ns.smoothed_motion = ns.smoothed_motion * (1.0 - MOTION_EMA_ALPHA)
-                       + adjusted * MOTION_EMA_ALPHA;
-    let sm = ns.smoothed_motion;
-
-    let candidate = raw_classify(sm);
-
-    if candidate == ns.current_motion_level {
-        ns.debounce_counter = 0;
-        ns.debounce_candidate = candidate;
-    } else if candidate == ns.debounce_candidate {
-        ns.debounce_counter += 1;
-        if ns.debounce_counter >= DEBOUNCE_FRAMES {
-            ns.current_motion_level = candidate;
-            ns.debounce_counter = 0;
-        }
-    } else {
-        ns.debounce_candidate = candidate;
-        ns.debounce_counter = 1;
-    }
-
-    raw.motion_level = ns.current_motion_level.clone();
-    raw.presence = sm > 0.03;
-    raw.confidence = (0.4 + sm * 0.6).clamp(0.0, 1.0);
-}
-
 /// If an adaptive model is loaded, override the classification with the
 /// model's prediction.  Uses the full 15-feature vector for higher accuracy.
 fn adaptive_override(state: &AppStateInner, features: &FeatureInfo, classification: &mut ClassificationInfo) {
@@ -1159,55 +1064,6 @@ fn smooth_vitals(state: &mut AppStateInner, raw: &VitalSigns) -> VitalSigns {
     }
 }
 
-/// Per-node variant of `smooth_vitals` that operates on a `NodeState` (issue #249).
-fn smooth_vitals_node(ns: &mut NodeState, raw: &VitalSigns) -> VitalSigns {
-    let raw_hr = raw.heart_rate_bpm.unwrap_or(0.0);
-    let raw_br = raw.breathing_rate_bpm.unwrap_or(0.0);
-
-    let hr_ok = ns.smoothed_hr < 1.0 || (raw_hr - ns.smoothed_hr).abs() < HR_MAX_JUMP;
-    let br_ok = ns.smoothed_br < 1.0 || (raw_br - ns.smoothed_br).abs() < BR_MAX_JUMP;
-
-    if hr_ok && raw_hr > 0.0 {
-        ns.hr_buffer.push_back(raw_hr);
-        if ns.hr_buffer.len() > VITAL_MEDIAN_WINDOW { ns.hr_buffer.pop_front(); }
-    }
-    if br_ok && raw_br > 0.0 {
-        ns.br_buffer.push_back(raw_br);
-        if ns.br_buffer.len() > VITAL_MEDIAN_WINDOW { ns.br_buffer.pop_front(); }
-    }
-
-    let trimmed_hr = trimmed_mean(&ns.hr_buffer);
-    let trimmed_br = trimmed_mean(&ns.br_buffer);
-
-    if trimmed_hr > 0.0 {
-        if ns.smoothed_hr < 1.0 {
-            ns.smoothed_hr = trimmed_hr;
-        } else if (trimmed_hr - ns.smoothed_hr).abs() > HR_DEAD_BAND {
-            ns.smoothed_hr = ns.smoothed_hr * (1.0 - VITAL_EMA_ALPHA)
-                           + trimmed_hr * VITAL_EMA_ALPHA;
-        }
-    }
-    if trimmed_br > 0.0 {
-        if ns.smoothed_br < 1.0 {
-            ns.smoothed_br = trimmed_br;
-        } else if (trimmed_br - ns.smoothed_br).abs() > BR_DEAD_BAND {
-            ns.smoothed_br = ns.smoothed_br * (1.0 - VITAL_EMA_ALPHA)
-                           + trimmed_br * VITAL_EMA_ALPHA;
-        }
-    }
-
-    ns.smoothed_hr_conf = ns.smoothed_hr_conf * 0.92 + raw.heartbeat_confidence * 0.08;
-    ns.smoothed_br_conf = ns.smoothed_br_conf * 0.92 + raw.breathing_confidence * 0.08;
-
-    VitalSigns {
-        breathing_rate_bpm: if ns.smoothed_br > 1.0 { Some(ns.smoothed_br) } else { None },
-        heart_rate_bpm: if ns.smoothed_hr > 1.0 { Some(ns.smoothed_hr) } else { None },
-        breathing_confidence: ns.smoothed_br_conf,
-        heartbeat_confidence: ns.smoothed_hr_conf,
-        signal_quality: raw.signal_quality,
-    }
-}
-
 /// Trimmed mean: sort, drop top/bottom 25%, average the middle 50%.
 /// More robust than median (uses more data) and less noisy than raw mean.
 fn trimmed_mean(buf: &VecDeque<f64>) -> f64 {
@@ -2964,115 +2820,6 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
                     })) {
                         let _ = s.tx.send(json);
                     }
-
-                    // Issue #323: Also emit a sensing_update so the UI renders
-                    // detections for ESP32 nodes running the edge DSP pipeline
-                    // (Tier 2+).  Without this, vitals arrive but the UI shows
-                    // "no detection" because it only renders sensing_update msgs.
-                    s.source = "esp32".to_string();
-                    s.last_esp32_frame = Some(std::time::Instant::now());
-
-                    // ── Per-node state for edge vitals (issue #249) ──────
-                    let node_id = vitals.node_id;
-                    let ns = s.node_states.entry(node_id).or_insert_with(NodeState::new);
-                    ns.last_frame_time = Some(std::time::Instant::now());
-                    ns.edge_vitals = Some(vitals.clone());
-                    ns.rssi_history.push_back(vitals.rssi as f64);
-                    if ns.rssi_history.len() > 60 { ns.rssi_history.pop_front(); }
-
-                    // Store per-node person count from edge vitals.
-                    let node_est = if vitals.presence {
-                        (vitals.n_persons as usize).max(1)
-                    } else {
-                        0
-                    };
-                    ns.prev_person_count = node_est;
-
-                    s.tick += 1;
-                    let tick = s.tick;
-
-                    let motion_level = if vitals.motion { "present_moving" }
-                        else if vitals.presence { "present_still" }
-                        else { "absent" };
-                    let motion_score = if vitals.motion { 0.8 }
-                        else if vitals.presence { 0.3 }
-                        else { 0.05 };
-
-                    // Aggregate person count across all active nodes.
-                    let now = std::time::Instant::now();
-                    let total_persons: usize = s.node_states.values()
-                        .filter(|n| n.last_frame_time.map_or(false, |t| now.duration_since(t).as_secs() < 10))
-                        .map(|n| n.prev_person_count)
-                        .sum();
-
-                    // Build nodes array with all active nodes.
-                    let active_nodes: Vec<NodeInfo> = s.node_states.iter()
-                        .filter(|(_, n)| n.last_frame_time.map_or(false, |t| now.duration_since(t).as_secs() < 10))
-                        .map(|(&id, n)| NodeInfo {
-                            node_id: id,
-                            rssi_dbm: n.rssi_history.back().copied().unwrap_or(0.0),
-                            position: [2.0, 0.0, 1.5],
-                            amplitude: vec![],
-                            subcarrier_count: 0,
-                        })
-                        .collect();
-
-                    let features = FeatureInfo {
-                        mean_rssi: vitals.rssi as f64,
-                        variance: vitals.motion_energy as f64,
-                        motion_band_power: vitals.motion_energy as f64,
-                        breathing_band_power: if vitals.presence { 0.5 } else { 0.0 },
-                        dominant_freq_hz: vitals.breathing_rate_bpm / 60.0,
-                        change_points: 0,
-                        spectral_power: vitals.motion_energy as f64,
-                    };
-                    let classification = ClassificationInfo {
-                        motion_level: motion_level.to_string(),
-                        presence: vitals.presence,
-                        confidence: vitals.presence_score as f64,
-                    };
-                    let signal_field = generate_signal_field(
-                        vitals.rssi as f64, motion_score, vitals.breathing_rate_bpm / 60.0,
-                        (vitals.presence_score as f64).min(1.0), &[],
-                    );
-
-                    let mut update = SensingUpdate {
-                        msg_type: "sensing_update".to_string(),
-                        timestamp: chrono::Utc::now().timestamp_millis() as f64 / 1000.0,
-                        source: "esp32".to_string(),
-                        tick,
-                        nodes: active_nodes,
-                        features: features.clone(),
-                        classification,
-                        signal_field,
-                        vital_signs: Some(VitalSigns {
-                            breathing_rate_bpm: if vitals.breathing_rate_bpm > 0.0 { Some(vitals.breathing_rate_bpm) } else { None },
-                            heart_rate_bpm: if vitals.heartrate_bpm > 0.0 { Some(vitals.heartrate_bpm) } else { None },
-                            breathing_confidence: if vitals.presence { 0.7 } else { 0.0 },
-                            heartbeat_confidence: if vitals.presence { 0.7 } else { 0.0 },
-                            signal_quality: vitals.presence_score as f64,
-                        }),
-                        enhanced_motion: None,
-                        enhanced_breathing: None,
-                        posture: None,
-                        signal_quality_score: None,
-                        quality_verdict: None,
-                        bssid_count: None,
-                        pose_keypoints: None,
-                        model_status: None,
-                        persons: None,
-                        estimated_persons: if total_persons > 0 { Some(total_persons) } else { None },
-                    };
-
-                    let persons = derive_pose_from_sensing(&update);
-                    if !persons.is_empty() {
-                        update.persons = Some(persons);
-                    }
-
-                    if let Ok(json) = serde_json::to_string(&update) {
-                        let _ = s.tx.send(json);
-                    }
-                    s.latest_update = Some(update);
                     s.edge_vitals = Some(vitals);
                     continue;
                 }
@@ -3104,90 +2851,24 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
                     s.source = "esp32".to_string();
                     s.last_esp32_frame = Some(std::time::Instant::now());
 
-                    // Also maintain global frame_history for backward compat
+                    // Append current amplitudes to history before extracting features so
-                    // (simulation path, REST endpoints, etc.).
+                    // that temporal analysis includes the most recent frame.
                     s.frame_history.push_back(frame.amplitudes.clone());
                     if s.frame_history.len() > FRAME_HISTORY_CAPACITY {
                         s.frame_history.pop_front();
                     }
 
-                    // ── Per-node processing (issue #249) ──────────────────
+                    let sample_rate_hz = 1000.0 / 500.0_f64; // default tick; ESP32 frames arrive as fast as they come
-                    // Process entirely within per-node state so different
-                    // ESP32 nodes never mix their smoothing/vitals buffers.
-                    // We scope the mutable borrow of node_states so we can
-                    // access other AppStateInner fields afterward.
-                    let node_id = frame.node_id;
-                    let adaptive_model_ref = s.adaptive_model.as_ref().map(|m| m as *const _);
-                    let ns = s.node_states.entry(node_id).or_insert_with(NodeState::new);
-                    ns.last_frame_time = Some(std::time::Instant::now());
-
-                    ns.frame_history.push_back(frame.amplitudes.clone());
-                    if ns.frame_history.len() > FRAME_HISTORY_CAPACITY {
-                        ns.frame_history.pop_front();
-                    }
-
-                    let sample_rate_hz = 1000.0 / 500.0_f64;
                     let (features, mut classification, breathing_rate_hz, sub_variances, raw_motion) =
-                        extract_features_from_frame(&frame, &ns.frame_history, sample_rate_hz);
+                        extract_features_from_frame(&frame, &s.frame_history, sample_rate_hz);
-                    smooth_and_classify_node(ns, &mut classification, raw_motion);
+                    smooth_and_classify(&mut s, &mut classification, raw_motion);
-
+    adaptive_override(&s, &features, &mut classification);
-                    // SAFETY: adaptive_model_ref points into s which we hold
-                    // via write lock; the model is not mutated here. We use a
-                    // raw pointer to break the borrow-checker deadlock between
-                    // node_states and adaptive_model (both inside s).
-                    if let Some(model_ptr) = adaptive_model_ref {
-                        let model: &adaptive_classifier::AdaptiveModel = unsafe { &*model_ptr };
-                        let amps = ns.frame_history.back()
-                            .map(|v| v.as_slice())
-                            .unwrap_or(&[]);
-                        let feat_arr = adaptive_classifier::features_from_runtime(
-                            &serde_json::json!({
-                                "variance": features.variance,
-                                "motion_band_power": features.motion_band_power,
-                                "breathing_band_power": features.breathing_band_power,
-                                "spectral_power": features.spectral_power,
-                                "dominant_freq_hz": features.dominant_freq_hz,
-                                "change_points": features.change_points,
-                                "mean_rssi": features.mean_rssi,
-                            }),
-                            amps,
-                        );
-                        let (label, conf) = model.classify(&feat_arr);
-                        classification.motion_level = label.to_string();
-                        classification.presence = label != "absent";
-                        classification.confidence = (conf * 0.7 + classification.confidence * 0.3).clamp(0.0, 1.0);
-                    }
-
-                    ns.rssi_history.push_back(features.mean_rssi);
-                    if ns.rssi_history.len() > 60 {
-                        ns.rssi_history.pop_front();
-                    }
-
-                    let raw_vitals = ns.vital_detector.process_frame(
-                        &frame.amplitudes,
-                        &frame.phases,
-                    );
-                    let vitals = smooth_vitals_node(ns, &raw_vitals);
-                    ns.latest_vitals = vitals.clone();
-
-                    let raw_score = compute_person_score(&features);
-                    ns.smoothed_person_score = ns.smoothed_person_score * 0.90 + raw_score * 0.10;
-                    if classification.presence {
-                        let count = score_to_person_count(ns.smoothed_person_score, ns.prev_person_count);
-                        ns.prev_person_count = count;
-                    } else {
-                        ns.prev_person_count = 0;
-                    }
-
-                    // Done with per-node mutable borrow; now read aggregated
-                    // state from all nodes (the borrow of `ns` ends here).
-                    // (We re-borrow node_states immutably via `s` below.)
 
+                    // Update RSSI history
                     s.rssi_history.push_back(features.mean_rssi);
                     if s.rssi_history.len() > 60 {
                         s.rssi_history.pop_front();
                     }
-                    s.latest_vitals = vitals.clone();
 
                     s.tick += 1;
                     let tick = s.tick;
@@ -3196,33 +2877,37 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
                         else if classification.motion_level == "present_still" { 0.3 }
                         else { 0.05 };
 
-                    // Aggregate person count across all active nodes.
+                    let raw_vitals = s.vital_detector.process_frame(
-                    let now = std::time::Instant::now();
+                        &frame.amplitudes,
-                    let total_persons: usize = s.node_states.values()
+                        &frame.phases,
-                        .filter(|n| n.last_frame_time.map_or(false, |t| now.duration_since(t).as_secs() < 10))
+                    );
-                        .map(|n| n.prev_person_count)
+                    let vitals = smooth_vitals(&mut s, &raw_vitals);
-                        .sum();
+                    s.latest_vitals = vitals.clone();
 
-                    // Build nodes array with all active nodes.
+                    // Multi-person estimation with temporal smoothing (EMA α=0.10).
-                    let active_nodes: Vec<NodeInfo> = s.node_states.iter()
+                    let raw_score = compute_person_score(&features);
-                        .filter(|(_, n)| n.last_frame_time.map_or(false, |t| now.duration_since(t).as_secs() < 10))
+                    s.smoothed_person_score = s.smoothed_person_score * 0.90 + raw_score * 0.10;
-                        .map(|(&id, n)| NodeInfo {
+                    let est_persons = if classification.presence {
-                            node_id: id,
+                        let count = score_to_person_count(s.smoothed_person_score, s.prev_person_count);
-                            rssi_dbm: n.rssi_history.back().copied().unwrap_or(0.0),
+                        s.prev_person_count = count;
-                            position: [2.0, 0.0, 1.5],
+                        count
-                            amplitude: n.frame_history.back()
+                    } else {
-                                .map(|a| a.iter().take(56).cloned().collect())
+                        s.prev_person_count = 0;
-                                .unwrap_or_default(),
+                        0
-                            subcarrier_count: n.frame_history.back().map_or(0, |a| a.len()),
+                    };
-                        })
-                        .collect();
 
                     let mut update = SensingUpdate {
                         msg_type: "sensing_update".to_string(),
                         timestamp: chrono::Utc::now().timestamp_millis() as f64 / 1000.0,
                         source: "esp32".to_string(),
                         tick,
-                        nodes: active_nodes,
+                        nodes: vec![NodeInfo {
+                            node_id: frame.node_id,
+                            rssi_dbm: features.mean_rssi,
+                            position: [2.0, 0.0, 1.5],
+                            amplitude: frame.amplitudes.iter().take(56).cloned().collect(),
+                            subcarrier_count: frame.n_subcarriers as usize,
+                        }],
                         features: features.clone(),
                         classification,
                         signal_field: generate_signal_field(
@@ -3239,7 +2924,7 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
                         pose_keypoints: None,
                         model_status: None,
                         persons: None,
-                        estimated_persons: if total_persons > 0 { Some(total_persons) } else { None },
+                        estimated_persons: if est_persons > 0 { Some(est_persons) } else { None },
                     };
 
                     let persons = derive_pose_from_sensing(&update);
@@ -3991,7 +3676,6 @@ async fn main() {
                   m.trained_frames, m.training_accuracy * 100.0);
             m
         }),
-        node_states: HashMap::new(),
     }));
 
     // Start background tasks based on source

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__"]