feat: adaptive CSI classifier with signal smoothing (ADR-048)#144
Merged
feat: adaptive CSI classifier with signal smoothing (ADR-048)#144
Conversation
Add environment-tuned activity classification that learns from labeled ESP32 CSI recordings, replacing brittle static thresholds. - Adaptive classifier: 15-feature logistic regression trained from JSONL recordings (variance, motion band, subcarrier stats: skew, kurtosis, entropy, IQR). Trains in <1s, persists as JSON, auto-loads on restart. - Three-stage signal smoothing: adaptive baseline subtraction (α=0.003), EMA + trimmed-mean median filter (21-frame window), hysteresis debounce (4 frames). Motion classification now stable across seconds, not frames. - Vital signs stabilization: outlier rejection (±8 BPM HR, ±2 BPM BR), trimmed mean, dead-band (±2 BPM HR), EMA α=0.02. HR holds steady for 10+ seconds instead of jumping 50 BPM every frame. - Observatory auto-detect: always probes /health on startup, connects WebSocket to live ESP32 data automatically. - New API endpoints: POST /api/v1/adaptive/train, GET /adaptive/status, POST /adaptive/unload for runtime model management. - Updated user guide with Observatory, adaptive classifier tutorial, signal smoothing docs, and new troubleshooting entries. Co-Authored-By: claude-flow <ruv@ruv.net>
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary
adaptive_classifier.rs): Pure Rust 15-feature multiclass logistic regression trained from labeled JSONL recordings, with softmax inference and JSON model persistencemain.rs: adaptive baseline subtraction (α=0.003), EMA+median motion scoring, hysteresis debounce (4 frames) — stabilizes noisy ESP32 CSI dataCloses #143
Test plan
--source auto, verify ESP32 data flowstrain_empty_*,train_still_*,train_walking_*,train_active_*)POST /api/v1/adaptive/train, check accuracy viaGET /api/v1/adaptive/statusPOST /api/v1/adaptive/unloadreverts to threshold-based classification🤖 Generated with claude-flow