wifiCsiProcessingEntry function

void wifiCsiProcessingEntry(dynamic mainSendPortArg)

Entry point for the background isolate doing compute-heavy CSI signal processing (OFDM subcarrier analysis for WiFi sensing).

Receives {'timestamp': int, 'amplitudes': List<double>} frames and emits processed subject updates as maps.

Implementation

void wifiCsiProcessingEntry(dynamic mainSendPortArg) {
  final mainSendPort = mainSendPortArg as SendPort;
  final receivePort = ReceivePort();
  mainSendPort.send(receivePort.sendPort);

  // Track state inside Isolate
  final List<double> history = [];

  receivePort.listen((message) {
    if (message is Map<String, dynamic>) {
      final List<double> amplitudes = List<double>.from(message['amplitudes']);

      // SOTA CSI processing: Compute standard deviation of subcarriers
      // to detect multipath phase/amplitude disturbance caused by movement
      double sum = 0.0;
      for (final val in amplitudes) {
        sum += val;
      }
      final mean = sum / amplitudes.length;

      double sqDiffSum = 0.0;
      for (final val in amplitudes) {
        sqDiffSum += (val - mean) * (val - mean);
      }
      final variance = sqDiffSum / amplitudes.length;
      final stdDev = sqrt(variance);

      // Keep running history for sliding window (vital signs respiration detection)
      history.add(stdDev);
      if (history.length > 50) history.removeAt(0);

      // Analyze respiration rate: count zero-crossings of bandpassed variance
      double zeroCrossings = 0;
      for (var i = 1; i < history.length; i++) {
        if ((history[i] - 1.0) * (history[i - 1] - 1.0) < 0) {
          zeroCrossings++;
        }
      }
      // Respiration rate estimation in breaths per minute (typically 12 - 20 bpm)
      final estimatedResp = 12.0 + (zeroCrossings * 0.4).clamp(0.0, 8.0);

      // Estimate subject coordinates based on multi-antenna amplitude ratios (Trilateration)
      final double dist = 1.0 + (5.0 / (mean + 0.1)).clamp(0.0, 5.0);

      // Simulate a circular walking trajectory based on time
      final double timeSecs = message['timestamp'] / 1000.0;
      final double px = sin(timeSecs * 0.5) * dist;
      final double py =
          1.0 +
          sin(timeSecs * estimatedResp * 0.1) *
              0.02; // breathing chest displacement
      final double pz = -2.0 + cos(timeSecs * 0.5) * dist;

      final isMoving = stdDev > 0.15;

      // Return processed coordinates & state back to the main thread
      mainSendPort.send({
        'id': 'subject_alpha',
        'px': px,
        'py': py,
        'pz': pz,
        'respiration': estimatedResp,
        'intensity': stdDev,
        'isMoving': isMoving,
      });
    }
  });
}