user_accelerometer_walk_detection.cpp

Simple walk detection using an accelerometer. This uses a very simple algorithm: thresholding the standard deviation of energy (dot product) of the acceleration vector over a history of samples. The choice of this algorithm is based on the discussion in "Walk detection and step counting on unconstrained smartphones" by Agata Brajdic and Robert Harle http://dl.acm.org/citation.cfm?id=2493449 which suggests that this is as good (or better) than more complex methods.

To use, upload the Arduino101_Accelerometer_100Hz to an Arduino 101. After calibration, place the Arduino 101 in a front pants pocket.

It's certainly possible to fool this algorithm into thinking that you are walking when you aren't (e.g. by jumping up and down). It also might require lowering of the threshold to detect slow walking. But, in general, this should be at least useful for experimenting.

#include <ESP.h>

ASCIISerialStream stream(9600, 3);
GestureRecognitionPipeline pipeline;
Calibrator calibrator;
TcpOStream oStream("localhost", 5204);

double zeroG = 0, oneG = 0;

double processAccelerometerData(double input)
{
    return (input - zeroG) / (oneG - zeroG);
}

CalibrateResult restingDataCollected(const MatrixDouble& data)
{
    // take average of X and Y acceleration as the zero G value
    zeroG = (data.getMean()[0] + data.getMean()[1]) / 2;
    oneG = data.getMean()[2]; // use Z acceleration as one G value
    
    double range = abs(oneG - zeroG);
    vector<double> stddev = data.getStdDev();
    
    if (stddev[0] / range > 0.05 ||
        stddev[1] / range > 0.05 ||
        stddev[2] / range > 0.05)
        return CalibrateResult(CalibrateResult::WARNING,
            "Accelerometer seemed to be moving; consider recollecting the "
            "calibration sample.");
    
    if (abs(data.getMean()[0] - data.getMean()[1]) / range > 0.1)
        return CalibrateResult(CalibrateResult::WARNING,
            "X and Y axes differ by " + std::to_string(
            abs(data.getMean()[0] - data.getMean()[1]) / range * 100) +
            " percent. Check that accelerometer is flat.");

    return CalibrateResult::SUCCESS;
}

VectorDouble dotProduct(VectorDouble in)
{
    VectorDouble out(1, 0);
    
    for (int i = 0; i < in.size(); i++)
        out[0] += in[i] * in[i];
    
    return out;
}

VectorDouble stddev(VectorDouble v) {
    double sum = std::accumulate(v.begin(), v.end(), 0.0);
    double mean = sum / v.size();

    double sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), 0.0);
    double stdev = std::sqrt(sq_sum / v.size() - mean * mean);
    
    return VectorDouble(1, stdev);
}

double t = 0.6;
VectorDouble threshold(VectorDouble in) {
    VectorDouble out(in.size());
    for (int i = 0; i < in.size(); i++)
        out[i] = (in[i] > t) ? 1.0 : 0.0;
    return out;
}

void setup()
{
    stream.setLabelsForAllDimensions({"x", "y", "z"});
    useStream(stream);
    useOutputStream(oStream);

    calibrator.setCalibrateFunction(processAccelerometerData);
    calibrator.addCalibrateProcess("Resting",
        "Rest accelerometer on flat surface.", restingDataCollected);
    useCalibrator(calibrator);

    pipeline.addFeatureExtractionModule(FeatureApply(3, 1, dotProduct));
    pipeline.addFeatureExtractionModule(TimeseriesBuffer(80, 1));
    pipeline.addFeatureExtractionModule(FeatureApply(80, 1, stddev));
    pipeline.addFeatureExtractionModule(FeatureApply(1, 1, threshold));
    usePipeline(pipeline);
    
    registerTuneable(t, 0, 1.0, "Walking Threshold",
        "How much the accelerometer data needs to be "
        "changing to be considered walking.");
}