android传感器数据的线性化（加速度计）

Question

我正在研究使用 Android 手机中的传感器准确检测用户的脚步。我主要使用加速度计传感器来检测脚步。我使用了一种方法来获取加速度计数据并寻找一种方法来消除加速度计数据中不需要的噪声。我发现数据的线性化是一个很好的方法。但我不太清楚该怎么做。我认为它获得了中值。所以我没有找到一种实时获取中值的方法。谁能建议我一种更好的线性化方法来消除加速度计数据的噪声。

这是我的代码。（我使用 NChart 库在图表中绘制加速度计数据。）

package com.android.gait;

import org.achartengine.GraphicalView;

import android.hardware.Sensor;
import android.hardware.SensorEvent;
import android.hardware.SensorEventListener;
import android.hardware.SensorListener;

import android.hardware.SensorManager;
import android.os.Bundle;
import android.app.Activity;
import android.content.Context;
import android.view.Menu;
import android.view.View;
import android.widget.LinearLayout;
import android.widget.RelativeLayout;
import android.widget.TextView;

public class MainActivity extends Activity implements SensorEventListener{

    private int count=0;
    private static GraphicalView view;
    private LineGraph line = new LineGraph();
    private static Thread thread;
    private SensorManager mSensorManager;
    private Sensor mAccelerometer;
    TextView title,tv,tv1,tv2;
    RelativeLayout layout;
    private static Point p;
static float m = 0;

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);
         //get the sensor service
           mSensorManager = (SensorManager) getSystemService(Context.SENSOR_SERVICE);
           //get the accelerometer sensor
           mAccelerometer = mSensorManager.getDefaultSensor(Sensor.TYPE_ACCELEROMETER);
           //get layout
           layout = (RelativeLayout)findViewById(R.id.relative);
           LinearLayout layout = (LinearLayout) findViewById(R.id.layoutC);
           view= line.getView(this);
            layout.addView(view);
            //get textviews
           title=(TextView)findViewById(R.id.name);
           tv=(TextView)findViewById(R.id.xval);
           tv1=(TextView)findViewById(R.id.yval);
           tv2=(TextView)findViewById(R.id.zval);

           thread = new Thread(){
             int iniX=0;  
               public void run()
               {
                 while(true)
                 {

                     try {
                        Thread.sleep(1);
                    } catch (InterruptedException e) {
                        // TODO Auto-generated catch block
                        e.printStackTrace();
                    }
                    iniX=+1;

                     line.addNewPoint(iniX,m);
                     view.repaint();
                    }
                 }  


           };

           thread.start();


    }

      public final void onAccuracyChanged(Sensor sensor, int accuracy)
       {
         // Do something here if sensor accuracy changes.
       }
    @Override
     public final void onSensorChanged(SensorEvent event)
       {
        count=+1;
        // Many sensors return 3 values, one for each axis.
       float x = event.values[0];
       float y = event.values[1];
       float z = event.values[2];

       //get merged value
        m = (float) Math.sqrt(x*x+y*y+z*z);


       // p =MockData.getDataFromReceiver(count, m);


       //display values using TextView
       title.setText(R.string.app_name);
       tv.setText("X axis" +"\t\t"+x);
       tv1.setText("Y axis" + "\t\t" +y);
       tv2.setText("Z axis" +"\t\t" +z);
       }

    @Override
    protected void onResume()
    {
    super.onResume();
    mSensorManager.registerListener(this, mAccelerometer, SensorManager.SENSOR_DELAY_NORMAL);
    }
    @Override
    protected void onPause()
    {
    super.onPause();
    mSensorManager.unregisterListener(this);
    }

    public void LineGraphHandler(View view){



    }


}

Answer 1

存在多种算法来消除数据中的噪声，需要进行一些实验才能找出最佳算法。当我上次不得不处理遥测数据时，我让 Android 将其放入 CSV，然后使用 R 对其进行分析。但是，如果您无法使用 Java，我会看看 commons-math，特别是他们的Kalman filter。一个例子：

    // discrete time interval
    double dt = 0.1d;
    // position measurement noise (meter)
    double measurementNoise = 10d;
    // acceleration noise (meter/sec^2)
    double accelNoise = 0.2d;

    // A = [ 1 dt ]
    //     [ 0  1 ]
    RealMatrix A = new Array2DRowRealMatrix(new double[][] { { 1, dt }, { 0, 1 } });

    // B = [ dt^2/2 ]
    //     [ dt     ]
    RealMatrix B = new Array2DRowRealMatrix(
            new double[][] { { Math.pow(dt, 2d) / 2d }, { dt } });

    // H = [ 1 0 ]
    RealMatrix H = new Array2DRowRealMatrix(new double[][] { { 1d, 0d } });

    // x = [ 0 0 ]
    RealVector x = new ArrayRealVector(new double[] { 0, 0 });

    RealMatrix tmp = new Array2DRowRealMatrix(
            new double[][] { { Math.pow(dt, 4d) / 4d, Math.pow(dt, 3d) / 2d },
                             { Math.pow(dt, 3d) / 2d, Math.pow(dt, 2d) } });

    // Q = [ dt^4/4 dt^3/2 ]
    //     [ dt^3/2 dt^2   ]
    RealMatrix Q = tmp.scalarMultiply(Math.pow(accelNoise, 2));

    // P0 = [ 1 1 ]
    //      [ 1 1 ]
    RealMatrix P0 = new Array2DRowRealMatrix(new double[][] { { 1, 1 }, { 1, 1 } });

    // R = [ measurementNoise^2 ]
    RealMatrix R = new Array2DRowRealMatrix(
            new double[] { Math.pow(measurementNoise, 2) });

    // constant control input, increase velocity by 0.1 m/s per cycle
    RealVector u = new ArrayRealVector(new double[] { 0.1d });

    ProcessModel pm = new DefaultProcessModel(A, B, Q, x, P0);
    MeasurementModel mm = new DefaultMeasurementModel(H, R);
    KalmanFilter filter = new KalmanFilter(pm, mm);

希望这会有所帮助。如果您需要进一步的帮助，请随时发表评论。