Question

我正在研究使用Android手机中的传感器准确检测用户脚步。我主要使用加速计传感器来检测脚步。我使用一种方法来获取加速度计数据并搜索一种方法来去除加速度计数据中不需要的噪声。我发现数据线性化是一种很好的方法。但我不清楚如何做到这一点。我认为它有中等价值。所以我没有找到一种以实时方式获得中值的方法。任何人都可以建议我一个更好的lenearization方法来消除加速度计数据的噪音。

这是我的代码。（我使用NChart库在图表中绘制加速计数据。）

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

存在各种用于从数据中去除噪声的算法，并且需要一些实验来找出哪个是最佳的。当我最后不得不弄乱遥测数据时，我让Android将其放入CSV中，然后使用R进行分析。但是，如果您使用Java，我会看一下commons-math并特别注意，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。

android传感器数据的线性化（Accelerometer）

1 个答案: