Detecting when someone begins walking using Core Motion and CMAccelerometer Data

Question

I'm trying to detect three actions: when a user begins walking, jogging, or running. I then want to know when the stop. I've been successful in detecting when someone is walking, jogging, or running with the following code:

- (void)update:(CMAccelerometerData *)accelData {

    [(id) self setAcceleration:accelData.acceleration];

    NSTimeInterval secondsSinceLastUpdate = -([self.lastUpdateTime timeIntervalSinceNow]);

    if (labs(_acceleration.x) >= 0.10000) {
        NSLog(@"walking: %f",_acceleration.x);
    }
    else if (labs(_acceleration.x) > 2.0) {
        NSLog(@"jogging: %f",_acceleration.x);
    }
    else if (labs(_acceleration.x) > 4.0) {
        NSLog(@"sprinting: %f",_acceleration.x);
    }

The problem I run into is two-fold:

1) update is called multiple times every time there's a motion, probably because it checks so frequently that when the user begins walking (i.e. _acceleration.x >= .1000) it is still >= .1000 when it calls update again.

Example Log:

    2014-02-22 12:14:20.728 myApp[5039:60b] walking: 1.029846
    2014-02-22 12:14:20.748 myApp[5039:60b] walking: 1.071777
    2014-02-22 12:14:20.768 myApp[5039:60b] walking: 1.067749

2) I'm having difficulty figuring out how to detect when the user stopped. Does anybody have advice on how to implement "Stop Detection"

Answer 1

According to your logs, accelerometerUpdateInterval is about 0.02. Updates could be less frequent if you change mentioned property of CMMotionManager.

Checking only x-acceleration isn't very accurate. I can put a device on a table in a such way (let's say on left edge) that x-acceleration will be equal to 1, or tilt it a bit. This will cause a program to be in walking mode (x > 0.1) instead of idle.

Here's a link to ADVANCED PEDOMETER FOR SMARTPHONE-BASED ACTIVITY TRACKING publication. They track changes in the direction of the vector of acceleration. This is the cosine of the angle between two consecutive acceleration vector readings.

Obviously, without any motion, angle between two vectors is close to zero and cos(0) = 1. During other activities d < 1. To filter out noise, they use a weighted moving average of the last 10 values of d.

After implementing this, your values will look like this (red - walking, blue - running):

Now you can set a threshold for each activity to separate them. Note that average step frequency is 2-4Hz. You should expect current value to be over the threshold at least few times in a second in order to identify the action.

Another helpful publications:

• ERSP: An Energy-efficient Real-time Smartphone Pedometer (analyze peaks and throughs)
• A Gyroscopic Data based Pedometer Algorithm (threshold detection of gyro readings)

UPDATE

_acceleration.x, _accelaration.y, _acceleration.z are coordinates of the same acceleration vector. You use each of these coordinates in d formula. In order to calculate d you also need to store acceleration vector of previous update (with i-1 index in formula).

WMA just take into account 10 last d values with different weights. Most recent d values have more weight, therefore, more impact on resulting value. You need to store 9 previous d values in order to calculate current one. You should compare WMA value to corresponding threshold.

Answer 2

if you are using iOS7 and iPhone5S, I suggest you look into CMMotionActivityManager which is available in iPhone5S because of the M7 chip. It is also available in a couple of other devices:

M7 chip

Here is a code snippet I put together to test when I was learning about it.

#import <CoreMotion/CoreMotion.h>

@property (nonatomic,strong) CMMotionActivityManager *motionActivityManager;

-(void) inSomeMethod
{
  self.motionActivityManager=[[CMMotionActivityManager alloc]init];

  //register for Coremotion notifications
  [self.motionActivityManager startActivityUpdatesToQueue:[NSOperationQueue mainQueue] withHandler:^(CMMotionActivity *activity) 
  {
    NSLog(@"Got a core motion update");
    NSLog(@"Current activity date is %f",activity.timestamp);
    NSLog(@"Current activity confidence from a scale of 0 to 2 - 2 being best- is: %ld",activity.confidence);
    NSLog(@"Current activity type is unknown: %i",activity.unknown);
    NSLog(@"Current activity type is stationary: %i",activity.stationary);
    NSLog(@"Current activity type is walking: %i",activity.walking);
    NSLog(@"Current activity type is running: %i",activity.running);
    NSLog(@"Current activity type is automotive: %i",activity.automotive);
  }];
}

I tested it and it seems to be pretty accurate. The only drawback is that it will not give you a confirmation as soon as you start an action (walking for example). Some black box algorithm waits to ensure that you are really walking or running. But then you know you have a confirmed action.

This beats messing around with the accelerometer. Apple took care of that detail!

Answer 3

You can use this simple library to detect if user is walking, running, on vehicle or not moving. Works on all iOS devices and no need M7 chip.

https://github.com/SocialObjects-Software/SOMotionDetector

In repo you can find demo project

Answer 4

I'm following this paper(PDF via RG) in my indoor navigation project to determine user dynamics(static, slow walking, fast walking) via merely accelerometer data in order to assist location determination.

Here is the algorithm proposed in the project:

And here is my implementation in Swift 2.0:

import CoreMotion
let motionManager = CMMotionManager()
motionManager.accelerometerUpdateInterval = 0.1
motionManager.startAccelerometerUpdatesToQueue(NSOperationQueue.mainQueue()) { (accelerometerData: CMAccelerometerData?, error: NSError?) -> Void in
        if((error) != nil) {
            print(error)
        } else {
            self.estimatePedestrianStatus((accelerometerData?.acceleration)!)
        }
}

After all of the classic Swifty iOS code to initiate CoreMotion, here is the method crunching the numbers and determining the state:

func estimatePedestrianStatus(acceleration: CMAcceleration) {
    // Obtain the Euclidian Norm of the accelerometer data
    accelerometerDataInEuclidianNorm = sqrt((acceleration.x.roundTo(roundingPrecision) * acceleration.x.roundTo(roundingPrecision)) + (acceleration.y.roundTo(roundingPrecision) * acceleration.y.roundTo(roundingPrecision)) + (acceleration.z.roundTo(roundingPrecision) * acceleration.z.roundTo(roundingPrecision)))

    // Significant figure setting
    accelerometerDataInEuclidianNorm = accelerometerDataInEuclidianNorm.roundTo(roundingPrecision)

    // record 10 values
    // meaning values in a second
    // accUpdateInterval(0.1s) * 10 = 1s
    while accelerometerDataCount < 1 {
        accelerometerDataCount += 0.1

        accelerometerDataInASecond.append(accelerometerDataInEuclidianNorm)
        totalAcceleration += accelerometerDataInEuclidianNorm

        break   // required since we want to obtain data every acc cycle
    }

    // when acc values recorded
    // interpret them
    if accelerometerDataCount >= 1 {
        accelerometerDataCount = 0  // reset for the next round

        // Calculating the variance of the Euclidian Norm of the accelerometer data
        let accelerationMean = (totalAcceleration / 10).roundTo(roundingPrecision)
        var total: Double = 0.0

        for data in accelerometerDataInASecond {
            total += ((data-accelerationMean) * (data-accelerationMean)).roundTo(roundingPrecision)
        }

        total = total.roundTo(roundingPrecision)

        let result = (total / 10).roundTo(roundingPrecision)
        print("Result: \(result)")

        if (result < staticThreshold) {
            pedestrianStatus = "Static"
        } else if ((staticThreshold < result) && (result <= slowWalkingThreshold)) {
            pedestrianStatus = "Slow Walking"
        } else if (slowWalkingThreshold < result) {
            pedestrianStatus = "Fast Walking"
        }

        print("Pedestrian Status: \(pedestrianStatus)\n---\n\n")

        // reset for the next round
        accelerometerDataInASecond = []
        totalAcceleration = 0.0
    }
}

Also I've used the following extension to simplify significant figure setting:

extension Double {
    func roundTo(precision: Int) -> Double {
        let divisor = pow(10.0, Double(precision))
        return round(self * divisor) / divisor
    }
}

With raw values from CoreMotion, the algorithm was haywire.

Hope this helps someone.

EDIT (4/3/16)

I forgot to provide my roundingPrecision value. I defined it as 3. It's just plain mathematics that that much significant value is decent enough. If you like you provide more.

Also one more thing to mention is that at the moment, this algorithm requires the iPhone to be in your hand while walking. See the picture below. Sorry this was the only one I could find.

My GitHub Repo hosting Pedestrian Status

Answer 5

You can use Apple's latest Machine Learning framework CoreML to find out user activity. First you need to collect labeled data and train the classifier. Then you can use this model in your app to classify user activity. You may follow this series if are interested in CoreML Activity Classification.

https://medium.com/@tyler.hutcherson/activity-classification-with-create-ml-coreml3-and-skafos-part-1-8f130b5701f6