MotionSense Dataset ( time-series data generated by smartphone's sensors: accelerometer and gyroscope)
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README.md

MotionSense Dataset

This dataset includes time-series data generated by accelerometer and gyroscope sensors (attitude, gravity, userAcceleration, and rotationRate). It is collected with an iPhone 6s kept in the participant's front pocket using SensingKit which collects information from Core Motion framework on iOS devices. A total of 24 participants in a range of gender, age, weight, and height performed 6 activities in 15 trials in the same environment and conditions: downstairs, upstairs, walking, jogging, sitting, and standing. With this dataset, we aim to look for personal attributes fingerprints in time-series of sensor data, i.e. attribute-specific patterns that can be used to infer gender or personality of the data subjects in addition to their activities.

Time-series correspond to Walking activity of data subject(code 3). There are 12-features.

Download

The MotionSense dataset is publicly available in the current repository and also in the Queen Mary University of London's repository as a backup.

Citation

If you use this dataset, please cite the following paper:

Dataset Description

Scenario

For each participant, the study had been commenced by collecting their demographic (age and gender) and physically-related (height and weight) information. Then, we provided them with a dedicated smartphone (iPhone 6) and asked them to store it in their trousers' front pocket during the experiment. All the participant were asked to wear flat shoes. We then asked them to perform 6 different activities (walk downstairs, walk upstairs, sit, stand and jogging) around the Queen Mary University of London's Mile End campus. For each trial, the researcher set up the phone and gave it to the current participants, then the researcher stood in a corner. Then, the participant pressed the start button of Crowdsense app and put it in their trousers' front pocket and performed the specified activity. We asked them to do it as natural as possible, like their everyday life. At the end of each trial, they took the phone out of their pocket and pressed the stop button. The exact places and routes for running all the activities are shown in the illustrative map in the following Figure.

As we can see, there are 15 trials:

  1. Long trials: those with number 1 to 9 with around 2 to 3 minutes duration.
  2. Short trials: those with number 11 to 16 that are around 30 seconds to 1 minutes duration.

Data Subjects

There are 24 data subjects. Here we summarized their information:

Code Weight (kg) Height (cm) Age (years) Gender (F:0,M:1)
1 102 188 46 1
2 72 180 28 1
3 48 161 28 0
4 90 176 31 1
5 48 164 23 0
6 76 180 28 1
7 62 175 30 0
8 52 161 24 0
9 93 190 32 1
10 72 164 31 0
11 70 178 24 1
12 60 167 33 1
13 60 178 33 1
14 70 180 35 1
15 70 185 33 1
16 96 172 29 0
17 76 180 26 1
18 54 164 26 0
19 78 164 28 0
20 88 180 25 1
21 52 165 24 1
22 100 186 31 1
23 68 170 25 0
24 74 173 18 0

Folders (and Features)

There three different folders. Usually, you just need the folder (A) (DeviceMotion), because this folder includes everything that can be captured by both Accelerometer and Gyroscope. However, we also have data captured by these two sensors separately in the folder (B) and (C).

(A) DeviceMotion_data

This folder contains time-series collected by both Accelerometer and Gyroscope for all 15 trials. For every trial we have a multivariate time-series, like this:

index attitude.roll attitude.pitch attitude.yaw gravity.x gravity.y gravity.z rotationRate.x rotationRate.y rotationRate.z userAcceleration.x userAcceleration.y userAcceleration.z
0 -2.544349 -1.250641 2.175416 -0.176977 0.949187 0.260222 -7.204869 2.267762 0.103529 -0.060221 1.576174 -0.091292
1 -2.524075 -1.187355 2.047589 -0.21661 0.927383 0.305012 -2.554745 6.548334 -0.005139 0.134136 0.860307 -2.152149
2 -2.534324 -1.141923 1.990077 -0.237286 0.909435 0.341488 -2.38587 0.112576 -0.576825 0.427914 0.442891 -0.892025
3 -2.564504 -1.098202 1.960054 -0.248344 0.89039 0.381471 -2.098059 0.199309 -0.671066 0.619987 0.007925 -0.946626
... ... ... ... ... ... ... ... ... ... ... ... ...

Thus, we have time-series with 12 features:

  1. attitude.roll
  2. attitude.pitch
  3. attitude.yaw
  4. gravity.x
  5. gravity.y
  6. gravity.z
  7. rotationRate.x
  8. rotationRate.y
  9. rotationRate.z
  10. userAcceleration.x
  11. userAcceleration.y
  12. userAcceleration.z

For more information, please read this page: CMDeviceMotion

The accelerometer measures the sum of two acceleration vectors: gravity and user acceleration. User acceleration is the acceleration that the user imparts to the device. Because Core Motion is able to track a device’s attitude using both the gyroscope and the accelerometer, it can differentiate between gravity and user acceleration. A CMDeviceMotion object provides both measurements in the gravity and userAcceleration properties.

(B) Accelerometer_data

Here we just have data reported by Accelerometer sensor. Thus, there are just three features correspond to 3 different axes:

  1. x
  2. y
  3. z

(C) Gyroscope_data

Here we just have data reported by Gyroscope sensor. Thus, there are again just three features correspond to 3 different axes:

  1. x
  2. y
  3. z

Labels

There are 6 different labels:

  1. dws: downstairs
  2. ups: upstairs
  3. sit: sitting
  4. std: standing
  5. wlk: walking
  6. jog: jogging

A Code to Build a Labeled Time-Series from data into a Pandas DataFrame

import numpy as np
import pandas as pd

def get_ds_infos():
    """
    Read the file includes data subject information.
    
    Data Columns:
    0: code [1-24]
    1: weight [kg]
    2: height [cm]
    3: age [years]
    4: gender [0:Female, 1:Male]
    
    Returns:
        A pandas DataFrame that contains inforamtion about data subjects' attributes 
    """ 

    dss = pd.read_csv("data_subjects_info.csv")
    print("[INFO] -- Data subjects' information is imported.")
    
    return dss

def set_data_types(data_types=["userAcceleration"]):
    """
    Select the sensors and the mode to shape the final dataset.
    
    Args:
        data_types: A list of sensor data type from this list: [attitude, gravity, rotationRate, userAcceleration] 

    Returns:
        It returns a list of columns to use for creating time-series from files.
    """
    dt_list = []
    for t in data_types:
        if t != "attitude":
            dt_list.append([t+".x",t+".y",t+".z"])
        else:
            dt_list.append([t+".roll", t+".pitch", t+".yaw"])

    return dt_list


def creat_time_series(dt_list, act_labels, trial_codes, mode="mag", labeled=True):
    """
    Args:
        dt_list: A list of columns that shows the type of data we want.
        act_labels: list of activites
        trial_codes: list of trials
        mode: It can be "raw" which means you want raw data
        for every dimention of each data type,
        [attitude(roll, pitch, yaw); gravity(x, y, z); rotationRate(x, y, z); userAcceleration(x,y,z)].
        or it can be "mag" which means you only want the magnitude for each data type: (x^2+y^2+z^2)^(1/2)
        labeled: True, if we want a labeld dataset. False, if we only want sensor values.

    Returns:
        It returns a time-series of sensor data.
    
    """
    num_data_cols = len(dt_list) if mode == "mag" else len(dt_list*3)

    if labeled:
        dataset = np.zeros((0,num_data_cols+7)) # "7" --> [act, code, weight, height, age, gender, trial] 
    else:
        dataset = np.zeros((0,num_data_cols))
        
    ds_list = get_ds_infos()
    
    print("[INFO] -- Creating Time-Series")
    for sub_id in ds_list["code"]:
        for act_id, act in enumerate(act_labels):
            for trial in trial_codes[act_id]:
                fname = 'A_DeviceMotion_data/'+act+'_'+str(trial)+'/sub_'+str(int(sub_id))+'.csv'
                raw_data = pd.read_csv(fname)
                raw_data = raw_data.drop(['Unnamed: 0'], axis=1)
                vals = np.zeros((len(raw_data), num_data_cols))
                for x_id, axes in enumerate(dt_list):
                    if mode == "mag":
                        vals[:,x_id] = (raw_data[axes]**2).sum(axis=1)**0.5        
                    else:
                        vals[:,x_id*3:(x_id+1)*3] = raw_data[axes].values
                    vals = vals[:,:num_data_cols]
                if labeled:
                    lbls = np.array([[act_id,
                            sub_id-1,
                            ds_list["weight"][sub_id-1],
                            ds_list["height"][sub_id-1],
                            ds_list["age"][sub_id-1],
                            ds_list["gender"][sub_id-1],
                            trial          
                           ]]*len(raw_data))
                    vals = np.concatenate((vals, lbls), axis=1)
                dataset = np.append(dataset,vals, axis=0)
    cols = []
    for axes in dt_list:
        if mode == "raw":
            cols += axes
        else:
            cols += [str(axes[0][:-2])]
            
    if labeled:
        cols += ["act", "id", "weight", "height", "age", "gender", "trial"]
    
    dataset = pd.DataFrame(data=dataset, columns=cols)
    return dataset
#________________________________


ACT_LABELS = ["dws","ups", "wlk", "jog", "std", "sit"]
TRIAL_CODES = {
    ACT_LABELS[0]:[1,2,11],
    ACT_LABELS[1]:[3,4,12],
    ACT_LABELS[2]:[7,8,15],
    ACT_LABELS[3]:[9,16],
    ACT_LABELS[4]:[6,14],
    ACT_LABELS[5]:[5,13]
}

## Here we set parameter to build labeld time-series from dataset of "(A)DeviceMotion_data"
## attitude(roll, pitch, yaw); gravity(x, y, z); rotationRate(x, y, z); userAcceleration(x,y,z)
sdt = ["userAcceleration"]
print("[INFO] -- Selected sensor data types: "+str(sdt))    
act_labels = ACT_LABELS [0:4]
print("[INFO] -- Selected activites: "+str(act_labels))    
trial_codes = [TRIAL_CODES[act] for act in act_labels]
dt_list = set_data_types(sdt)
dataset = creat_time_series(dt_list, act_labels, trial_codes, mode="mag", labeled=True)
print("[INFO] -- Shape of time-Series dataset:"+str(dataset.shape))    
dataset.head()

MotionSense dataset have been used in the following papers: