Driving behavior plays a vital role in maintaining safe and sustainable transport, and specifically, in the area of traffic management and control, driving behavior is of great importance since specific driving behaviors are significantly related with traffic congestion levels. Beyond that, it affects fuel consumption, air pollution, public health as well as personal mental health and psychology. Use of Smartphone sensors for data acquisition has emerged as a means to understand and model driving behavior. Our aim is to analyze driving behavior using on Smartphone sensors’ data streams.

Instructions: 

The datasets folder include .csv files of sensor data like Accelerometer, Gyroscope, etc. This data was recorded in live traffic while driver was executing certain driving events. The travel time for each one way trip was approximately 5kms - 20kms. The smartphone position was fixed horizontally in the vehicles utility box. Vehicle type used for data recording was LMV.

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Nurse Care Activity Recognition

Instructions: 

This dataset consists of two folders: training and testing

The training folder contains data collected in the lab and data from two users collected in the nursing home. They are separated in folders and there is one labels file for each.

 

The testing folder contains data for only the nursing home from the same users as the training folder but different days.

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This dataset consists of sensory data of digits, i.e., from 0 to 9. The dataset is collected from 20 volunteers by using a 9−axis Inertial Measurement Unit (IMU) equipped marker pen. The objective of this dataset is to design classification algorithms for recognizing a handwritten digit in real-time.

Instructions: 

The dataset contains Multivariate Time Series (MTS) data of digits, which is collected from a sensors-equipped marker pen. A 9-Axis IMU sensor, attached to the marker pen, is used to record the hand movements of the writer while writing a digit on a wall-mounted whiteboard. The IMU consists of three sensors:  3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer. These sensors are connected to a NodeMCU which transfers the data to an MQTT server over Wi-Fi. The sensory data is collected from 20 volunteers at the sampling rate  75 Hz. A writing activity is designed which consists of 5 rows where each row has 10 digits in ascending order  [i.e., 0, 1, · · ·, 9]. Therefore, a single writing activity provides an MTS of 50 digits (i.e., each digit 5 times), which is later segmented to have a separate MTS for each of 50 digits. As each volunteer performs the writing activity 10  times, total 50x10x20 (=10000) labeled instances are created in the dataset.

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The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.

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The data format is described as follows:

Event: {‘acc’: array([[x_axis], [y_axis], [z_axis], ‘gyr’,array([x_axis], [y_axis], [z_axis], ‘label’: No ]

No =1 means acceleration.

No =2 means normal driving.

No =3 means collision.

No =4 means left turn.

No =5 means right turn.

 

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The electronic system has been design to know the position human body. Of this way the system use a three axis accelerometer to detect five common positions (i) ventral decubitus, (ii) right lateral decubitus, (iii) left lateral decubitus, (iv) supine decubitus and (v) seated.  The sensor data was acquire with ten diferrents persons, their each positions was  how they felt confortable. The accelerometer acquire data from  3 axis possible (X,Y,Z)

Instructions: 

The unbalanced data set is for classification tasks.  The sensor has been put in the chest  and  Y axis pointing the chin. The labels corresponding the human position as follows.

Label       Position

1            supine decubitus    

2             left lateral decubitus

3             right lateral decubitus

4             seated

5              ventral decubitus

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