This readme file was generated on 2022-10-27 by Huiseok Moon
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GENERAL INFORMATION
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Title of Dataset: Dataset for Gait Mode Recognition on Activities of Daily Living

Objective: The engineering application concerns the challenging task of recognizing locomotion activities of the wearer in real-time, which is needed to ensure appropriate control of the robot during daily living activities. This dataset was collected to predict future sequences of activities from a given sequence of daily living activities of a subjective wearing a lower limb exoskeleton by using a deep-learning-based algorithm. Hence, this dataset contains not kinematic features used for the advanced deep-learning-based algorithm to get high accuracy classifier but also raw data such as accelerometer, angular late, and quaternions of both foot.

Date of data collection: 2021-04-30

Institution: Laboratoire Images, Signaux et Systèmes Intelligents (LISSI)
Address: 120 rue Paul Armangot, 94400, Vitry sur Seine, France

Author/Principal Investigator: Ghazaleh Khodabandelou (ghazaleh.khodabandelou@u-pec.fr)
Author/Associate or Co-investigator: Huiseok Moon (huiseok.moon@univ-paris-est.fr)
Author/Alternate Contact Information: Samer Mohammed (samer.mohammed@u-pec.fr)

Links to publications: A Fuzzy Convolutional Attention-based GRU Network for Human Activity Recognition, Engineering Applications of Artificial Intelligence, 2022. (under review)



METHODOLOGICAL INFORMATION
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Software-specific information needed to interpret the data: These ".csv" format files were validated to interprete on the software "Excel 2016"

Experiment protocol: Ten healthy subjects (gender: 9 males, 1 female; age: 28±4 years; height: 1.74±0.05 m; weight: 66.8±10 kg) have participated in this study. Nine subject’s data are used for the learning process and the tenth subject data are used for the evaluation process. The experiments are conducted by each subject wearing the lower-limbs exoskeleton and performing five different gait modes: level walking (LW), ramp up (RU), ramp down (RD), stair up (SU), and stair down (SD). During the data acquisition, the exoskeleton is in a passive mode, the segment lengths of the robot (shank and thigh) are adjusted to fit the subject’s height and to avoid misalignment between human and robot joints. 

Experimental device: 
Lower-limbs exoskeleton: The weight of the whole exoskeleton is about 12 Kg. The main parts of the Angelegs are the exoskeletal frames, hardware control system, sensor system, and power unit. Ten degrees of freedom (DoFs) constitute the exoskeletal frames that are attached using straps to the wearer’s waist and legs. Each limb has three DoFs, one at the hip joint level, one at the knee joint level, and one at the ankle joint. The shank’s and thigh’s lengths are adjustable for different wearer’s heights.
IMUs: two IMUs (MTw Awinda, Xsens, Netherland) are attached on both (e.g., left and right) shoes.
data acquisition: The data are acquired from two IMUs using a 100 Hz sampling frequency on the evaluation software LABVIEW 2019 (National Instruments, US).



DATA & FILE OVERVIEW
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File List:  
*The subject dataset folder contains ten subjects' data sheet (subjectA.csv ~ subjectJ.csv) 
*The dataset statistical plot is included (dataset_statistics.png)

Number of variables: 26

Variable List:
index: time index number, a difference (e.g., 1) means 0.01 seconds since sampling frequency is 100Hz. 
x_acc_left: x-axis accelerometer data of the IMU on left foot.
y_acc_left: y-axis accelerometer data of the IMU on left foot.
z_acc_left: z-axis accelerometer data of the IMU on left foot.
x_acc_right: x-axis accelerometer data of the IMU on right foot.
y_acc_right: y-axis accelerometer data of the IMU on right foot.
z_acc_right: z-axis accelerometer data of the IMU on right foot.
quat_1_left: 1st quaternion element data of IMU on left foot.
quat_2_left: 2nd quaternion element data of IMU on left foot.
quat_3_left: 3rd quaternion element data of IMU on left foot.
quat_4_left: 4th quaternion element data of IMU on left foot.
quat_1_right: 1st quaternion element data of IMU on right foot.
quat_2_right: 2nd quaternion element data of IMU on right foot.
quat_3_right: 3rd quaternion element data of IMU on right foot.
quat_4_right: 4th quaternion element data of IMU on right foot.
x_gyro_left: x-axis gyroscope data of the IMU on left foot.
y_gyro_left: y-axis gyroscope data of the IMU on left foot.
z_gyro_left: z-axis gyroscope data of the IMU on left foot.
x_gyro_right: x-axis gyroscope data of the IMU on right foot.
y_gyro_right: y-axis gyroscope data of the IMU on right foot.
z_gyro_right: z-axis gyroscope data of the IMU on right foot.
feature1: Absolute horizontal position feature
feature2: Vertical position feature
feature3: Absolute horizontal velocity feature
feature4: Vertical velocity feature
label: gait mode label, 1~5 denote level walking, stair ascent, stair descent, ramp ascent, ramp descent, respectively.


Last updated: 2022-10-27