Human locomotion dataset

The zip file contains 128 npy files. Each npy file is a python numpy array file that contains one dictionary with the following keys: ['info', 'emg_r', 'kinematic_r', 'kinematic_l', 'emg_l'].

info: Is a pandas dataframe with the following columns.

file_name ⟶ refers to subject ID

fs_emg (Hz) ⟶ refers to sample frequency of EMG data

fs_kin (Hz) ⟶ refers to sample frequency of kinematic data

mass (kg) ⟶ subject mass in kg

trailing_leg ⟶ leg used as trailing leg during unilateral skipping. right (r) or left (l)

vel (km/h) ⟶ speed on km/h

emg_r: List of n elements. n is equivalent to the number of strides registered. Each element of the list present a DataFrame containig 14 EMG signals of right and left legs corresponding to a one stride segmented using the heel strike of the right leg.

emg_l: List of n elements. n is equivalent to the number of strides registered. Each element of the list present a DataFrame containig 14 EMG signals of right and left legs corresponding to a one stride segmented using the heel strike of the left leg.

kinematic_r: List of n elements. n is equivalent to the number of strides registered. Each element of the list present a DataFrame containig x, y and z coordinates of 18 reflective markers used in a MOCAP system during one stride identified using the heel strike of the right leg.

kinematic_l: List of n elements. n is equivalent to the number of strides registered. Each element of the list present a DataFrame containig x, y and z coordinates of 18 reflective markers used in a MOCAP system during one stride identified using the heel strike of the left leg.

#example of use
data = np.load('IT_R_110.npy',allow_pickle=True,encoding='latin1').item() #load a file
emg5r = data['emg_r'][5] #Load all emg data of the 5th stride defined from right leg heel strike
kinematic5l = data['kinematic_l'][5] #Load all kinematic data of the 5th stride defined from right leg heel strike