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

gpequera@cup.edu.uy