DOI: 10.5281/zenodo.7273759
title: Labelled and unlabelled hand acceleration data captured unobtrusively from PD patients and Healthy Controls

description: The dataset contains acceleration signals captured in-the-wild via the IMU sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). It contains two different groups of subjects: tremor_sdata.pickle --&gt; A group of 45 subjects that have been subjected to neurological examination (the same dataset as https://zenodo.org/record/3519213) tremor_gdata.pickle --&gt; A group of 454 subjects who just self-reported their PD status All subjects contributed accelerometer data using their personal smartphones, for a period spanning many months. Tri-axial acceleration values were recorded automatically whenever a phone call was realized. The recording lasted for 75 seconds at the most. Each phone call thus resulted in one recorded accelerometer signal, also referred to as session. Each subject contributed a different amount of sessions depending on the number of phone calls they realized during the data collection period as well as their participation time (they were free to drop-out at any time). A detailed description of the capturing process as well as analysis results, can be found in the related research article. The data is presented as a python dictionary, indexed by the subject ids. Each element of the dictionary is a list with the following significance: Index Meaning 0 List of np.arrays, Each array contains the power spectral density for an acceleration segment of 5s duration 1 Dictionary, Denotes subject updrs 2 List of str containing a unique identifier of the acceleration session that each segment in the other lists belongs to 3 List of np.arrays, Each array contains the pre-processed acceleration values for a 5s segment The subject updrs is represented as dictionary containing the following tremor-related annotation values (FOR THE FIRST GROUP ONLY):<br> * updrs16: scalar int<br> The value related to tremor as described in item 16 of the part II of the MDS-UPDRS scale, as reported by the subject. * updrs20_right: scalar int in range [0, 4]<br> The value related to rest tremor in the right hand as described in item 20 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist. * updrs20_left: scalar int in range [0, 4]<br> Same as above but for left hand. * updrs21_right: scalar int in range [0, 4]<br> The value related to action/postural tremor in the right hand as described in item 21 of the part III of the MDS-UPDRS scale, as reported by the attending neurologist. * updrs21_left: scalar int in range [0, 4]<br> Same as above but for left hand. * sp_expert: scalar int in range [0, 1]<br> A binary tremor annotation created by a group of signal processing experts, upon visually examining the contributed signals in both time and frequency domain and taking into consideration the UDPRS scores of each subject. This was necessary due to the intermittent nature of tremor, as well as a number of considerations related to the in-the-wild nature of the data capturing process. For more details, we refer the reader to the dataset description in the related research article.<br> A '1' value indicates that the subject has tremor.<br> A '0' value indicates that the subject doesn't have tremor. * pd_status: scalar int in range [0, 1]<br> A '1' value indicates that the subject is a PD patient.<br> A '0' value indicates that the subject is a Healthy Control Note: Each annotation value refers to the subject as a whole, and not in any one session.
description: {"references": ["A. Papadopoulos, K. Kyritsis, L. Klingelhoefer, S. Bostanjopoulou, K. R. Chaudhuri and A. Delopoulos, \"Detecting Parkinsonian Tremor From IMU Data Collected in-the-Wild Using Deep Multiple-Instance Learning,\" in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 9, pp. 2559-2569, Sept. 2020, doi: 10.1109/JBHI.2019.2961748.", "Papadopoulos, A., Iakovakis, D., Klingelhoefer, L. et al. Unobtrusive detection of Parkinson's disease from multi-modal and in-the-wild sensor data using deep learning techniques. Sci Rep 10, 21370 (2020). https://doi.org/10.1038/s41598-020-78418-8", "https://zenodo.org/record/3519213"]}