IMU data captured unobtrusively and in-the-wild by Parkinson's disease patients and healthy controls

<strong>DATASET</strong> The dataset contains IMU signals captured in-the-wild via the accelerometer sensor embedded in modern smartphones, for the purpose of detecting tremorous episodes, related to Parkinson's Disease (PD). A group of 31 PD patients and 14 Healthy controls contributed accelerometer data using their personal smartphones, for a period spanning many months.Tri-axial acceleration values were recorded automatically whenevera 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 list of python dictionaries, stored in a pickle file. Each dictionary in the list, corresponds to one subject and containes the following fields: 1. subject_id: scalar<br> A numerical value that uniquely identifies the subject. 2. subject_sessions: list of numpy.array<br> A list of numpy arrays of shape (N, 4) that contains the tri-axial accelerometer sessions that the subject contributed. N denotes the total length of the session in samples (which varies from session to session) Column 0 of the array contains the timestamps of the accelerometer samples. Columns 1-3 contain the acceleration values across the x,y,z directions. 3. session_datetimes: list of datetime objects <br> A list of datetime objects that denote the capturing date and time of the corresponding entries in the subject_sessions field. 4. annotation: dict<br> A dictionary containing the following tremor-related annotation values:<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.<br> <strong>ETHICS &amp; FUNDING</strong> The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: http://www.i-prognosis.eu/?page_id=3606). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (i-prognosis.eu). <strong>CORRESPONDANCE</strong> Any inquiries regarding this dataset should be adressed to: Mr. Alexandros Papadopoulos (Electrical &amp; Computer Engineer, PhD candidate) Multimedia Understanding Groupmug<br> Department of Electrical &amp; Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124 Tel: [phone], 996365 <br> Fax: [phone]<br> E-mail: [e-mail] <strong>LICENSE</strong> This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/). <strong>WARRANTY</strong> This dataset comes without any warranty. Administrators of this dataset can not be held accountable for any damage (physical, financial or otherwise) caused by the use of this dataset.<strong>数据集</strong> 本数据集包含通过现代智能手机内置加速度传感器在自然真实场景下采集的加速度信号，用于检测与帕金森病（Parkinson's Disease, PD）相关的震颤发作。 共有31名帕金森病患者与14名健康对照受试者使用个人智能手机贡献加速度数据，采集周期长达数月。每当受试者拨打电话或接听电话时，系统会自动记录三轴加速度数值，单次录制最长持续75秒。每一通电话对应一条录制的加速度信号，亦称为一次会话（session）。每位受试者贡献的会话数量取决于其在数据采集期间的通话次数与参与时长（受试者可随时自愿退出研究）。有关采集流程与分析结果的详细说明，请参阅相关研究论文。 本数据集以Python字典列表的形式存储于pickle文件（pickle）中。列表中的每个字典对应一位受试者，包含以下字段： 1. subject_id：标量 用于唯一标识受试者的数值。 2. subject_sessions：numpy数组（numpy array）列表 由若干形状为(N, 4)的numpy数组组成的列表，存储受试者贡献的三轴加速度会话数据。其中N为单条会话的采样总点数（各会话点数各不相同）。数组第0列包含加速度采样的时间戳，第1至3列分别为x、y、z三个方向的加速度数值。 3. session_datetimes：datetime对象（datetime object）列表 由datetime对象组成的列表，对应subject_sessions字段中各条会话的采集日期与时间。 4. annotation：字典 包含以下与震颤相关的标注值： * updrs16：整数标量 对应受试者报告的运动障碍学会统一帕金森病评定量表（Movement Disorder Society-Unified Parkinson's Disease Rating Scale, MDS-UPDRS）第二部分第16项中与震颤相关的评分。 * updrs20_right：范围为[0, 4]的整数标量 对应主治医师评定的MDS-UPDRS第三部分第20项中，受试者右手静息震颤相关评分。 * updrs20_left：范围为[0, 4]的整数标量 同上，对应左手静息震颤评分。 * updrs21_right：范围为[0, 4]的整数标量 对应主治医师评定的MDS-UPDRS第三部分第21项中，受试者右手动作/姿势性震颤相关评分。 * updrs21_left：范围为[0, 4]的整数标量 同上，对应左手动作/姿势性震颤评分。 * sp_expert：范围为[0, 1]的整数标量 由一组信号处理专家通过时域与频域视觉审查受试者提交的信号，并结合各受试者的UPDRS评分得到的二元震颤标注。由于震颤具有间歇性，且数据采集过程为自然真实场景下进行，需结合多方面考量，因此采用该标注方式。有关详细细节，请参阅相关研究论文中的数据集说明。 标注值为1表示受试者存在震颤，为0表示受试者无震颤。 * pd_status：范围为[0, 1]的整数标量 标注值为1表示受试者为帕金森病患者，为0表示受试者为健康对照。 注意：所有标注值均针对受试者整体，而非某单一会话。 <strong>伦理与资助</strong> 本数据集采集相关研究为多中心研究，已在所有参与国家获得伦理批准（更多信息请访问：http://www.i-prognosis.eu/?page_id=3606）。所有受试者在参与研究前均已签署知情同意书，包括允许第三方访问其伪匿名化数据的许可。本研究获得欧盟地平线2020研究与创新计划资助，资助协议编号为690494 - i-PROGNOSIS：智能帕金森病早期检测指导新型辅助干预措施（i-prognosis.eu）。 <strong>联系方式</strong> 有关本数据集的任何问询，请联系： 亚历山德罗斯·帕帕佐普洛斯先生（电气与计算机工程师，博士候选人） 多媒体理解小组（Multimedia Understanding Group, MUG） 电气与计算机工程系 亚里士多德大学塞萨洛尼基分校 大学校园C楼3层 希腊塞萨洛尼基，GR54124 电话：[phone]、996365 传真：[phone] 电子邮箱：[e-mail] <strong>许可证</strong> 本数据集为开放获取数据集，采用知识共享署名4.0国际许可协议（https://creativecommons.org/licenses/by/4.0/）进行授权。 <strong>免责声明</strong> 本数据集按“现状”提供，不附带任何形式的担保。数据集管理员不对因使用本数据集而导致的任何损害（包括人身、财产或其他形式的损害）承担责任。