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

DATASET 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 A numerical value that uniquely identifies the subject. 2. subject_sessions: list of numpy.array 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 A list of datetime objects that denote the capturing date and time of the corresponding entries in the subject_sessions field. 4. annotation: dict A dictionary containing the following tremor-related annotation values: * updrs16: scalar int 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] 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] Same as above but for left hand. * updrs21_right: scalar int in range [0, 4] 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] Same as above but for left hand. * sp_expert: scalar int in range [0, 1] 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. A '1' value indicates that the subject has tremor. A '0' value indicates that the subject doesn't have tremor. * pd_status: scalar int in range [0, 1] A '1' value indicates that the subject is a PD patient. 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. ETHICS & FUNDING 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). CORRESPONDANCE Any inquiries regarding this dataset should be adressed to: Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate) Multimedia Understanding Groupmug Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: [phone], 996365 Fax: [phone] E-mail: [e-mail] LICENSE This is an open access dataset, licensed under Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/). WARRANTY 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.

### 数据集概述
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野外采集场景（in-the-wild）采集的惯性测量单元（Inertial Measurement Unit, IMU）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名健康对照者使用个人智能手机贡献了加速度数据，数据采集周期跨越数月。每当受试者拨打电话时，系统会自动记录三轴加速度数值，单次录制最长持续75秒。每通电话对应一段加速度记录，亦称为会话（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（受试者可随时退出研究）。关于采集流程与分析结果的详细说明，请参阅相关研究论文。
本数据以Python字典列表的形式存储于pickle序列化文件中。列表中的每个字典对应一名受试者，包含以下字段：
1. `subject_id`：标量数值，用于唯一标识受试者。
2. `subject_sessions`：numpy数组列表，每个数组的形状为(N, 4)，存储受试者贡献的三轴加速度会话。其中N为单条会话的采样总长度（不同会话的N值存在差异）；数组第0列为加速度采样的时间戳，第1至3列分别对应x、y、z三个方向的加速度数值。
3. `session_datetimes`：datetime对象列表，用于标记`subject_sessions`字段中对应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项：
* `updrs16`：整数标量，对应受试者报告的运动障碍协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`：取值范围为[0, 4]的整数标量，对应接诊神经科医师报告的MDS-UPDRS量表第三部分第20项中右手静止性震颤评分。
* `updrs20_left`：取值范围为[0, 4]的整数标量，同上述字段，但对应左手静止性震颤。
* `updrs21_right`：取值范围为[0, 4]的整数标量，对应接诊神经科医师报告的MDS-UPDRS量表第三部分第21项中右手动作性/姿势性震颤评分。
* `updrs21_left`：取值范围为[0, 4]的整数标量，同上述字段，但对应左手动作性/姿势性震颤。
* `sp_expert`：取值范围为[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检视受试者提交的信号，并结合每位受试者的UPDRS评分得到的二分类震颤标注。考虑到震颤的间歇性特征与野外采集流程的诸多限制，该标注为必要补充。其中1代表受试者存在震颤，0代表受试者无震颤。更多细节请参阅相关研究论文中的数据集说明。
* `pd_status`：取值范围为[0, 1]的整数标量，1代表受试者为帕金森病患者，0代表受试者为健康对照。
> 注：所有标注值均针对受试者整体，而非单条会话。

## 伦理与资助
本数据集的采集研究为多中心研究，已在所有参与国家获得伦理批准（详细信息请访问：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，包括授权第三方访问其伪匿名化数据。
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支持性干预措施（i-prognosis.eu）。

## 联系方式
有关本数据集的任何疑问，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，塞萨洛尼基，希腊，GR54124
电话：[phone], 996365
传真：[phone]
邮箱：[e-mail]

## 许可协议
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/）授权。

## 免责声明
本数据集按“现状”提供，无任何形式的担保。数据集管理者不对因使用本数据集导致的任何损害（包括人身、财产或其他形式的损害）承担责任。

搜集汇总

背景与挑战

背景概述

该数据集包含通过智能手机加速度计传感器采集的帕金森病患者和健康对照者的IMU数据，用于检测与帕金森病相关的震颤发作。数据集包含31名帕金森病患者和14名健康对照者的数据，以Python字典列表形式存储，包含三轴加速度值、时间戳和震颤相关注释。