ALAMEDA Data: Bridging the Early Diagnosis and Treatment Gaps of Brain Diseases (Parkinson's Disease, Multiple Slerosis and Stroke)

ALAMEDA is an Horizon 2020 Research and Innovation project that aims to bridge the early diagnosis and treatment gap of brain diseases via smart, connected, proactive and evidence-based technological interventions. Its vision is to research and prototype new generation Artificial Intelligence (AI) systems to support brain disorders patients' healthcare, focusing on Parkinson's Disease (PD), Multiple Sclerosis (MS) and Stroke. To this end, three (one for each disease) small scale validation pilots were performed in real world settings. Throughout these pilots, various types of data, such as accelerometer, gyroscopic, heart rate, etc., were collected via smart wearable sensors from the patients enrolled. The smart devices that were employed include: a Fitbit smartwatch, a GENEActiv smart bracelet, Novel Loadsol insole sensors and a prototype smart belt with triaxial accelerometers and gyroscopes embedded. Moreover, the patients underwent several clinical assessments and filled in numerous both disease-specific and non-disease-specific questionnaires. In this record, both raw and processed sensory data are combined with both clinical and patient reported outcomes (PROs) to form different disease-specific datasets. More specifically: For Parkinson's disease: Three datasets are provided (one for tremor detection, one for dyskinesia detection, and one for Hoehn & Yahr score estimation) alongside the vertical ground reaction force recordings. For Multiple Sclerosis: Two datasets are provided (one for Expanded Disability Status Scale (EDSS) scores classification and one that accumulates clinical data and individual scores from various MS-related questionnaires) alongside the vertical ground reaction force and the smart belt recordings. For Stroke: Two datasets are provided (one for rehabilitation exercises' recognition and one for walking classification, both with and without manual annotations) alongside the smart belt recordings. More information about the datasets provided can be found in the respective READ ME files that are included in the current record.

ALAMEDA是一项欧盟地平线2020（Horizon 2020）研究与创新项目，旨在通过智能化、互联化、主动式且基于证据的技术干预手段，缩小脑部疾病的早期诊断与治疗差距。其愿景是研发并原型化新一代人工智能（Artificial Intelligence, AI）系统，以支持脑部疾病患者的医疗照护，聚焦帕金森病（Parkinson's Disease, PD）、多发性硬化症（Multiple Sclerosis, MS）与脑卒中。
为此，针对上述三种疾病各开展了一项小规模验证试点，均在真实世界场景中实施。试点期间，研究人员通过智能可穿戴传感器为入组患者采集了多类数据，涵盖加速度计、陀螺仪、心率等类型。所采用的智能设备包括：Fitbit智能手表、GENEActiv智能手环、Novel Loadsol鞋垫传感器，以及一款内置三轴加速度计与陀螺仪的原型智能腰带。
此外，入组患者还接受了多项临床评估，并填写了大量疾病特异性与非疾病特异性问卷。本数据集将原始与处理后的传感数据，与临床评估结果及患者报告结局（Patient Reported Outcomes, PROs）相结合，构建出不同的疾病特异性数据集。
具体分类如下：
针对帕金森病：共提供3组数据集（分别用于震颤检测、异动症检测，以及Hoehn & Yahr量表评分预测），并附带垂直地面反作用力记录数据。
针对多发性硬化症：共提供2组数据集（分别用于扩展残疾状态量表（Expanded Disability Status Scale, EDSS）评分分类，以及整合临床数据与各类多发性硬化相关问卷的个体评分），并附带垂直地面反作用力与智能腰带记录数据。
针对脑卒中：共提供2组数据集（分别用于康复训练动作识别与行走状态分类，均包含手动标注与未标注版本），并附带智能腰带记录数据。
有关本次提供的数据集的更多详细信息，可查阅本数据集包中附带的对应README文件。

搜集汇总

背景与挑战

背景概述

ALAMEDA数据集是欧盟Horizon 2020项目的一部分，专注于帕金森病、多发性硬化症和卒中三种脑部疾病的早期诊断与治疗研究。该数据集整合了来自真实世界验证试验的智能穿戴传感器数据（如加速度计、心率等）、临床评估和患者报告结果，针对每种疾病提供了特定子集，例如帕金森病的震颤和运动障碍检测数据，多发性硬化症的EDSS评分分类数据，以及卒中的康复练习识别数据。数据集旨在支持人工智能系统开发，以改善脑部疾病患者的医疗保健。