Full-body mobility data to validate inertial measurement unit algorithms in healthy and neurological cohorts

Gait and balance dysfunctions are common in neurological disorders and have a negative effect on the quality of life. Regularly quantifying these mobility limitations can be used to measure the disease progression and the effect of treatment. This information can be used to provide a more individualised treatment. 

Inertial measurement units (IMUs) can be utilized to quantify mobility in different contexts. However, algorithms are required to extract valuable parameters out of the raw IMU data. These algorithms need to be validated to make sure that they extract the features they should extract. This validation should be done per disease, since different mobility limitations or symptoms can influence the performance of an algorithm in different ways. Therefore, this dataset contains data from both healthy participants and patients with neurological diseases (Parkinson disease, stroke, multiple sclerosis, chronic low back pain). The full-body of 167 participants were measured with IMUs and an optical motion capture (reference) system. Participants performed multiple standardized mobility assessments and non-standardized activities of daily living.

步态与平衡功能障碍在神经系统疾病中极为常见，且会严重损害患者的生活质量。定期量化这类运动功能受限状况，可用于评估疾病进展与治疗效果，进而为患者提供更具个性化的治疗方案。

惯性测量单元（Inertial Measurement Unit, IMU）可用于不同场景下的运动功能量化，但需借助算法从原始IMU数据中提取有效参数。为确保算法能够准确提取预设特征，需对其开展验证工作。由于不同的运动功能受限类型或临床症状会以不同方式影响算法性能，因此验证需针对特定疾病进行。本数据集涵盖167名健康受试者与神经系统疾病患者（帕金森病患者、脑卒中患者、多发性硬化症患者、慢性下腰痛患者）的相关数据。研究人员采用惯性测量单元与光学运动捕捉参考系统，对所有受试者的全身运动状态进行了采集，受试者完成了多项标准化运动功能评估任务与非标准化日常活动。