OBF-Psychiatric, a motor activity dataset of patients diagnosed with major depression, schizophrenia, and ADHD

Mental health is vital to human well-being and its prevention strategies have a huge impact on quality of life. With new developments in sensors, it is now possible to continuously collect behavioral data which can be used to gain insights into mental health states. This has the potential to optimize psychiatric assessment and intervention processes, thereby improving patient experiences and outcomes. However, access to high quality medical data for research purposes is limited due to high costs, time, and privacy concerns, to name a few, and this is especially true for mental health related fields. To this extent, we present the "OBF-Psychiatric, a motor activity dataset of patients diagnosed with major depression, schizophrenia, and ADHD" dataset which comprises motor activity recordings of patients with bipolar and unipolar depression, schizophrenia, and ADHD (attention deficit hyperactivity disorder). The dataset also contains motor activity data from a clinical and healthy control group, making it suitable for building machine learning predictive models and other analytics. It contains recordings from 162 individuals totaling 1,565 days worth of motor activity data.心理健康是人类福祉的核心组成部分，其预防策略对生活质量具有显著影响。随着传感器技术的新进展，如今可连续采集行为数据，以此深入洞察个体的心理健康状态。这为优化精神科评估与干预流程提供了可行路径，进而改善患者的诊疗体验与治疗结局。然而，受高昂成本、耗时周期以及隐私顾虑等因素制约，用于科研的高质量医疗数据往往难以获取，这一问题在精神健康相关领域尤为突出。为此，我们推出「OBF-Psychiatric：重性抑郁障碍、精神分裂症与注意缺陷多动障碍患者运动活动数据集」，该数据集涵盖双相抑郁障碍、单相抑郁障碍、精神分裂症以及注意缺陷多动障碍（attention deficit hyperactivity disorder，ADHD）患者的运动活动记录。数据集同时纳入临床对照组与健康对照组的运动活动数据，适用于构建机器学习预测模型及开展其他分析研究。本数据集共收录162名受试者的运动活动数据，累计时长达1565天。