COTIDIANA Dataset

About The COTIDIANA Dataset is a holistic, multimodal, and multidimensional dataset that captures three dimensions in which patients are frequently impacted by Rheumatic and Musculoskeletal Diseases (RMDs), namely, (a) mobility and physical activity, due to joint stiffness, fatigue, or pain; (b) finger dexterity, due to finger joint stiffness or pain; or (c) mental health (anxiety/depression level), due to the functional impairments or pain. We release this dataset to facilitate research in rheumatology, while contributing to the characterisation of RMD patients using smartphone-based sensor and log data. We gathered smartphone and self-reported data from 31 patients with RMDs and 28 age-matched controls, including (i) inertial sensors, (ii) keyboard metrics, (iii) communication logs, and (iv) reference tests/scales. We provide both raw and (pre-)processed dataset versions, to enable researchers or developers to use their own methods or benefit from the computed variables. Additional materials containing (a) illustrations, (b) visualization charts, and (c) variable descriptions can be consulted through this link. Citing When using this dataset, please cite P. Matias, R. Araújo, R. Graça, A. R. Henriques, D. Belo, M. Valada, N. N. Lotfi, E. Frazão Mateus, H. Radner, A. M. Rodrigues, P. Studenic, F. Nunes (2024) COTIDIANA Dataset – Smartphone-Collected Data on the Mobility, Finger Dexterity, and Mental Health of People With Rheumatic and Musculoskeletal Diseases, in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 11, pp. 6538-6547, DOI: 10.1109/JBHI.2024.3456069. Data structure The data is organised by participant and includes: Inertial Sensor Data, retrieved from accelerometer, gyroscope, and magnetometer sensors collected during three distinct walking exercises (Timed Up and Go, Daily Living Activity, and Simple Walk); Keyboard Dynamic Metrics, collecting 38 raw variables related with the keyboard typing performance while writing 10 sentences (e.g., number of errors, words-per-minute); Communication Logs, e.g., with weekly averages of number of calls and SMS sent or received; Validated Clinical Questionnaires, such as general Health (EQ-5D-5L), Multidimensional Health Assessment Questionnaire (MDHAQ), Hospital Anxiety and Depression Scale (HADS); Validated Functional Tests, including time to perform the Timed Up and Go (TUG) and Moberg Pick-Up Test (fine motor skills); Characterization Questionnaire, containing sociodemographic and clinical information. cotidiana_dataset├── info│ ├── codebook.xlsx│ ├── missings_report.csv├── processed│ ├── com_calls│ │ └── features.csv│ ├── com_sms│ │ └── features.csv│ ├── full│ │ └── cotidiana_dataset.csv│ ├── hd_kst│ │ └── features.csv│ ├── hd_mpu│ │ └── features.csv│ ├── mob_dla│ │ └── features.csv│ ├── mob_sw│ │ └── features.csv│ ├── mob_tug│ │ └── features.csv│ ├── quest│ └── features.csv├── raw│ ├── com_calls│ │ └── p[0-58]│ │ └── calls_log.csv│ ├── com_sms│ │ └── p[0-58]│ │ └── sms_log.csv│ ├── hd_kst│ │ └── p[0-58]│ │ ├── imu│ │ │ ├── Accelerometer_s[0-9].csv│ │ │ ├── Gyroscope_s[0-9].csv│ │ │ └── Magnetometer_s[0-9].csv│ │ └── keyboard│ │ └── kb_metrics.csv│ ├── hd_mpu│ │ └── p[0-58]│ │ └── mpu_time.csv│ ├── mob_dla│ │ └── p[0-58]│ │ ├── bag│ │ │ ├── Accelerometer.csv│ │ │ ├── Gyroscope.csv│ │ │ ├── Magnetometer.csv│ │ │ └── Annotation.csv│ │ └── pocket│ │ ├── Accelerometer.csv│ │ ├── Gyroscope.csv│ │ ├── Magnetometer.csv│ │ └── Annotation.csv│ ├── mob_sw│ │ └── p[0-58]│ │ ├── ann│ │ │ └── walk_ann.csv│ │ ├── bag│ │ │ ├── Accelerometer.csv│ │ │ ├── Gyroscope.csv│ │ │ ├── Magnetometer.csv│ │ │ └── Annotation.csv│ │ └── pocket│ │ ├── Accelerometer.csv│ │ ├── Gyroscope.csv│ │ ├── Magnetometer.csv│ │ └── Annotation.csv│ ├── mob_tug│ │ └── p[0-58]│ │ ├── bag│ │ │ ├── Accelerometer.csv│ │ │ ├── Gyroscope.csv│ │ │ ├── Magnetometer.csv│ │ │ └── Annotation.csv│ │ └── pocket│ │ ├── Accelerometer.csv│ │ ├── Gyroscope.csv│ │ ├── Magnetometer.csv│ │ └── Annotation.csv│ ├── quest│ └── features.csv## 关于COTIDIANA数据集 COTIDIANA数据集是一项全面性、多模态且多维度的数据集，覆盖风湿肌肉骨骼疾病（Rheumatic and Musculoskeletal Diseases, RMDs）患者常受影响的三大维度：(a) 活动能力与身体活动——因关节僵硬、疲劳或疼痛引发；(b) 手指灵活性——因手指关节僵硬或疼痛导致；(c) 心理健康状况（焦虑/抑郁水平）——因功能受损或疼痛所致。 本数据集的发布旨在推动风湿病学领域的研究，并助力基于智能手机传感器与日志数据的RMD患者特征刻画工作。 我们从31名RMD患者与28名年龄匹配的健康对照者处采集了智能手机数据与自我报告数据，涵盖：(i) 惯性传感器数据、(ii) 键盘指标、(iii) 通信日志、(iv) 参考测试/量表。我们同时提供原始数据集与（预）处理后的数据集版本，以便研究者或开发者既可以采用自定义分析方法，也可直接使用已计算得到的变量。额外材料包含(a) 示意图、(b) 可视化图表、(c) 变量说明，可通过指定链接查阅。 ## 引用说明 使用本数据集时，请引用如下文献：P. Matias、R. Araújo、R. Graça、A. R. Henriques、D. Belo、M. Valada、N. N. Lotfi、E. Frazão Mateus、H. Radner、A. M. Rodrigues、P. Studenic、F. Nunes（2024）发表于《IEEE生物医学与健康信息学杂志（IEEE Journal of Biomedical and Health Informatics）》的论文《COTIDIANA Dataset – Smartphone-Collected Data on the Mobility, Finger Dexterity, and Mental Health of People With Rheumatic and Musculoskeletal Diseases》，该文刊载于第28卷第11期，页码范围6538-6547，DOI: 10.1109/JBHI.2024.3456069。 ## 数据结构 本数据集按参与者进行组织，包含以下内容： 1. **惯性传感器数据**：源自加速度计、陀螺仪与磁力计，采集自三类不同的行走训练任务：计时起立行走试验（Timed Up and Go, TUG）、日常生活活动行走（Daily Living Activity, DLA）以及简易行走（Simple Walk, SW）； 2. **键盘动态指标**：采集与键盘打字表现相关的38项原始变量，受试者需录入10个句子（例如错误次数、每分钟单词数）； 3. **通信日志**：例如每周平均通话与短信的收发数量； 4. **经过验证的临床问卷**：包括通用健康量表（EQ-5D-5L）、多维健康评估问卷（Multidimensional Health Assessment Questionnaire, MDHAQ）、医院焦虑抑郁量表（Hospital Anxiety and Depression Scale, HADS）； 5. **经过验证的功能测试**：包括完成计时起立行走试验的时长，以及莫伯格拾取测试（Moberg Pick-Up Test, MPU，用于评估精细运动技能）的结果； 6. **特征问卷**：包含社会人口学与临床相关信息。 ## 目录结构 cotidiana_dataset ├── info │ ├── codebook.xlsx │ ├── missings_report.csv ├── processed │ ├── com_calls │ │ └── features.csv │ ├── com_sms │ │ └── features.csv │ ├── full │ │ └── cotidiana_dataset.csv │ ├── hd_kst │ │ └── features.csv │ ├── hd_mpu │ │ └── features.csv │ ├── mob_dla │ │ └── features.csv │ ├── mob_sw │ │ └── features.csv │ ├── mob_tug │ │ └── features.csv │ └── quest │ └── features.csv ├── raw │ ├── com_calls │ │ └── p[0-58] │ │ └── calls_log.csv │ ├── com_sms │ │ └── p[0-58] │ │ └── sms_log.csv │ ├── hd_kst │ │ └── p[0-58] │ │ ├── imu │ │ │ ├── Accelerometer_s[0-9].csv │ │ │ ├── Gyroscope_s[0-9].csv │ │ │ └── Magnetometer_s[0-9].csv │ │ └── keyboard │ │ └── kb_metrics.csv │ ├── hd_mpu │ │ └── p[0-58] │ │ └── mpu_time.csv │ ├── mob_dla │ │ └── p[0-58] │ │ ├── bag │ │ │ ├── Accelerometer.csv │ │ │ ├── Gyroscope.csv │ │ │ ├── Magnetometer.csv │ │ │ └── Annotation.csv │ │ └── pocket │ │ ├── Accelerometer.csv │ │ ├── Gyroscope.csv │ │ ├── Magnetometer.csv │ │ └── Annotation.csv │ ├── mob_sw │ │ └── p[0-58] │ │ ├── ann │ │ │ └── walk_ann.csv │ │ ├── bag │ │ │ ├── Accelerometer.csv │ │ │ ├── Gyroscope.csv │ │ │ ├── Magnetometer.csv │ │ │ └── Annotation.csv │ │ └── pocket │ │ ├── Accelerometer.csv │ │ ├── Gyroscope.csv │ │ ├── Magnetometer.csv │ │ └── Annotation.csv │ ├── mob_tug │ │ └── p[0-58] │ │ ├── bag │ │ │ ├── Accelerometer.csv │ │ │ ├── Gyroscope.csv │ │ │ ├── Magnetometer.csv │ │ │ └── Annotation.csv │ │ └── pocket │ │ ├── Accelerometer.csv │ │ ├── Gyroscope.csv │ │ ├── Magnetometer.csv │ │ └── Annotation.csv │ └── quest │ └── features.csv