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Published October 9, 2026 | Version V2.0
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Health Indices Dataset from Clinical Study A - TOLIFE Project

Description

Health Indices Dataset from Clinical Study A - TOLIFE Project

This dataset has been developed in the context of the European HORIZON project TOLIFE (Combining Artificial Intelligence and smart sensing TOward better management and improved quality of LIFE in COPD, Grant Agreement No. 101057103). The project aims to enable continuous and unobtrusive health monitoring of patients affected by Chronic Obstructive Pulmonary Disease (COPD) by leveraging a multi-modal sensing infrastructure and AI-based data analytics.
 
In TOLIFE, two clinical studies are planned; the first is called "Clinical Study A (CSA)". In CSA, a group of patients was followed for 12 months with the TOLIFE sensor kit, while periodic clinical examinations provided the clinical references for the AI tools. CSA involved 86 monitored participants across three sites (Germany, Spain and Italy), enrolled between May 2024 and September 2025.
 
This dataset (version 2.0) contains the sensor-derived health indices of the complete CSA monitoring period (May 2024 - April 2026), extracted from heterogeneous signals collected using the TOLIFE sensor kit. The kit integrates both commercial devices (smartphone, smartwatch, spirometer) and custom-made IoT devices (smart mattress cover, smart shoes, environmental sensing unit), designed to collect physiological, behavioral, and environmental data from patients during their everyday life. The dataset also contains the periodic clinical examinations of CSA (up to five scheduled visits per participant, T1 to T5) and the exacerbation register. Version 2.0 supersedes version 1.0 (first year of monitoring, 74 participants): it adds 12 participants and the Italian site, extends the monitoring period to the end of the study, adds the daily estimates of the CAT, CCQ and mMRC clinical scores, and updates the clinical records to the end of the study. A participant manifest (kits.csv) and dataset-level clinical and exacerbation tables are included.
 
To obtain the sensor-derived health indices of CSA, data are processed through a hierarchical and modular analytics pipeline, which transforms raw signals from the TOLIFE sensor kit into clinically meaningful health-related indicators. The first stage involves the extraction of primary features (e.g., gait speed, sleep duration, heart rate), while subsequent layers aggregate and interpret these features to produce higher-level indices for health assessment of the following health domains:
 
Mobility and Gait Analysis
Mobility-related indices are computed by combining sensor data from the smartphone, smartwatch, and smart shoes. Walking episodes are detected using lightweight machine learning models, and gait speed is estimated via a modular deep learning model capable of adapting to the number of available devices. From these models, several metrics are computed, including mean gait speed, step length, estimated six-minute walking distance, and total walked time and distance, aggregated at daily level.
 
Sleep and Environmental Indices
Night-time parameters are derived from the smart mattress cover and environmental unit. A dedicated algorithm classifies segments of the night into "off-bed", "on-bed with movement", and "on-bed still" phases. Based on these labels, the system estimates total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, and the number of movement episodes. In parallel, heart rate and breathing rate during sleep are computed from pressure and inertial signals, using time-domain and spectral analysis methods. Environmental metrics, such as temperature, humidity, air quality, light and sound intensity, are recorded continuously and summarized separately for the night-time and daytime periods of each day.
 
Cardiac Function (PPG-based)
The smartwatch provides photoplethysmographic (PPG) signals used to estimate heart rate variability (HRV) markers (specifically, pulse rate variability, PRV). A deep-learning-based denoising pipeline, based on convolutional autoencoders (CNN-DAEs), is applied to remove motion artifacts. Peaks are detected from the cleaned signals to derive time-domain features such as stdRR and RMSSD, which reflect autonomic nervous system regulation.
 
Pulmonary Function and Clinical Scores
Respiratory function is assessed through a portable spirometer, used by patients at home. Key metrics such as FEV1 and PEF are extracted, and their daily mean and range are reported. Additionally, higher-level clinical scores, including the COPD Assessment Test (CAT), the Clinical COPD Questionnaire (CCQ), and the modified Medical Research Council dyspnea scale (mMRC), are estimated daily via both data-driven and literature-informed models, using the previously computed indices as inputs.
 
The README.md file included in the record documents the folder structure, every column of every file, the data quality flags and the version history.

Files

index.zip

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Additional details

Funding

European Commission
TOLIFE - Combining Artificial Intelligence and smart sensing TOward better management and improved quality of LIFE in chronic obstructive pulmonary disease 101057103