This dataset was created in the context of the EU project Tolife, which aims at validating an artificial intelligence solution to process daily life patient data captured by non-obtrusive sensors to enable personalized treatment, assessment of health outcomes and improved quality of life in chronic obstructive pulonary disease patients.

In this study, we utilized the Tolife sensor platform, comprising a Samsung Galaxy A14 smartphone, a Samsung Galaxy Watch 5, and custom smart shoes. These devices gathered inertial sensor data, including accelerometers, gyroscopes, and pressure sensors, for gait analysis. The smart shoes feature integrated electronics, including inertial measurement units and Bluetooth connectivity, along with pressure sensors in the insole. Reference measurements for gait speed were obtained using the Xsens Awinda inertial motion tracker with MVN Analyze software. Twenty participants underwent a modified Six Minute-Walking-Test (6MWT) at three different paces: slow, medium, and fast, with consistent device placement. The dataset is organized into folders storing wearable device data and reference measurements, with individual .csv files for each sensor and test pace.

The dataset is structured in two folders: the first stores data acquired from the wearable devices, while the second one contains the reference data. Within the folders, the organization follows the structure outlined in the "readme" file. The distances covered by subjects during the tests are stored in the “smwd.csv” file. Here, each rows represent a subject, and the columns the test’s paces: slow, medium and fast. 

When using this dataset, please cite "Zanoletti, M.; Bufano, P.; Bossi, F.; Di Rienzo, F.; Marinai, C.; Rho, G.; Vallati, C.; Carbonaro, N.; Greco, A.; Laurino, M.; et al. Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings. Sensors 2024, 24, 3205. https://doi.org/10.3390/s24103205"