DOI: 10.5281/zenodo.16949732
title: Real-World Gait Speed Estimation: Laboratory and Real-Life Patient Data from TOLIFE Wearables

description: TOLIFE Wearable Dataset Collection

This data collection was carried out in the context of the TOLIFE Horizon Europe project, which aims to develop and validate artificial intelligence (AI) solutions for processing patient data collected through non-intrusive wearable sensors. The ultimate goal is to enable personalized treatment, monitor health outcomes, and improve the quality of life of patients with Chronic Obstructive Pulmonary Disease (COPD).

The dataset is composed of four distinct subsets, each acquired under different conditions and with specific goals:

1. Gait Speed Dataset

2. Activity Recognition Dataset

3. Validation Dataset

4. Daily Life Dataset

 

1. Gait Speed Dataset:

Objective: To collect reference gait speed data under controlled conditions using wearable sensors, with a focus on validating gait speed estimation algorithms.

 

Materials:

Smartphone: Samsung Galaxy A14

Smartwatch: Samsung Galaxy Watch 5

Smart shoes (custom prototype)

Acquisition Protocol:

Participants: 25 healthy individuals (10 M, 15 F), age: 28.9 ± 4.8 years

Test: Six Minute Walking Test (6MWT) along a 10-meter path

Paces: Slow, Medium, Fast (self-selected)

 

Device Placement:

- Watch: left 

- Phone: left front pocket (screen facing thigh, Y-axis upward)

- Reference System: Xsens Awinda (17 wireless IMUs) with MVN Analyze software

- Reference gait speed = horizontal velocity of Center of Mass (CoM)

Folder Content:

Wearable folder: individual .csv files for each sensor (phone, watch, shoes)

Reference folder: CoM speed from Xsens + walked distances in smwd.csv

 

2. Activity Recognition Dataset:

Objective:

To build a dataset for training AI models to recognize different daily activities (e.g., resting, walking, stair climbing) using wearable sensor data.

Materials:

Same TOLIFE wearable platform as above (phone, watch, smart shoes).

Acquisition Protocol:

Participants: 15 healthy individuals (7 F, 8 M), age: 27.3 ± 3 years

Activities:

- Resting: lying, sitting, standing (max 5 sec walking allowed)

- Walking: 3 sessions (2 x 180 m + 1 x 450 m with directional changes)

- Stair climbing: continuous ascent and descent for ~2 minutes

Smartwatch placement: user's preferred wrist

Folder Content:

Data folder: wearable sensor data for each recording session

Timestamps folder: .csv files with annotated start/stop times and activity labels

 

3. Validation Dataset:

Objective:

To test the robustness of trained activity and gait estimation models in less controlled, real-world environments.

Materials: Same TOLIFE wearable platform.

Acquisition protocol:Participants: 10 healthy adults (5 M, 5 F), age: 43.8 ± 12.1 yearsProtocol: free walking outdoors, with mixed activities:- Resting, level walking, stair climbing- 4 walking paths of known length (100–280)- Device placement: user’s preferred side (watch), front pants pocket (phone)

Folder Content:Data folder: continuous sensor data recordingsTimestamps folder: annotated transitions between activities

 

4. Daily Life Dataset:

 

Objective: To capture real-world data from COPD patients in daily life conditions for long-term monitoring of mobility and gait speed.

Materials: TOLIFE wearable platform (smartphone, smartwatch, smart shoes)

Acquisition Protocol:Participants: 38 COPD patients (23 M, 15 F), age: 63.3 ± 5.8 yearsDuration: continuous data collection over two weeksInitial Reference Visit (RV):- 2 clinical 6MWTs performed by a pulmonologist, average distance used as a reference value- Instructions: participants used the devices freely (indoors/outdoors) in comfortable conditions

Folder Content:Data folder: two weeks of wearable sensor recordings per patientClinical Six_Minute_Walking_Distance.csv: distances from RV

 

Citation:

When using any portion of this dataset collection, 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