TOWalk

TOWalk: A Multi-Modal Dataset for Real-World Movement AnalysisThe TOWalk Dataset has been developed to support research on gait analysis, with a focus on leveraging data from head-worn sensors combined with other wearable devices. This dataset provides an extensive collection of movement data captured in both controlled laboratory settings and natural, unsupervised real-world conditions in Turin (Italy).The dataset features high-quality recordings from 15 healthy young participants, using the INDIP system alongside head-worn and wrist-mounted inertial sensors. The INDIP system includes two foot-mounted MIMUs, pressure-sensing insoles, and two ankle-mounted time-of-flight distance sensors. Additional head-worn and wrist-mounted MIMUs complement this setup, offering comprehensive insights into human locomotion.The TOWalk Dataset has already yielded excellent results in studies involving gait speed estimation, real-world gait event segmentation, and gait sequence detection, proving its utility for researchers working on wearable-based gait analysis and mobility monitoring in diverse settings.Key FeaturesSubjects: 15 healthy young participants.Devices: INDIP reference system (foot-mounted MIMUs, pressure insoles, and ankle-mounted distance sensors), head-worn MIMU, and wrist-worn MIMUs.Acquisition Conditions: Lab-based tests and unsupervised real-world activities.Data Types: Raw sensor data, segmented gait sequences, strides, and spatial-temporal gait parameters.Lab-Based AcquisitionsLab-based data were recorded in controlled indoor conditions through the following structured tests:Static Acquisition: Sensors placed on a flat surface, data collected in static conditions.Standing Acquisition: Subjects standing still while wearing sensors.Data Personalization: Controlled tasks, including standing, raising arms and legs, and walking 12 meters at a comfortable speed, to verify data quality.Walking Straight at Slow Speed: 12-meter walk at a slow speed (three trials).Walking Straight at Comfortable Speed: 12-meter walk at a comfortable speed (three trials).Walking Straight at Fast Speed: 12-meter walk at a fast speed (three trials).Walking with Turns: Walking back and forth over 12 meters with turns (three trials).Tests 1–3 are non-walking and primarily used for calibration. When performing gait-related operations, these tests should be excluded.Each trial began and ended with the participant standing still.Real-World AcquisitionsReal-world data were collected under natural, unsupervised conditions to ensure ecological validity. Participants were not given specific instructions but were required to:Raise and sit from a chair at least once.Climb stairs or ramps at least once.Enter a new room at least once.This setup ensured the inclusion of diverse, unconstrained movements.For real-world acquisitions, Recording4 contains the actual activity data. Recordings 1–3 are calibration recordings and are not required for most analyses.Data OrganizationThe dataset is structured by subject and condition:/data.mat: Raw sensor data sorted by subject ID and acquisition condition (Lab-based or Real-world)./participants_summary.xlsx: Demographic information for each participant.Files for Each RecordinginfoForAlgo.mat: Metadata containing demographic and sensor-related details.data.mat: Raw sensor data, spatial-temporal gait parameters, and metadata from the INDIP system.Notes and RecommendationsData QualityAll recordings have undergone rigorous quality checks to ensure reliability.General NotesParticipant IDs are pseudonymized for privacy.The dataset is intended for research and algorithm validation, not clinical applications.Suggested CitationIf you use the TOWalk Dataset in your research, please cite the following works:Salis, F., Bertuletti, S., Bonci, T., et al. (2023). “A Multi-Sensor Wearable System for the Assessment of Diseased Gait in Real-World Conditions.” Frontiers in Bioengineering and Biotechnology, 11:1143248. DOI: 10.3389/fbioe.2023.1143248.Tasca, P., et al. (2023). A machine learning-based pipeline for stride speed estimation with a head-worn inertial sensor. Gait & Posture. DOI: 10.1016/j.gaitpost.2023.07.341.Tasca, P., et al. (2023). A machine learning approach for stride speed estimation based on a head-mounted IMU. GNB2023 Conference Proceedings. ResearchGate Link.Tasca, P., et al. (2024). Estimating Gait Events and Speed in the Real World with a Head-Worn IMU. (pre-print). DOI: 10.36227/techrxiv.170654480.02767120/v1.Tasca, P., et al. (2024). Real-world gait detection with a head-worn inertial unit and features-based machine learning. Gait & Posture. DOI: 10.1016/j.gaitpost.2024.08.068.License and DisclaimerThe TOWalk Dataset © 2024 is licensed under CC BY-NC-ND 4.0. It is provided exclusively for research purposes. The authors make no guarantees regarding the dataset's accuracy or reliability and are not responsible for any misuse or misinterpretation of the data.