Gait analysis is crucial for the management of various neurological and musculoskeletal disorders. As the number of patients and elderly suffering from gait disorders increases, there is a steady increase in the demand for gait monitoring and rehabilitation treatment. The accurate detection of characteristic gait events is a valuable tool for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. Towards this end, the Smart-Insole dataset is released for the development and evaluation of computational methods focusing on gait analysis. The Smart-Insole Dataset (v1.0) includes data derived from pressure sensor insoles, while 29 participants (Healthy Adults, Elderly, Parkinson’s disease patients) performed two different set of tests: The Walk Straight and Turn Test, and the Timed Up and Go Test. A neurologist, specialized on movement disorders, has evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson’s Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm.
For the development of the Smart-Insole Dataset v1.0, the Moticon SCIENCE pressure sensor insole was selected. For the recordings, the sampling rate was set at 100Hz. The generated file for each recording includes 51 features in total. Specifically, 25 values for the left and 25 values for the right leg plus the timestamp:
- The Timestamp (ms)
- The pressure from 1 to 16 sensors (N/cm2)
- The acceleration in x,y,z axis (g)
- The angular rate in ωx, ωy,ωz (dps)
- The computed, center of pressure in x,y ( -0.5…+0.5 (related to insole length/width)
- The computed by MOTICON, total force (N)
For more information, please refer to the publication: C. Chatzaki, V. Skaramagkas, N. Tachos, G. Christodoulakis, E. Maniadi, Z. Kefalopoulou, D. I. Fotiadis, and M. Tsiknakis, “The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson’s Patients,” Sensors, vol. 21, no. 8, p. 2821, Apr. 2021.
Data availability: The Smart-Insole Dataset v1.0 is available upon request (please email Biomedical Informatics Laboratory at: bmi@hmu.gr) for non-commercial, research and educational purposes only, after the sign of a database usage agreement which establish the terms and conditions of data usage.
Acknowledgment: This research has been funded by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH –CREATE –INNOVATE (project code: Τ1ΕΔΚ-01888).
