DOI: 10.5281/zenodo.8125704
title: The MD-Vibe Dataset: Footstep-Induced Floor Vibration Data for Functional Gait Assessment in Individuals with Muscular Dystrophy

description: The purpose of this dataset is to evaluate the performance of floor vibration sensing in tracking the progression of muscular dystrophy through individuals’ gait patterns. We recruited human subjects (N=36) and conducted experiments at Stanford University and Nationwide Children's Hospital in Columbus, Ohio, with healthy human subjects (N=21) and children with MD (N=15). The experiments consist of two phases - Phase 1 is a pilot study to test the feasibility of floor vibration sensing in capturing gait characteristics in lab settings; Phase 2 has two hospital studies to evaluate our sensing method in real life. This dataset consists of floor vibration data (vertical velocity of floor vibration) induced by 9 healthy subjects’ footsteps from these two phases, named <strong>lab data.zip</strong>, <strong>hospital data 1.zip</strong>, and <strong>hospital data 2.zip</strong>, respectively. The complete dataset requires a data-sharing agreement with Nationwide Children's Hospital. The custom coding scripts (in MATLAB and Python) to process the data are included in the <strong>code.zip</strong> file, which includes 3 processing steps: 1) Preprocessing and Footstep Detection, 2) Feature Extraction, and 3) Model Prediction, the scripts of each step are grouped into a folder with their corresponding names. Detailed function descriptions are included in the scripts. The footstep-induced structural vibration data is stored as both raw data and as individual footsteps. The raw data files start with “raw_”, each consisting of a series of consecutive footsteps (see the sample plot). The individual footstep data files start with “detected_steps_”, each consisting of one single footstep detected from the raw data. The dataset is stored in MAT file format that can be accessed through MATLAB. The sensing unit consists of 5 components: 1) the geophone (SM-24), 2) the amplification module, 3) the processor board, 4) the data acquisition module (NI-Daq), and 5) the power cables. The sensing unit converts the structural vibration velocity into voltage records. The sampling frequency is 500 Hz for the lab study and 25600 Hz for the hospital studies. The experiment setup, sample data plot, and code usage can be found in <strong>Dataset Description.pdf</strong>. For more details about the hospital studies, please refer to the MD-Vibe paper at the following link: https://doi.org/10.1145/3410530.3414610 Please cite this dataset as: Yiwen Dong, Megan Iammarino, Jingxiao Liu, Jesse Codling, Jonathon Fagert, Mostafa Mirshekari, Linda Lowes, Pei Zhang, and Hae Young Noh. 2023. The MD-Vibe Dataset: Footstep-Induced Floor Vibration Data for Functional Gait Assessment in Individuals with Muscular Dystrophy. Zenodo, DOI: https://doi.org/10.5281/zenodo.8125704 Yiwen Dong, Joanna Jiaqi Zou, Jingxiao Liu, Jonathon Fagert, Mostafa Mirshekari, Linda Lowes, Megan Iammarino, Pei Zhang, and Hae Young Noh. 2020. MD-Vibe: physics-informed analysis of patient-induced structural vibration data for monitoring gait health in individuals with muscular dystrophy. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (UbiComp-ISWC '20). Association for Computing Machinery, New York, NY, USA, 525–531. https://doi.org/10.1145/3410530.3414610
description: {"references": ["Yiwen Dong, Joanna Jiaqi Zou, Jingxiao Liu, Jonathon Fagert, Mostafa Mirshekari, Linda Lowes, Megan Iammarino, Pei Zhang, and Hae Young Noh. 2020. MD-Vibe: physics-informed analysis of patient-induced structural vibration data for monitoring gait health in individuals with muscular dystrophy. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (UbiComp-ISWC '20). Association for Computing Machinery, New York, NY, USA, 525\u2013531. https://doi.org/10.1145/3410530.3414610"]}