Synergy-based estimation of balance condition during walking tests

This dataset comprises data from six experimental participants, each undergoing nine walking trials. Each participant engaged in three trials of low-speed walking, three trials of medium-speed walking, and three trials of high-speed walking. The dataset includes multi-channel electromyography (EMG) data and center of pressure/ground reaction force (COP/GRF) data. Specifically, EMG data is utilized to extract muscle coordination activation time coefficients during human walking, and a deep learning model is established based on these coefficients to predict COP/GRF parameters. We consider using VCOP (Vertical Center of Pressure) to assess the balance state during human walking. Consequently, muscle coordination is employed for continuous estimation of the human balance state.