Multimodal biomechanical dataset from transtibial amputees and able-bodied adults across five locomotion tasks - Abarca, V.E.; Chavez, J.; Eulogio, L.; Viviano, C.; Elías, D.A. (Nature Research, 2026)
This dataset addresses the need for multimodal biomechanical recordings during over-ground walking, ramps, and stairs by synchronously capturing electromyographic (EMG), inertial (IMU), and plantar pressure data. We collected data from 45 adults (15 with unilateral transtibial amputation and 30 without amputation) who completed five standardized locomotor tasks: level walking, ramp ascent/descent, and stair ascent/descent. Each participant performed 50 supervised trials. Wireless EMG and IMU sensors (Delsys Trigno Avanti) measured muscle activation and kinematics, while intelligent insoles (XSENSOR) captured plantar pressure distribution. Raw data were saved in.hpf (EMG/IMU) and.XSN (pressure) formats, with processed outputs in.csv files. All data are organized by task and sensor type, including complete participant metadata. Key dataset outputs include time-normalized EMG amplitudes, segment kinematics, and pressure maps across terrains and populations. The dataset was validated technically and experimentally during the acquisition. This resource enables quantitative analysis of gait adaptation and supports machine learning for locomotion classification. Data are provided in accessible formats to foster reuse in biomechanics, rehabilitation engineering, robotics, and clinical gait research.
Keywords: Stairs; Inertial measurement unit; Gait; Rehabilitation; Pressure sensor; Task (project management); Electromyography; Gait analysis; Activity recognition; https://purl.org/pe-repo/ocde/ford#2.03.01