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    Multimodal biomechanical dataset from transtibial amputees and able-bodied adults across five locomotion tasks
    (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.
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    Design exploration and kinematic validation of a transtibial prosthesis using a 2SPU-RU parallel mechanism
    (Springer Science and Business Media Deutschland GmbH, 2025)
    This study presents the design exploration and kinematic validation of a transtibial prosthesis based on a 2SPU-RU parallel mechanism. The prototype is currently at Technology Readiness Level 3 (TRL 3), indicating an early-stage concept that has been evaluated under controlled conditions. The mechanism replicates dorsiflexion–plantarflexion, inversion–eversion, and abduction–adduction, corresponding to ankle motion in the sagittal, frontal, and transverse anatomic planes. The system integrates electric actuators and an ESP32 DevKit V1 microcontroller to control joint movements. Functional tests were conducted to assess kinematic performance using inertial sensors and video tracking. Additional evaluations included force distribution analysis using pressure insoles during dorsiflexion and plantarflexion, as well as energy consumption measurements across gait cycles. While the prototype demonstrates the ability to reproduce fundamental gait patterns in a suspended setup, limitations in torque, speed, and control precision restrict its current applicability. These findings provide a foundation for further development. Future work will focus on improving actuator performance, refining control strategies, and extending validation to real-world scenarios and amputee trials.
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