Multimodal biomechanical dataset from transtibial amputees and able-bodied adults across five locomotion tasks

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.

本数据集针对平地行走、坡道及楼梯运动过程中的多模态生物力学记录需求，通过同步采集肌电（electromyographic，EMG）、惯性测量单元（inertial measurement unit，IMU）及足底压力数据予以满足。我们从45名成年受试者中采集数据，其中15名为单侧胫骨截肢患者，30名为非截肢者；所有受试者均完成了5项标准化运动任务：平地行走、坡道上下行及楼梯上下行。每名受试者完成50次有监督试验。无线肌电与惯性测量单元传感器（Delsys Trigno Avanti）用于采集肌肉激活度及运动学数据，智能鞋垫（XSENSOR）则用于获取足底压力分布数据。原始数据分别以.hpf（肌电/惯性测量单元数据）和.XSN（压力数据）格式存储，处理后的输出数据则保存为.csv文件。所有数据均按任务类型与传感器类别进行组织，并包含完整的受试者元数据。数据集的核心输出包括不同运动场景与受试群体下的时间归一化肌电振幅、节段运动学数据及压力分布图。本数据集在采集过程中已完成技术与实验层面的验证。该资源可用于步态适应性的定量分析，并为运动分类相关的机器学习研究提供支撑。数据集以易于获取的格式提供，以推动其在生物力学、康复工程、机器人学及临床步态研究领域的复用。