A Multimodal Dataset of Full-Body Kinematics and Kinetics from Laboratory- and Wearable-Based Systems

Kinematic and kinetic biomechanical measurements are critical for understanding human movements. Although laboratory-based systems such as optical motion capture (OMC) and instrumented treadmills provide gold-standard accuracy, their use is confined to controlled laboratory settings. Wearable systems, including inertial motion capture (IMC) and pressure-sensing insoles, enable measurements in real-world conditions but require validation against laboratory-based references. In this study, we present a comprehensive dataset that combines laboratory- and wearable-based systems to capture synchronized full-body kinematic and kinetic measurements. Twelve healthy young adults performed static postures, treadmill walking at multiple speeds and inclines, treadmill running, and functional movement tasks, including squats, weight shifts, and squat jumps. The dataset included raw signals from four measurement systems-OMC, IMC, instrumented treadmill, and insoles-as well as from biomechanical variables derived using a biomechanical model. Synchronized multimodal signals and processed biomechanical variables support diverse applications beyond level walking, including wearable sensor validation, machine learning model training, and gait or movement analysis.