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

Area of Science:

• Biomechanics
• Human Movement Analysis
• Wearable Technology

Background:

• Laboratory-based motion capture systems offer high accuracy but are limited to controlled environments.
• Wearable systems like inertial motion capture and pressure insoles allow real-world measurements but need validation.
• A gap exists in synchronized, multimodal biomechanical datasets for diverse human movements.

Purpose of the Study:

• To create a comprehensive dataset of synchronized full-body kinematic and kinetic measurements.
• To combine data from laboratory (optical motion capture, instrumented treadmill) and wearable (inertial motion capture, insoles) systems.
• To support validation of wearable sensors, machine learning model training, and advanced gait/movement analysis.

Main Methods:

• Collected synchronized data from optical motion capture, inertial motion capture, an instrumented treadmill, and pressure-sensing insoles.
• Recruited twelve healthy young adults to perform various movements.
• Included static postures, treadmill walking (varying speeds/inclines), running, squats, weight shifts, and squat jumps.

Main Results:

• Generated a dataset with raw signals from four distinct measurement systems.
• Included derived biomechanical variables computed using a biomechanical model.
• Achieved synchronized multimodal data capturing full-body kinematics and kinetics.

Conclusions:

• The comprehensive dataset enables robust validation of wearable biomechanical sensors.
• Facilitates the development and training of machine learning models for human movement analysis.
• Supports diverse biomechanical research beyond basic level walking, including complex functional tasks.