Development of a workflow for processing motion capture data for upper limb neuromusculoskeletal modeling
- Lehrstuhl für Ergonomie
- experimentell theoretisch
Key words: exosuit, exoskeleton, simulation, biomechanics, modelling, muscles, control, EMG, Motion capture
The upper limb plays an important role in the activities of daily living. A large number of musculoskeletal and neurological disorders can reduce the upper limb function and thus the quality of life of the person concerned. This type of disorders can occur after stroke, severe rotator cuff tear, spinal cord injury, or after breast cancer.
Soft exoskeletal assistive devices represent a promising solution to assist people with upper extremity impairments in the rehabilitation process outside the clinic or rehabilitation center.
Current solutions have deficiencies especially in human-centered, intention-based control, which currently prevents wider application. EMG-driven model based control strategies can help to overcome current drawbacks.
To integrate EMG signals into the control of exoskeletal devices efficiently, personalized models are needed describing the relation between muscle excitations and muscle forces or joint torques. Therefore, motion and EMG data need to be recorded and processed to create input signals for the neuromusculoskeletal model.
The goal of this study is to record motion and EMG Data of the shoulder joint and using this data to scale, parametrize and calibrate a musculoskeletal model and processing personalized data to serve as input for neuromusculoskeletal modeling.
- Adaption of an existing musculoskeletal model of the upper limb,
- Recording of shoulder motions and EMG data in the motion lab,
- Processing the recorded data,
- Implementing the recorded data into the musculoskeletal model,
- Performing musculoskeletal simulations,
- Basic understanding of the human musculoskeletal system,
- Experience in programming and/or simulation,
- Strong interest in biomechanics, control and soft robotics.
- Möglicher Beginn
Dr.-Ing. Manuel Ferle
Tel.: +49 162 2126321