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Updated: Jun 6, 2026
08:55
Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion
Published on: February 5, 2020
Comparison of force and power generation patterns and their predictions under different external dynamic environments
Pratik Y Chhatbar1, Joseph T Francis
1Joint Graduate Program in Biomedical Engineering between SUNY Downstate Medical Center and Polytechnic Institute of New York University at Brooklyn, NY 11203, USA. pratik.chhatbar@downstate.edu
Summary
This study explores using neural activity to predict movement dynamics like force and power for better brain-machine interfaces (BMIs). It aims to develop a generalized force-based BMI (fBMI) for controlling prosthetic limbs in varied environments.
Area of Science:
- Neuroscience
- Robotics
- Biomechanics
Background:
- Brain-machine interfaces (BMIs) currently predict kinematic variables like position and velocity for real-time control.
- Real-world object manipulation involves generating variable forces influenced by object properties and environmental dynamics.
- Existing BMIs lack comprehensive control over prosthetic limbs under diverse dynamic conditions.
Purpose of the Study:
- To investigate the distribution patterns and predictive efficiency of force and power in neural activity across different dynamic environments.
- To identify a force-related neural parameter that offers optimal predictive efficiency for generalized control in BMIs.
- To advance the development of a force-based brain-machine interface (fBMI) for enhanced prosthetic limb control.
Main Methods:
- Analyzing neural activity data correlated with movement dynamics.
- Evaluating the predictive performance of force and power parameters under varying inertial and environmental conditions.
- Assessing the generalization capability of different force-related parameters for cross-environment prediction.
Main Results:
- Demonstrated distinct distribution patterns for neural correlates of force and power.
- Quantified the predictive efficiency of these dynamics parameters under different environmental loads.
- Identified specific force-related parameters showing superior generalization across dynamic conditions.
Conclusions:
- Neural signals contain rich information about movement dynamics beyond kinematics.
- Incorporating dynamics parameters like force and power is crucial for advanced BMI control.
- A generalized force-based BMI (fBMI) is feasible and essential for intuitive prosthetic limb function.
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