Natural Myocontrol in a Realistic Setting: a Comparison Between Static and Dynamic Data Acquisition

Area of Science:

• Biomedical Engineering
• Rehabilitation Robotics
• Human-Computer Interaction

Background:

• Natural myocontrol uses pattern recognition of voluntary muscular activations for intuitive robotic limb control.
• Myoelectric signal reliability depends on initial data capture, which must account for limb position and orientation variability.
• Current best practice involves collecting data in multiple body postures to enhance signal reliability.

Purpose of the Study:

• To investigate the relative merits of static versus dynamic data acquisition protocols for myocontrol.
• To compare the effectiveness of myoelectric signal collection while users maintain static postures versus moving limbs dynamically.

Main Methods:

• Fourteen able-bodied subjects performed complex, realistic bimanual activities of daily living using two dexterous hand prostheses.
• Data was collected using both static (holding positions) and dynamic (moving through positions at constant velocity) acquisition protocols.
• Performance was evaluated based on total execution times and user experience (tiring).

Main Results:

• No significant difference was found in the total execution times between static and dynamic data acquisition protocols.
• Dynamic data acquisition was reported as faster and less tiring for the subjects compared to the static protocol.
• This suggests dynamic acquisition may be more efficient and comfortable for users.

Conclusions:

• Dynamic data acquisition is a viable and potentially preferable method for collecting myoelectric signals for natural myocontrol.
• The dynamic protocol offers advantages in speed and user comfort without compromising task performance.
• Dynamic data acquisition should be considered for future myocontrol system development and training.