Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses
Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning contr...
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Zusammenfassung: | Recent advances in upper limb prostheses have led to significant improvements
in the number of movements provided by the robotic limb. However, the method
for controlling multiple degrees of freedom via user-generated signals remains
challenging. To address this issue, various machine learning controllers have
been developed to better predict movement intent. As these controllers become
more intelligent and take on more autonomy in the system, the traditional
approach of representing the human-machine interface as a human controlling a
tool becomes limiting. One possible approach to improve the understanding of
these interfaces is to model them as collaborative, multi-agent systems through
the lens of joint action. The field of joint action has been commonly applied
to two human partners who are trying to work jointly together to achieve a
task, such as singing or moving a table together, by effecting coordinated
change in their shared environment. In this work, we compare different
prosthesis controllers (proportional electromyography with sequential
switching, pattern recognition, and adaptive switching) in terms of how they
present the hallmarks of joint action. The results of the comparison lead to a
new perspective for understanding how existing myoelectric systems relate to
each other, along with recommendations for how to improve these systems by
increasing the collaborative communication between each partner. |
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DOI: | 10.48550/arxiv.2212.14124 |