Introduction: Despite recent technological advances that have led to sophisticated bionic prostheses, attaining embodied solutions still remains a challenge. Recently, the investigation of prosthetic embodiment has become a topic of interest in the research community, which deals with enhancing the perception of artificial limbs as part of users' own body. Surface electromyography (sEMG) interfaces have emerged as a promising technology for enhancing upper-limb prosthetic control. However, little is known about the impact of these sEMG interfaces on users' experience regarding embodiment and their interaction with different functional levels.
Methods: To investigate this aspect, a comparison is conducted among sEMG configurations with different number of sensors (4 and 16 channels) and different time delay. We used a regression algorithm to simultaneously control hand closing/opening and forearm pronation/supination in an immersive virtual reality environment. The experimental evaluation includes 24 able-bodied subjects and one prosthesis user. We assess functionality with the Target Achievement Control test, and the sense of embodiment with a metric for the users perception of self-location, together with a standard survey.
Results: Among the four tested conditions, results proved a higher subjective embodiment when participants used sEMG interfaces employing an increased number of sensors. Regarding functionality, significant improvement over time is observed in the same conditions, independently of the time delay implemented.
Conclusions: Our work indicates that a sufficient number of sEMG sensors improves both, functional and subjective embodiment outcomes. This prompts discussion regarding the potential relationship between these two aspects present in bionic integration. Similar embodiment outcomes are observed in the prosthesis user, showing also differences due to the time delay, and demonstrating the influence of sEMG interfaces on the sense of agency.
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| http://dx.doi.org/10.1186/s12984-024-01352-7 | DOI Listing |
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Article Synopsis
- Gesture recognition using surface electromyography (sEMG) shows promise for intuitive human-machine interfaces, but achieving reliability is the main challenge that needs to be addressed for practical use.
- * This research examined how different limb conditions and electrode placements affect sEMG classification performance, comparing static and dynamic situations at the wrist, elbow, and midpoint between them.
- * Results showed that dynamic training improved classification accuracy, particularly at the wrist and midpoint, and using optimal features significantly reduced classification errors across all locations, highlighting the need for tailored training conditions.*
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