ArticleFrontiers in neurorobotics2022
Continuous Semi-autonomous Prosthesis Control Using a Depth Sensor on the Hand.
Article in Frontiers in neurorobotics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Assessment of artificial intelligence-based control algorithms to be implemented in an affordable transradial myoelectric prosthesis.Scientific reports · 2026Article
- Shared human-machine control of an intelligent bionic hand improves grasping and decreases cognitive burden for transradial amputees.Nature communications · 2025Article
- A Semi-Autonomous Telemanipulation Order-Picking Control Based on Estimating Operator Intent for Box-Stacking Storage Environments.Sensors (Basel, Switzerland) · 2025Article
- A Review of Myoelectric Control for Prosthetic Hand Manipulation.Biomimetics (Basel, Switzerland) · 2023Review
- HANDdata - first-person dataset including proximity and kinematics measurements from reach-to-grasp actions.Scientific data · 2023Article
- Hand Grasp Pose Prediction Based on Motion Prior Field.Biomimetics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern myoelectric prostheses can perform multiple functions (e.g., several grasp types and wrist rotation) but their intuitive control by the user is still an open challenge. It has been recently demonstrated that semi-autonomous control can allow the subjects to operate complex prostheses effectively; however, this approach often requires placing sensors on the user. The present study proposes a system for semi-autonomous control of a myoelectric prosthesis that requires a single depth sensor placed on the dorsal side of the hand. The system automatically pre-shapes the hand (grasp type, size, and wrist rotation) and allows the user to grasp objects of different shapes, sizes and orientations, placed individually or within cluttered scenes. The system "reacts" to the side from which the object is approached, and enables the user to target not only the whole object but also an object part. Another unique aspect of the system is that it relies on online interaction between the user and the prosthesis; the system reacts continuously on the targets that are in its focus, while the user interprets the movement of the prosthesis to adjust aiming. Experimental assessment was conducted in ten able-bodied participants to evaluate the feasibility and the impact of training on prosthesis-user interaction. The subjects used the system to grasp a set of objects individually (Phase I) and in cluttered scenarios (Phase II), while the time to accomplish the task (TAT) was used as the performance metric. In both phases, the TAT improved significantly across blocks. Some targets (objects and/or their parts) were more challenging, requiring thus significantly more time to handle, but all objects and scenes were successfully accomplished by all subjects. The assessment therefore demonstrated that the system is indeed robust and effective, and that the subjects could successfully learn how to aim with the system after a brief training. This is an important step toward the development of a self-contained semi-autonomous system convenient for clinical applications.
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