Abstract
BackgroundState-of-the-art bionic hands incorporate hi-tech devices which try to overcome limitations of conventional single grip systems. Unfortunately, their complexity often limits mechanical robustness and intuitive prosthesis control. Recently, the translation of neuroscientific theories (i.e. postural synergies) in software and hardware architecture of artificial devices is opening new approaches for the design and control of upper-limb prostheses.MethodsFollowing these emerging principles, previous research on the SoftHand Pro, which embeds one physical synergy, showed promising results in terms of intuitiveness, robustness, and grasping performance. To explore these principles also in hands with augmented capabilities, this paper describes the SoftHand 2 Pro, a second generation of the device with 19 degrees-of-freedom and a second synergistic layer. After a description of the proposed device, the work explores a continuous switching control method based on a myoelectric pattern recognition classifier.ResultsThe combined system was validated using standardized assessments with able-bodied and, for the first time, amputee subjects. Results show an average improvement of more than 30% of fine grasp capabilities and about 10% of hand function compared with the first generation SoftHand Pro.ConclusionsEncouraging results suggest how this approach could be a viable way towards the design of more natural, reliable, and intuitive dexterous hands.
Highlights
Capturing the richness and complexity of the sensorymotor functions of the human hand in a prosthetic device remains one of the challenge in modern science and engineering [1]
From an engineering point of view, significant improvements over conventional methods are given by the introduction of simultaneous and proportional myoelectric control using linear regression techniques [13, 14], which create a continuous map between EMG signals and the intended movements or pattern recognition algorithms, mostly based on the information of muscles groups
We introduce the SoftHand 2 Pro (SH2P), the prosthetic release, characterized by a light-weight design and suitable to be connected with a prosthetic socket and multi-channel Surface wlectromyographic (sEMG) system
Summary
Capturing the richness and complexity of the sensorymotor functions of the human hand in a prosthetic device remains one of the challenge in modern science and engineering [1]. From a clinical point of view, two successful and innovative approaches are those based on Targeted Muscle Reinnervation (TMR) [8, 9] and intramuscular EMG [10], via wireless transceivers [11] or an osseointegrated implant [12] All of these techniques considerably increase users’ capabilities to selectively activate several muscles in a more natural fashion, and to control multiple DoFs. From an engineering point of view, significant improvements over conventional methods are given by the introduction of simultaneous and proportional myoelectric control using linear regression techniques [13, 14], which create a continuous map between EMG signals and the intended movements or pattern recognition algorithms, mostly based on the information of muscles groups. The translation of neuroscientific theories (i.e. postural synergies) in software and hardware architecture of artificial devices is opening new approaches for the design and control of upper-limb prostheses
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