Exploring augmented grasping capabilities in a multi-synergistic soft bionic hand

• Published in: Journal of neuroengineering and rehabilitation Vol. 17; no. 1; pp. 116 - 16
• Main Authors: Piazza, Cristina, Simon, Ann M., Turner, Kristi L., Miller, Laura A., Catalano, Manuel G., Bicchi, Antonio, Hargrove, Levi J.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 25.08.2020; Springer Nature B.V; BMC
• Subjects: Adaptive synergies; Adult; Amputees; Anthropomorphism; Architecture; Artificial Limbs; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Bionic hand; Bionics; Control algorithms; Control methods; Design; Dextrous hands; Electromyography - methods; Female; Grasping; Hand; Hand Strength; Hands; Healthy Volunteers; Humans; Investigations; Male; Myoelectric control; Myoelectricity; Neurology; Neurosciences; Pattern recognition; Posture; Principal components analysis; Prostheses; Prosthesis Design - methods; Prosthetics; Rehabilitation Medicine; Robotics; Robotics - instrumentation; Robust control; Robustness; Sensors; Signal processing; Soft robotics; Software; Young Adult; Myoelectric control; Soft robotics; Adaptive synergies; Bionic hand
• ISSN: 1743-0003; 1743-0003
• DOI: 10.1186/s12984-020-00741-y

Background State-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. Methods Following 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. Results The 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. Conclusions Encouraging results suggest how this approach could be a viable way towards the design of more natural, reliable, and intuitive dexterous hands.