title: Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation.
abstract: Background:For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. "Tacit Learning System" is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user.Methods:We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system.Findings:Reduction in the participants' upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants.Interpretation:Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.
authors.affiliation: Department of Hand Surgery, Nagoya University School of Medicine Nagoya, Japan.
authors.forename: Shintaro
authors.initials: S
authors.lastname: Oyama
authors.affiliation: Brain Science Institute-TOYOTA Collaboration Center, RIKEN Nagoya, Japan.
authors.forename: Shingo
authors.initials: S
authors.lastname: Shimoda
authors.affiliation: Brain Science Institute-TOYOTA Collaboration Center, RIKEN Nagoya, Japan.
authors.forename: Fady S K
authors.initials: FS
authors.lastname: Alnajjar
authors.affiliation: Department of Hand Surgery, Nagoya University School of Medicine Nagoya, Japan.
authors.forename: Katsuyuki
authors.initials: K
authors.lastname: Iwatsuki
authors.affiliation: Brain and Mind Research Center, Nagoya University School of Medicine Nagoya, Japan.
authors.forename: Minoru
authors.initials: M
authors.lastname: Hoshiyama
authors.affiliation: Department of Rehabilitation, Chubu Rosai Hospital Nagoya, Japan.
authors.forename: Hirotaka
authors.initials: H
authors.lastname: Tanaka
authors.affiliation: Department of Hand Surgery, Nagoya University School of Medicine Nagoya, Japan.
authors.forename: Hitoshi
authors.initials: H
authors.lastname: Hirata
keywords: artificial intelligence; biomechanical function reconstruction; interactive musculoskeletal modeling analysis; magnetoencephalography; motor control; muscle; myoelectric prosthesis; sensory synergy