The human hand is an incredibly complex system consisting of bones, muscles, nerves, tendons, as well as arteries, veins and other soft tissues. It has a wide spectrum of functionality. Hands are essential not only to manipulate different objects in daily life, but also necessary for social interactions, such communication and the arts. The loss of a hand is a terribly traumatic experience, usually followed by significant psychological and rehabilitation challenges. The interaction between engineering and science has, since a long time, been pointed towards the restauration of the functionality of a lost hand. Currently, promising developments are ongoing worldwide in the field of neuroprosthetics and the replacement of human limbs with robotic devices is advancing considerably. It is now possible to permanently connect a robotic arm to bone, nerves and muscles of a human being. Signals sourced from muscles can be decoded to enable intuitive control of the robotic limb. At the same time, connections in the nerves can be used as a path to transfer information from the external world to the subject’s brain, with the result of allowing amputees to feel again when they touch objects.
Unfortunately, robotic arms as seen in science-fiction are still far from reality. Clinical implementations lag behind the advancements in research, and the conventional solutions for amputees have remained basically unchanged since decades. More efforts are needed to close the gap between research findings within the lab and actual hospital practice. This thesis sets its efforts toward this direction.
This thesis ultimately focuses on the intuitive control of a prosthetic arm. An embedded system capable of prosthetic control by processing of bioelectric signals and pattern recognition algorithms was developed in the first part of this doctoral project. It includes a neurostimulator to provide tactile feedback modulated by sensory information from artificial sensors. The system functionality was proven by its successful use by amputee subjects outside the laboratory in daily life. Said system was then used during the second part of the doctoral project as a research platform to monitor prosthesis usage and training, machine learning based control algorithms, and neural stimulation paradigms for tactile sensory feedback. Within this work, a novel method for interfacing a multi-grip prosthetic hand to facilitate posture selection via pattern recognition was proposed. Moreover, the need for tactile sensory feedback to restore natural grasping behavior in amputees was investigated. In particular, the benefit for motor coordination of somatotopic tactile feedback achieved via direct neural stimulation was demonstrated. The findings and the technology developed during this project open to the clinical use of a new class of prosthetic arms which are directly connected to the neuromusculoskeletal system, intuitively controlled and capable of tactile sensory feedback.