Design and Assessment of Control Maps for Multi-Channel sEMG-Driven Prostheses and Supernumerary Limbs

Abstract

Proportional and simultaneous control algorithms are considered as one of the most effective ways of mapping electromyographic signals to an artificial device. However, the applicability of these methods is limited by the high number of electromyographic features that they require to operate—typically twice as many the actuators to be controlled. Indeed, extracting many independent electromyographic signals is challenging for a number of reasons—ranging from technological to anatomical. On the contrary, the number of actively moving parts in classic prostheses or extra-limbs is often high. This paper faces this issue, by proposing and experimentally assessing a set of algorithms which are capable of proportionally and simultaneously control as many actuators as there are independent electromyographic signals available. Two sets of solutions are considered. The first uses as input electromyographic signals only, while the second adds postural measurements to the sources of information. At first, all the proposed algorithms are experimentally tested in terms of precision, efficiency, and usability on twelve able-bodied subjects, in a virtual environment. A state-of-the-art controller using twice the amount of electromyographic signals as input is adopted as benchmark. We then performed qualitative tests, where the maps are used to control a prototype of upper limb prosthesis. The device is composed of a robotic hand and a wrist implementing active prono-supination movement. Eight able-bodied subjects participated to this second round of testings. Finally, the proposed strategies were tested in exploratory experiments involving two subjects with limb loss. Results coming from the evaluations in virtual and realistic settings show encouraging results and suggest the effectiveness of the proposed approach.

Review of Supernumerary Robotic Limbs

“…Because there is no muscle spindle in the auricle, it is impossible to determine the degree of muscle contraction at a specific time, so a head-mounted display device is used to feed back the contact information of the sixth fingertip. Maimeri et al [31] decoded the motion through the pattern recognition algorithm basing on the collected EMG signals and map it to the SRLs motion space to realize the motion control of SRL. Brain-computer interface control is a method to realize robot control by using human brain wave signals.…”

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

“…Indeed, through the physical introduction of hand coordinated movements, it is possible to considerably reduce the architectural complexity in terms of actuators and sensors, and consequently increase the device robustness and reliability. Preliminary investigations on the robotic prototype have been done in a previous work [37], where authors explored the possibility to use specific signal maps relying only on two EMG signals. The SH2-P is capable of spanning continuous movements in the synergy bi-dimensional space, through the simultaneous and coordinated activation of the two actuators.…”

Exploring Stiffness Modulation in Prosthetic Hands and Its Perceived Function in Manipulation and Social Interaction

“…This is due to both the clinical condition of the muscles and the technical difficulty of fitting many sensors in a small space. Novel techniques have been proposed to overcome this issue (e.g., Sartori et al, 2013 ; Maimeri et al, 2019 ), where muscle information is integrated within a musculoskeletal model or with postural measures.…”