Partial hand amputations significantly affect the physical and psychosocial well-being of individuals, yet intuitive control of externally powered prostheses remains an open challenge. To address this gap, we developed a force-controlled prosthetic finger activated by electromyography (EMG) signals. The prototype, constructed around a wrist brace, functions as a supernumerary finger placed near the index, allowing for early-stage evaluation on unimpaired subjects. A neural network-based model was then implemented to estimate fingertip forces from EMG inputs, allowing for online adjustment of the prosthetic finger grip strength. The force estimation model was validated through experiments with ten participants, demonstrating its effectiveness in predicting forces. Additionally, online trials with four users wearing the prosthesis exhibited precise control over the device. Our findings highlight the potential of using EMG-based force estimation to enhance the functionality of prosthetic fingers.

The System-on-Chip revolution has not impacted the field of safety-critical systems as widely as the rest of the electronics market. However, it is nowadays acknowledged that sharing resources between applications of different criticality is a key leverage to reduce costs and improve performance. Certification of systems hosting such mixed-criticality application sets requires sufficient isolation between criticality levels, i.e. a low-critical task must not cause a fault in a high-critical task, especially a temporal fault, which is likely when several co-running tasks compete for a shared resource. This digest describes implementation schemes which can be included, at both hardware and software levels, in mixed-critical systems to enforce such isolation.