Transcutaneous Magnet Localizer for a Self-Contained Myokinetic Prosthetic Hand

Abstract

The search for a physiologically appropriate interface for the control of dexterous hand prostheses is an ongoing challenge in bioengineering. In this context, we proposed an interface, named myokinetic control interface, based on the localization of magnets implanted in the residual limb muscles, to monitor their contractions and send appropriate commands to the artificial hand. As part of such concept, this interface requires a transcutaneous magnet localizer that can be integrated in a self-contained limb prosthesis, a feature yet to be realized within the current state of the art. Methods: In an attempt to cover this gap, here we present a modular embedded system consisting of a computation unit able to acquire synchronized samples captured by up to eight acquisition units, so to localize multiple magnets. Results: The system exhibits short computation times (<60ms) and power consumption (0.6-1.2W) which are suitable for use in a clinically viable prosthetic arm. The system proved able to localize magnets while moving at speeds in the range of physiological movements (<0.24m/s), with high accuracy (<1mm) and precision (<0.5mm). Conclusion: We demonstrated a system suitable for the implementation of a self-contained myokinetic prosthetic hand. Significance: These results pave the way towards the clinical implementation of the myokinetic interface, with amputees controlling an artificial arm by means of implanted magnets.

Generating Frequency Selective Vibrations in Remote Moving Magnets

“…This finding was possibly related to the slowly moving and gradual motion imposed to the magnets (1.5 cm s −1 ). Indeed, previous studies [ 34,45 ] had already characterized the effect of magnets speed in tracking accuracy, precision, and computation time, demonstrating no difference at slow magnets speed. Although, those studies were conducted in absence of the coil disturbing MFs, we speculate that such a behavior could be representative also in this scenario, including a monotonically increasing error once a certain speed threshold is crossed.…”

“…The medians (, ) and inter‐quartile range, IQR (, ), of and were used to quantify the effects of the MF on the tracking accuracy and precision, respectively, akin to our previous studies. [ 32,45 ]…”

“…The system comprised: 1) a personal computer (PC), 2) 12 cylindrical coils arranged around a cylindrical workspace (coil‐array, sized as a 99th percentile human‐forearm), supplied by commercial current drivers (MC5004, Faulhaber Minimotor SA, Switzerland), 3) the remote magnets (up to 4), and 4) four MF sensor boards mounting a grid of 20 3‐axis magnetometers (LIS3MLD, STMicroelectronics, Switzerland) (Figure 1B). [ 45 ]…”

“…The medians (ΔE P , ΔE O ) and inter-quartile range, IQR (ΔS P , ΔS O ), of Δe P and Δe O were used to quantify the effects of the MF on the tracking accuracy and precision, respectively, akin to our previous studies. [32,45] Test B2 -Multiple Static Magnets: The effects of the actuation MFs on the tracking performance of two magnets (M 1 and M 2 ) were assessed while varying the relative distance between the magnets. One magnet was vibrated at 90 Hz, under torque control (40 and 80 μNm peak-to-peak amplitude), about the two axes perpendicular to the magnetization vectors, while the other one was kept still (Figure 6).…”

“…The system comprised: 1) a personal computer (PC), 2) 12 cylindrical coils arranged around a cylindrical workspace (coil-array, sized as a 99th percentile human-forearm), supplied by commercial current drivers (MC5004, Faulhaber Minimotor SA, Switzerland), 3) the remote magnets (up to 4), and 4) four MF sensor boards mounting a grid of 20 3-axis magnetometers (LIS3MLD, STMicroelectronics, Switzerland) (Figure 1B). [45] The PC runs a RT Cþþ application (Ubuntu Linux with RT-PREEMPT patch) that implements the control algorithm through a three-step iterative procedure: 1) acquires and filters the MF collected by the sensor boards, 2) implements the tracking algorithm to retrieve the poses of the slowly moving magnets, and 3) uses this information to update a model-based controller (Figure 2). Specifically, the sensor boards sample and send to the PC (360 Hz rate) the MF which is generated by the magnets and coils acting in the workspace.…”