The Myokinetic Control Interface: How Many Magnets Can be Implanted in an Amputated Forearm? Evidence From a Simulated Environment

Abstract

We recently introduced the concept of a new human-machine interface (the myokinetic control interface) to control hand prostheses. The interface tracks muscle contractions via permanent magnets implanted in the muscles and magnetic field sensors hosted in the prosthetic socket. Previously we showed the feasibility of localizing several magnets in nonrealistic workspaces. Here, aided by a 3D CAD model of the forearm, we computed the localization accuracy simulated for three different below-elbow amputation levels, following general guidelines identified in early work. To this aim we first identified the number of magnets that could fit and be tracked in a proximal (T1), middle (T2) and distal (T3) representative amputation, starting from 18, 20 and 23 eligible muscles, respectively. Then we ran a localization algorithm to estimate the poses of the magnets based on the sensor readings. A sensor selection strategy (from an initial grid of 840 sensors) was also implemented to optimize the computational cost of the localization process. Results showed that the localizer was able to accurately track up to 11 (T1), 13 (T2) and 19 (T3) magnetic markers (MMs) with an array of 154, 205 and 260 sensors, respectively. Localization errors lower than 7% the trajectory travelled by the magnets during muscle contraction were always achieved. This work not only answers the question: "how many magnets could be implanted in a forearm and successfully tracked with a the myokinetic control approach?", but also provides interesting insights for a wide range of bioengineering applications exploiting magnetic tracking.

Effects of Sensor Resolution and Localization Rate on the Performance of a Myokinetic Control Interface

“…First, a planar distribution was used for both the sensors and the MMs. This approach was supported by the fact that previous investigations based on the same planar geometry [20] could be effectively translated to a curved, more realistic geometry [36]. Related to this, while the grid of 600 sensors appears technically complex (in terms of wiring, computing units, temporal delays, power consumption, etc.…”

“…In this sense, it represents an important asset of our study, which aimed at identifying general rules, not specific to the hardware choice. Nonetheless, the technical complexity could be significantly reduced without losing performance; as a fact, previous studies suggest that magnetic tracking is more affected by the sensor placement than by its number [26], [36].…”

The myokinetic stimulation interface: activation of proprioceptive neural responses with remotely actuated magnets implanted in rodent forelimb muscle

“…In this implementation, the desired signal shape, frequency, and amplitude could be chosen manually. The localization of the magnet was done once, to calibrate the system (or to identify M) (equation ( 7)) [22][23][24]; during normal operation, the position of the magnet was assumed to be time-invariant.…”

Clinical implementation of a bionic hand controlled with kineticomyographic signals

“…Cipriani and his colleagues performed a series of experiments from 2017 to 2019 on a Myokinetic control interface to study the feasibility of an embedded system 21 , 22 using an experimental mockup. They further identified the desired number of magnetic markers by simulating three levels of trans-radial amputation forearms 23 , 24 . However, these works are tested in electronic hardware only, far from the actual anatomy of an amputee's residual hand.…”