Comparison of online algorithms for the tracking of multiple magnetic targets in a myokinetic control interface

Montero, J.; Gherardini, Marta; Clemente, Francesco; Cipriani, Christian

doi:10.1109/icra40945.2020.9196804

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Moreover, it was shown that the viability of such an approach depends on several factors, including the number and arrangement of the MMs [15], [19], [20], the volume of the workspace w.r.t. the strength of the magnetic field produced by the MMs [21], the approximations used in the point dipole model [17], [22], the cost-function and the numerical solver used [18], [23], and several other environmental factors [13]. Nonetheless, most of these findings were confined to single-objective tracking.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Magnetic tracking systems have been widely investigated in biomedical engineering due to the transparency of the human body to static magnetic fields. We recently proposed a novel human-machine interface for prosthetic application, namely the myokinetic interface. This controls multi-articulated prostheses by tracking magnets implanted in the residual muscles of individuals with amputation. Previous studies in this area focused solely on the choice and tuning of the localization algorithm. Here, we addressed the role of the intrinsic properties of the sensors, by analysing their effects on the tracking accuracy and on the computation time of the localization algorithm, through experimentally-verified computer simulations. We observed that the tracking accuracy is primarily affected by the localization rate, which is directly related to the sampling frequency of the sensors, and less significantly affected by the sensor resolution. The computation time, instead, proved positively correlated to the number of MMs, and negatively correlated with the localization rate. Our results may contribute to the development of novel human-machine interfaces for prosthetic limbs and could be extended to a broad range of applications involving magnetic tracking.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Clinical implementation of a bionic hand controlled with kineticomyographic signals

Moradi

Rafiei

Daliri

et al. 2022

Sci Rep

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Sensing the proper signal could be a vital piece of the solution to the much evading attributes of prosthetic hands, such as robustness to noise, ease of connectivity, and intuitive movement. Towards this end, magnetics tags have been recently suggested as an alternative sensing mechanism to the more common EMG signals. Such sensing technology, however, is inherently invasive and hence only in simulation stages of magnet localization to date. Here, for the first time, we report on the clinical implementation of implanted magnetic tags for an amputee's prosthetic hand from both the medical and engineering perspectives. Specifically, the proposed approach introduces a flexor–extensor tendon transfer surgical procedure to implant the tags, artificial neural networks to extract human intention directly from the implanted magnet's magnetic fields -in short KineticoMyoGraphy (KMG) signals- rather than localizing them, and a game strategy to examine the proposed algorithms and rehabilitate the patient with his new prosthetic hand. The bionic hand's ability is then tested following the patient's intended gesture type and grade. The statistical results confirm the possible utility of surgically implanted magnetic tags as an accurate sensing interface for recognizing the intended gesture and degree of movement between an amputee and his bionic hand.

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Graph Convolutional Network based Configuration Detection for Freeform Modular Robot Using Magnetic Sensor Array

Liang

Lam

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Comparison of online algorithms for the tracking of multiple magnetic targets in a myokinetic control interface

Cited by 5 publications

References 23 publications

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

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

Clinical implementation of a bionic hand controlled with kineticomyographic signals

Graph Convolutional Network based Configuration Detection for Freeform Modular Robot Using Magnetic Sensor Array

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