Translating Research on Myoelectric Control into Clinics-Are the Performance Assessment Methods Adequate?

Missing an upper limb dramatically impairs daily-life activities. Significant efforts in overcoming the issues arising from this disability have been made in both academia and industry, although their clinical outcome is still limited. Translation of prosthetic research into clinics has been challenging because of the difficulties in meeting the necessary requirements of the market. In this perspective, we focus on myocontrol algorithms for upper limb prostheses and we emphasize that one relevant factor determining the relatively small clinical impact of these methods is the limit of commonly used laboratory performance metrics. The laboratory conditions, in which the majority of the solutions are being evaluated, fail to sufficiently replicate real-life challenges. We qualitatively substantiate this argument with data from seven transradial amputees. Their ability to control a myoelectric prosthesis was tested by measuring the accuracy of offline EMG signal classification, as a typical laboratory performance metrics, as well as by clinical scores when performing standard tests of daily living. Despite all subjects reached relatively high classification accuracy offline, their clinical scores were largely different and were not strongly predicted by classification accuracy. As argued in previous reports, we reinforce the suggestion to test myocontrol systems using clinical tests on amputees, fully fitted with sockets and prostheses highly resembling the systems they would use in daily living, as evaluation benchmark. Agreement on this level of testing for systems developed in research laboratories would facilitate clinically relevant progresses in this field.<br

Ultra-Low-Power Digital Filtering for Insulated EMG Sensing

Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by state-of-the-art electromyography sensors, which use a conductive connection to the skin and are therefore sensitive to sweat. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Here, we present ultra-low-power digital signal processing algorithms for an insulated EMG sensor which couples the EMG signal capacitively. These sensors require neither conductive connection to the skin nor electrolytic paste or skin preparation. Capacitive sensors allow straightforward application. However, they make a sophisticated signal amplification and noise suppression necessary. A low-cost sensor has been developed for real-time myoelectric prostheses control. The major hurdles in measuring the EMG are movement artifacts and external noise. We designed various digital filters to attenuate this noise. Optimal system setup and filter parameters for the trade-off between attenuation of this noise and sufficient EMG signal power for high signal quality were investigated. Additionally, an algorithm for movement artifact suppression, enabling robust application in real-world environments, is presented. The algorithms, which require minimal calculation resources and memory, are implemented on an ultra-low-power microcontroller.(VLID)344808

An Insulated Flexible Sensor for Stable Electromyography Detection : Application to Prosthesis Control

Electromyography (EMG), the measurement of electrical muscle activity, is used in a variety of applications, including myoelectric upper-limb prostheses, which help amputees to regain independence and a higher quality of life. The state-of-the-art sensors in prostheses have a conductive connection to the skin and are therefore sensitive to sweat and require preparation of the skin. They are applied with some pressure to ensure a conductive connection, which may result in pressure marks and can be problematic for patients with circulatory disorders, who constitute a major group of amputees. Due to their insulating layer between skin and sensor area, capacitive sensors are insensitive to the skin condition, they require neither conductive connection to the skin nor electrolytic paste or skin preparation. Here, we describe a highly stable, low-power capacitive EMG measurement set-up that is suitable for real-world application. Various flexible multi-layer sensor set-ups made of copper and insulating foils, flex print and textiles were compared. These flexible sensor set-ups adapt to the anatomy of the human forearm, therefore they provide high wearing comfort and ensure stability against motion artifacts. The influence of the materials used in the sensor set-up on the magnitude of the coupled signal was demonstrated based on both theoretical analysis and measurement.The amplifier circuit was optimized for high signal quality, low power consumption and mobile application. Different shielding and guarding concepts were compared, leading to high SNR.(VLID)344807

Wilcoxon signed-rank test results of the tta for each option between the two methods.

<p>Wilcoxon signed-rank test results of the tta for each option between the two methods.</p

GA performance results for the different object’s position.

<p>1 grasp attempt was the minimum possible value for this measurement. The error bars indicate the standard error.</p

Hardware used to test the prototype.

<p>a) 4 Electrode Pads were placed on the forearm. b) Otto Bock Michelangelo hand was controlled using an Inertial Measurement Unit to detect subject’s motion and an electrocutaneous stimulator to implement the Menu Interface and the Feedback Interface.</p

Mean values (and standard deviation) of the grasp and release performance obtained during experiments with the Menu Interface and the Myocontrol Interface.

<p>*p < 0.05</p><p>Mean values (and standard deviation) of the grasp and release performance obtained during experiments with the Menu Interface and the Myocontrol Interface.</p

Friedman’s ANOVA Post-Hoc results pair-wise comparison between options for each method.

<p>Friedman’s ANOVA Post-Hoc results pair-wise comparison between options for each method.</p

Time to Activate a Grasp Option for each of the methods used in this paper.

<p>The error bars indicate the standard error.</p

Grasp selection performance between the Menu interface modes (M1G4, M2G4) and the Myoelectric interface modes for 4 grasps (G4, G4A, G4AR).

<p>The error bars indicate the standard error.</p