Abstract
New upper limb prostheses controllers are continuously being proposed in the literature. However, most of the prostheses commonly used in the real world are based on very old basic controllers. One reason to explain this reluctance to change is the lack of robustness. Traditional controllers have been validated by many users and years, so the introduction of a new controller paradigm requires a lot of strong evidence of a robust behavior. In this work, we approach the robustness against donning/doffing and arm position for recently proposed linear filter adaptive controllers based on myoelectric signals. The adaptive approach allows to introduce some feedback in a natural way in real time in the human-machine collaboration, so it is not so sensitive to input signals changes due to donning/doffing and arm movements. The average completion rate and path efficiency obtained for eight able-bodied subjects donning/doffing five times in four days is 95.83% and 84.19%, respectively, and for four participants using different arm positions is 93.84% and 88.77%, with no statistically significant difference in the results obtained for the different conditions. All these characteristics make the adaptive linear regression a potential candidate for future real world prostheses controllers.
Highlights
In spite of the continuous improvement in the amputation rehabilitation and prosthetic restoration [1], living without a limb limits the daily life activities leading to a lower quality of life
Three arm positions were adopted to analyze the effects of arm postures variation: straight arm pointing aligned with the torso (P1), elbow flexed 90 degrees (P2) and straight arm at a 90 degree angle to the torso (P3)
In order to show a visual interpretation of some trajectory patterns examples that determine the global path efficiency (PE) values, Figure 7a shows the paths traveled by two users: participant #1 and participant #5 for targets 1 to 5
Summary
In spite of the continuous improvement in the amputation rehabilitation and prosthetic restoration [1], living without a limb limits the daily life activities leading to a lower quality of life. There have been developments in all processes of the control loop: new features and feature extraction methods [18,19], new algorithms based on classification [20,21,22] and regression models [23,24], new feedback procedures [25,26,27,28,29,30,31] and new prostheses technology [32] Most of these improvements have never been implemented in real life devices. The most common problems are: limitations of EMG signal acquisition process [34,35,36,37], arm positioning [38,39], electrode shifting [39,40,41], skin conditions [25], fatigue [42] or time degradation [43] These factors affected the reliability of modern prosthesis control methods over time and conditions of use. These capabilities are essential for a comfortable real prosthesis control and open the possibility that the proposed method could be implemented in the future in a test stage with real patients in a clinically supervised experiment
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