Abstract
Grasping is a complex task routinely performed in an anticipatory (feedforward) manner, where sensory feedback is responsible for learning and updating the internal model of grasp dynamics. This study aims at evaluating whether providing a proportional tactile force feedback during the myoelectric control of a prosthesis facilitates learning a stable internal model of the prosthesis force control. Ten able-bodied subjects controlled a sensorized myoelectric prosthesis performing four blocks of consecutive grasps at three levels of target force (30, 50, and 70%), repeatedly closing the fully opened hand. In the first and third block, the subjects received tactile and visual feedback, respectively, while during the second and fourth block, the feedback was removed. The subjects also performed an additional block with no feedback 1 day after the training (Retest). The median and interquartile range of the generated forces was computed to assess the accuracy and precision of force control. The results demonstrated that the feedback was indeed an effective instrument for the training of prosthesis control. After the training, the subjects were still able to accurately generate the desired force for the low and medium target (30 and 50% of maximum force available in a prosthesis), despite the feedback being removed within the session and during the retest (low target force). However, the training was substantially less successful for high forces (70% of prosthesis maximum force), where subjects exhibited a substantial loss of accuracy as soon as the feedback was removed. The precision of control decreased with higher forces and it was consistent across conditions, determined by an intrinsic variability of repeated myoelectric grasping. This study demonstrated that the subject could rely on the tactile feedback to adjust the motor command to the prosthesis across trials. The subjects adjusted the mean level of muscle activation (accuracy), whereas the precision could not be modulated as it depends on the intrinsic myoelectric variability. They were also able to maintain the feedforward command even after the feedback was removed, demonstrating thereby a stable learning, but the retention depended on the level of the target force. This is an important insight into the role of feedback as an instrument for learning of anticipatory prosthesis force control.
Highlights
Grasping is a complex action governed by a sophisticated coordination between several body systems (GeorgopoulosExp Brain Res (2017) 235:2547–2559 and Grillner 1989; Marteniuk and Bertram 2001; van der Wel and Rosenbaum 2007)
Somatosensory information plays a fundamental role in the learning, maintenance, and updating of these anticipatory mechanisms (Witney et al 2004; Hermsdorfer et al 2011; Jarrasse et al 2013) and predictive force control requires at least intermittent cutaneous and proprioceptive feedback to signal the effectiveness of descending motor commands and to update internal models (Nowak et al 2004)
The tactile and visual feedback conditions across all the target forces were characterized with a good accuracy
Summary
Grasping is a complex action governed by a sophisticated coordination between several body systems (GeorgopoulosExp Brain Res (2017) 235:2547–2559 and Grillner 1989; Marteniuk and Bertram 2001; van der Wel and Rosenbaum 2007). Previous studies have shown that grip force is tightly coupled to load force and that both grip and load force are modulated in an anticipatory manner as a function of the properties of the object (size, shape, and contact surface) (Johansson and Westling 1988; Johansson and Flanagan 2009; Hermsdorfer et al 2011) To account for these anticipatory mechanisms, the concept of internal forward models that predicts the consequences of our movements has been proposed (Wolpert and Miall 1996; Wolpert et al 2001). Somatosensory information plays a fundamental role in the learning, maintenance, and updating of these anticipatory mechanisms (Witney et al 2004; Hermsdorfer et al 2011; Jarrasse et al 2013) and predictive force control requires at least intermittent cutaneous and proprioceptive feedback to signal the effectiveness of descending motor commands and to update internal models (Nowak et al 2004)
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