Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know

Domain adaptation methods have been proposed to reduce the training efforts needed to control an upper-limb prosthesis by adapting well performing models from previous subjects to the new subject. These studies generally reported impressive reductions in the required number of training samples to achieve a certain level of accuracy for intact subjects. We further investigate two popular methods in this field to verify whether this result also applies to amputees. Our findings show instead that this improvement can largely be attributed to a suboptimal hyperparameter configuration. When hyperparameters are appropriately tuned, the standard approach that does not exploit prior information performs on par with the more complicated transfer learning algorithms. Additionally, earlier studies erroneously assumed that the number of training samples relates proportionally to the efforts required from the subject. However, a repetition of a movement is the atomic unit for subjects and the total number of repetitions should therefore be used as reliable measure for training efforts. Also when correcting for this mistake, we do not find any performance increase due to the use of prior models

Unsupervised Myocontrol of a Virtual Hand Based on a Coadaptive Abstract Motor Mapping

Applications of simultaneous and proportional control for upper-limb prostheses typically rely on supervised machine learning to map muscle activations to prosthesis movements. This scheme often poses problems for individuals with limb differences, as they may not be able to reliably reproduce the training activations required to construct a natural motor mapping. We propose an unsupervised myocontrol paradigm that eliminates the need for labeled data by mapping the most salient muscle synergies in arbitrary order to a number of predefined prosthesis actions. The paradigm is coadaptive, in the sense that while the user learns to control the system via interaction, the system continually refines the identification of the user’s muscular synergies. Our evaluation consisted of eight subjects without limb-loss performing target achievement control tasks of four actions of the hand and wrist. The subjects achieved comparable performance using the proposed unsupervised myocontrol paradigm and a supervised benchmark method, despite reporting increased mental load with the former

Scylax of Caryanda, Pseudo-Scylax, and the Paris Periplus: Reconsidering the Ancient Tradition of a Geographical Text

The Periplus preserved in the manuscript Parisinus suppl. gr. 443, and erroneously ascribed to Scylax of Caryanda (sixth century BC), is the oldest extant specimen of ancient Greek periplography: it belongs to the second half of the fourth century. In the present article, all the testimonies on the ancient tradition of both Scylax and the Paris Periplus are carefully evaluated. The aim is to determine when and why the Paris Periplus was mistakenly ascribed to Scylax and to clear any doubts on the alleged authorship of this ancient geographic work. The confusion, or the wilful falsification, is evident in Strabo: he knew of Scylax’s voyage in the East and at the same time was acquainted with the text of the Paris Periplus, which he ascribed to this famous ancient seafarer. Greek and Latin authors of the Roman Imperial age knew the Paris Periplus, but many followed slavishly the erroneous ascription to Scylax of Caryanda. When Marcianus of Heraclea in the early Byzantine age collected his corpus of ancient Greek geographers he also ascribed the Paris Periplus to Scylax, thus handing down the error to the copyist of the Paris. suppl. gr. 443

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

A hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person’s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics

Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

A hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person’s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics

Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression

Novel applications in unstructured and non-stationary human environments require robots that learn from experience and adapt autonomously to changing conditions. Predictive models therefore not only need to be accurate, but should also be updated incrementally in real-time and require minimal human intervention. Incremental Sparse Spectrum Gaussian Process Regression is an algorithm that is targeted specifically for use in this context. Rather than developing a novel algorithm from the ground up, the method is based on the thoroughly studied Gaussian Process Regression algorithm, therefore ensuring a solid theoretical foundation. Non-linearity and a bounded update complexity are achieved simultaneously by means of a finite dimensional random feature mapping that approximates a kernel function. As a result, the computational cost for each update remains constant over time. Finally, algorithmic simplicity and support for automated hyperparameter optimization ensures convenience when employed in practice. Empirical validation on a number of synthetic and real-life learning problems confirms that the performance of Incremental Sparse Spectrum Gaussian Process Regression is superior with respect to the popular Locally Weighted Projection Regression, while computational requirements are found to be significantly lower. The method is therefore particularly suited for learning with real-time constraints or when computational resources are limited