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.

Visual attention is often predictive for future actions in humans. In manipulation tasks, the eyes tend to fixate an object of interest even before the reach-to-grasp is initiated. Some recent studies have proposed to exploit this anticipatory gaze behavior to improve the control of dexterous upper limb prostheses. This requires a detailed understanding of visuomotor coordination to determine in which temporal window gaze may provide helpful information. In this paper, we verify and quantify the gaze and motor behavior of 14 transradial amputees who were asked to grasp and manipulate common household objects with their missing limb. For comparison, we also include data from 30 able-bodied subjects who executed the same protocol with their right arm. The dataset contains gaze, first person video, angular velocities of the head, and electromyography and accelerometry of the forearm. To analyze the large amount of video, we developed a procedure based on recent deep learning methods to automatically detect and segment all objects of interest. This allowed us to accurately determine the pixel distances between the gaze point, the target object, and the limb in each individual frame. Our analysis shows a clear coordination between the eyes and the limb in the reach-to-grasp phase, confirming that both intact and amputated subjects precede the grasp with their eyes by more than 500 ms. Furthermore, we note that the gaze behavior of amputees was remarkably similar to that of the able-bodied control group, despite their inability to physically manipulate the objects.

The instability of myoelectric signals over time complicates their use to control highly articulated prostheses. To address this problem, studies have tried to combine surface electromyography with modalities that are less affected by the amputation and environment, such as accelerometry or gaze information. In the latter case, the hypothesis is that a subject looks at the object he or she intends to manipulate and that knowing this object's affordances allows to constrain the set of possible grasps. In this paper, we develop an automated way to detect stable fixations and show that gaze information is indeed helpful in predicting hand movements. In our multimodal approach, we automatically detect stable gazes and segment an object of interest around the subject's fixation in the visual frame. The patch extracted around this object is subsequently fed through an off-the-shelf deep convolutional neural network to obtain a high level feature representation, which is then combined with traditional surface electromyography in the classification stage. Tests have been performed on a dataset acquired from five intact subjects who performed ten types of grasps on various objects as well as in a functional setting. They show that the addition of gaze information increases the classification accuracy considerably. Further analysis demonstrates that this improvement is consistent for all grasps and concentrated during the movement onset and offset.

Abstract-A number of studies have proposed to use domain adaptation to reduce the training efforts needed to control an upper-limb prosthesis exploiting pre-trained models from prior subjects. 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 equally 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.

During the past 60 years scientific research proposed many techniques to control robotic hand prostheses with surface electromyography (sEMG). Few of them have been implemented in commercial systems also due to limited robustness that may be improved with multimodal data. This paper presents the first acquisition setup, acquisition protocol and dataset including sEMG, eye tracking and computer vision to study robotic hand control. A data analysis on healthy controls gives a first idea of the capabilities and constraints of the acquisition procedure that will be applied to amputees in a next step. Different data sources are not fused together in the analysis. Nevertheless, the results support the use of the proposed multimodal data acquisition approach for prosthesis control. The sEMG movement classification results confirm that it is possible to classify several grasps with sEMG alone. sEMG can detect the grasp type and also small differences in the grasped object (accuracy: 95%). The simultaneous recording of eye tracking and scene camera data shows that these sensors allow performing object detection for grasp selection and that several neurocognitive parameters need to be taken into account for this. In conclusion, this work on intact subjects presents an innovative acquisition setup and protocol. The first results in terms of data analysis are promising and set the basis for future work on amputees, aiming to improve the robustness of prostheses with multimodal data.

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 which 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.

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