Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible.

In this paper, we characterize the Ninapro database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research on advanced myoelectric hand prostheses. The database is obtained by jointly recording surface electromyography signals from the forearm and kinematics of the hand and wrist while subjects perform a predefined set of actions and postures. Besides describing the acquisition protocol, overall features of the datasets and the processing procedures in detail, we present benchmark classification results using a variety of feature representations and classifiers. Our comparison shows that simple feature representations such as mean absolute value and waveform length can achieve similar performance to the computationally more demanding marginal discrete wavelet transform. With respect to classification methods, the nonlinear support vector machine was found to be the only method consistently achieving high performance regardless of the type of feature representation. Furthermore, statistical analysis of these results shows that classification accuracy is negatively correlated with the subject's Body Mass Index. The analysis and the results described in this paper aim to be a strong baseline for the Ninapro database. Thanks to the Ninapro database (and the characterization described in this paper), the scientific community has the opportunity to converge to a common position on hand movement recognition by surface electromyography, a field capable to strongly affect hand prosthesis capabilities.

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, a direct and real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were obtained from ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature sensors. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.

This paper is about (self-powered) advanced hand prosthetics and their control via surface electromyography (sEMG). We hereby introduce to the biorobotics community the first version of the Ninapro database, containing kinematic and sEMG data from the upper limbs of 27 intact subjects while performing 52 finger, hand and wrist movements of interest. The setup and experimental protocol are distilled from existing literature and thoroughly described; the data are then analysed and the results are discussed. In particular, it is clear that standard analysis techniques are no longer enough when so many subjects and movements are taken into account. The database is publicly available to download in standard ASCII format. The database is an ongoing work lasting several years, which is planned to contain data from more than 100 intact subjects and 50 trans-radial amputees; characteristics of the amputations, phantom limbs and prosthesis usage will be stored. We therefore hope that it will constitute a standard, widely accepted benchmark for all novel myoelectric hand prosthesis control methods, as well as a fundamental tool to deliver insight into the needs of trans-radial amputees.