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.

ObjectiveTo investigate the effectiveness of conservative interventions for pain, function and range of motion in adults with shoulder impingement.DesignSystematic review and meta-analysis of randomised trials.Data sourcesMedline, CENTRAL, CINAHL, Embase and PEDro were searched from inception to January 2017.Study selection criteriaRandomised controlled trials including participants with shoulder impingement and evaluating at least one conservative intervention against sham or other treatments.ResultsFor pain, exercise was superior to non-exercise control interventions (standardised mean difference (SMD) −0.94, 95% CI −1.69 to −0.19). Specific exercises were superior to generic exercises (SMD −0.65, 95% CI −0.99 to −0.32). Corticosteroid injections were superior to no treatment (SMD −0.65, 95% CI −1.04 to −0.26), and ultrasound guided injections were superior to non-guided injections (SMD −0.51, 95% CI −0.89 to −0.13). Nonsteroidal anti-inflammatory drugs (NSAIDS) had a small to moderate SMD of −0.29 (95% CI −0.53 to −0.05) compared with placebo. Manual therapy was superior to placebo (SMD −0.35, 95% CI −0.69 to −0.01). When combined with exercise, manual therapy was superior to exercise alone, but only at the shortest follow-up (SMD −0.32, 95% CI −0.62 to −0.01). Laser was superior to sham laser (SMD −0.88, 95% CI −1.48 to −0.27). Extracorporeal shockwave therapy (ECSWT) was superior to sham (−0.39, 95% CI −0.78 to –0.01) and tape was superior to sham (−0.64, 95% CI −1.16 to −0.12), with small to moderate SMDs.ConclusionAlthough there was only very low quality evidence, exercise should be considered for patients with shoulder impingement symptoms and tape, ECSWT, laser or manual therapy might be added. NSAIDS and corticosteroids are superior to placebo, but it is unclear how these treatments compare to exercise.

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.

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