Partially supervised exercise programmes for fall prevention improve physical performance of older people at risk of falling: a three-armed multi-centre randomised controlled trial.

BACKGROUND: Falls have a major impact on individual patients, their relatives, the healthcare system and related costs. Physical exercise programmes that include multiple categories of exercise effectively reduce the rate of falls and risk of falling among older adults. METHODS: This 12-month, assessor-blinded, three-armed multicentre randomised clinical trial was conducted in adults aged ≥ 65 years identified as at risk of falling. Four hundred and five participants were randomly allocated into 3 groups: experimental group (n = 166) with the Test&Exercise partially supervised programme based on empowerment delivered with a tablet, illustrated manual and cards, reference group (n = 158) with the Otago partially supervised programme prescribed by a physiotherapist delivered with an illustrated manual and control group (n = 81) with the Helsana self-administrated programme delivered with cards. Experimental and reference groups received partially supervised programmes with 8 home sessions over 6 months. Control group received a self-administered program with a unique home session. The 3 groups were requested to train independently 3 times a week for 12 months. Primary outcome was the incidence rate ratio of self-reported falls over 12 months. Secondary outcomes were fear of falling, basic functional mobility and balance, quality of life, and exercise adherence. RESULTS: A total of 141 falls occurred in the experimental group, 199 in the reference group, and 42 in the control group. Incidence rate ratios were 0.74 (95% CI 0.49 to 1.12) for the experimental group and 0.43 (95% CI 0.25 to 0.75) for the control group compared with the reference group. The Short Physical Performance Battery scores improved significantly in the experimental group (95% CI 0.05 to 0.86; P = 0.027) and in the reference group (95% CI 0.06 to 0.86; P = 0.024) compared with the control group. CONCLUSION: The self-administered home-based exercise programme showed the lowest fall incidence rate, but also the highest dropout rate of participants at high risk of falling. Both partially supervised programmes resulted in statistically significant improvements in physical performance compared with the self-administered programme. TRIAL REGISTRATION: NCT02926105. CLINICALTRIALS: gov. Date of registration: 06/10/2016.

Experiences in the creation of an electromyography database to help hand amputated persons.

Currently, trans-radial amputees can only perform a few simple movements with prosthetic hands. This is mainly due to low control capabilities and the long training time that is required to learn controlling them with surface electromyography (sEMG). This is in contrast with recent advances in mechatronics, thanks to which mechanical hands have multiple degrees of freedom and in some cases force control. To help improve the situation, we are building the NinaPro (Non-Invasive Adaptive Prosthetics) database, a database of about 50 hand and wrist movements recorded from several healthy and currently very few amputated persons that will help the community to test and improve sEMG-based natural control systems for prosthetic hands. In this paper we describe the experimental experiences and practical aspects related to the data acquisition.

Publisher Correction: Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

Megane Pro: Myo-electricity, visual and gaze tracking data acquisitions to improve hand prosthetics.

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.

Effect of clinical parameters on the control of myoelectric robotic prosthetic hands.

Improving the functionality of prosthetic hands with noninvasive techniques is still a challenge. Surface electromyography (sEMG) currently gives limited control capabilities; however, the application of machine learning to the analysis of sEMG signals is promising and has recently been applied in practice, but many questions still remain. In this study, we recorded the sEMG activity of the forearm of 11 male subjects with transradial amputation who were mentally performing 40 hand and wrist movements. The classification performance and the number of independent movements (defined as the subset of movements that could be distinguished with >90% accuracy) were studied in relationship to clinical parameters related to the amputation. The analysis showed that classification accuracy and the number of independent movements increased significantly with phantom limb sensation intensity, remaining forearm percentage, and temporal distance to the amputation. The classification results suggest the possibility of naturally controlling up to 11 movements of a robotic prosthetic hand with almost no training. Knowledge of the relationship between classification accuracy and clinical parameters adds new information regarding the nature of phantom limb pain as well as other clinical parameters, and it can lay the foundations for future "functional amputation" procedures in surgery.

Effects of prosthesis use on the capability to control myoelectric robotic prosthetic hands.

The natural control of robotic prosthetic hands with non-invasive techniques is still a challenge: myoelectric prostheses currently give some control capabilities; the application of pattern recognition techniques is promising and recently started to be applied in practice but still many questions are open in the field. In particular, the effects of clinical factors on movement classification accuracy and the capability to control myoelectric prosthetic hands are analyzed in very few studies. The effect of regularly using prostheses on movement classification accuracy has been previously studied, showing differences between users of myoelectric and cosmetic prostheses. In this paper we compare users of myoelectric and body-powered prostheses and intact subjects. 36 machine-learning methods are applied on 6 amputees and 40 intact subjects performing 40 movements. Then, statistical analyses are performed in order to highlight significant differences between the groups of subjects. The statistical analyses do not show significant differences between the two groups of amputees, while significant differences are obtained between amputees and intact subjects. These results constitute new information in the field and suggest new interpretations to previous hypotheses, thus adding precious information towards natural control of robotic prosthetic hands.

Characterization of a benchmark database for myoelectric movement classification.

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.

Electromyography data for non-invasive naturally-controlled robotic hand prostheses.

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.