Time filter

Source Type

Atzori M.,University of Applied Sciences and Arts Western Switzerland | Gijsberts A.,Institute Of Recherche Idiap | Heynen S.,University of Applied Sciences and Arts Western Switzerland | Hager A.-G.M.,University of Applied Sciences and Arts Western Switzerland | And 5 more authors.
Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics | Year: 2012

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. © 2012 IEEE. Source

Atzori M.,University of Applied Sciences and Arts Western Switzerland | Gijsberts A.,Institute Of Recherche Idiap | Kuzborskij I.,Institute Of Recherche Idiap | Elsig S.,University of Applied Sciences and Arts Western Switzerland | And 5 more authors.
IEEE Transactions on Neural Systems and Rehabilitation Engineering | Year: 2015

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. © 2014 IEEE. Source

Luthi F.,Clinique Romande de Readaptation SUVACare | Luthi F.,Institute Of Recherche En Readaptation | Stiefel F.,University of Lausanne | Gobelet C.,Institute Of Recherche En Readaptation | And 3 more authors.
Disability and Rehabilitation | Year: 2011

Purpose.Bio-psychosocial characteristics of patients after orthopaedic traumas may be a strong predictor of poor outcome. The objective of this prospective study was to assess whether the INTERMED, a measure of bio-psychosocial complexity, identifies complex inpatients during rehabilitation including vocational aspects with a poor outcome 1 year after discharge. Method.At entry, the INTERMED scores of 118 inpatients were used to assign patients to the high or low complexity group. A questionnaire evaluated 1 year after discharge whether patients: (1) returned to work, (2) still have therapies, (3) take psychoactive drugs, (4) take medication against pain and (5) were satisfied with vocational therapy. Univariate logistic regressions identified which variables predict INTERMED case complexity during hospitalisation as well as predictors (i.e. INTERMED case complexity, French as preferred language, duration of the disability, accident at work, work qualification, severity of the injury, psychiatric co-morbidities, pain) of the five measured outcomes 1 year after discharge. Results.During hospitalisation, the high complexity group was associated with a high prevalence of psychiatric co-morbidities, a higher level of pain and a weaker perception of treatment effects. One year after discharge, the INTERMED was the sole variable to predict all outcomes. Conclusion.The INTERMED identifies complex patients during vocational rehabilitation after orthopaedic trauma and is a good predictor of poor outcome 1 year after discharge. © 2011 Informa UK, Ltd. Source

Terrier P.,Institute Of Recherche En Readaptation | Deriaz O.,Institute Of Recherche En Readaptation
Journal of NeuroEngineering and Rehabilitation | Year: 2011

Background: Motorized treadmills are widely used in research or in clinical therapy. Small kinematics, kinetics and energetics changes induced by Treadmill Walking (TW) as compared to Overground Walking (OW) have been reported in literature. The purpose of the present study was to characterize the differences between OW and TW in terms of stride-to-stride variability. Classical (Standard Deviation, SD) and non-linear (fractal dynamics, local dynamic stability) methods were used. In addition, the correlations between the different variability indexes were analyzed. Methods. Twenty healthy subjects performed 10 min TW and OW in a random sequence. A triaxial accelerometer recorded trunk accelerations. Kinematic variability was computed as the average SD (MeanSD) of acceleration patterns among standardized strides. Fractal dynamics (scaling exponent ) was assessed by Detrended Fluctuation Analysis (DFA) of stride intervals. Short-term and long-term dynamic stability were estimated by computing the maximal Lyapunov exponents of acceleration signals. Results: TW did not modify kinematic gait variability as compared to OW (multivariate T 2, p = 0.87). Conversely, TW significantly modified fractal dynamics (t-test, p = 0.01), and both short and long term local dynamic stability (T 2 p = 0.0002). No relationship was observed between variability indexes with the exception of significant negative correlation between MeanSD and dynamic stability in TW (3 × 6 canonical correlation, r = 0.94). Conclusions: Treadmill induced a less correlated pattern in the stride intervals and increased gait stability, but did not modify kinematic variability in healthy subjects. This could be due to changes in perceptual information induced by treadmill walking that would affect locomotor control of the gait and hence specifically alter non-linear dependencies among consecutive strides. Consequently, the type of walking (i.e. treadmill or overground) is important to consider in each protocol design. © 2011 Terrier and Dériaz; licensee BioMed Central Ltd. Source

Terrier P.,Institute Of Recherche En Readaptation
PLoS ONE | Year: 2012

While walking, human beings continuously adjust step length (SpL), step time (SpT), step speed (SpS = SpL/SpT) and step width (SpW) by integrating both feedforward and feedback mechanisms. These motor control processes result in correlations of gait parameters between consecutive strides (statistical persistence). Constraining gait with a speed cue (treadmill) and/or a rhythmic auditory cue (metronome), modifies the statistical persistence to anti-persistence. The objective was to analyze whether the combined effect of treadmill and rhythmic auditory cueing (RAC) modified not only statistical persistence, but also fluctuation magnitude (standard deviation, SD), and stationarity of SpL, SpT, SpS and SpW. Twenty healthy subjects performed 6×5 min. walking tests at various imposed speeds on a treadmill instrumented with foot-pressure sensors. Freely-chosen walking cadences were assessed during the first three trials, and then imposed accordingly in the last trials with a metronome. Fluctuation magnitude (SD) of SpT, SpL, SpS and SpW was assessed, as well as NonStationarity Index (NSI), which estimates the dispersion of local means in the times series (SD of 20 local means over 10 steps). No effect of RAC on fluctuation magnitude (SD) was observed. SpW was not modified by RAC, what is likely the evidence that lateral foot placement is separately regulated. Stationarity (NSI) was modified by RAC in the same manner as persistent pattern: Treadmill induced low NSI in the time series of SpS, and high NSI in SpT and SpL. On the contrary, SpT, SpL and SpS exhibited low NSI under RAC condition. We used relatively short sample of consecutive strides (100) as compared to the usual number of strides required to analyze fluctuation dynamics (200 to 1000 strides). Therefore, the responsiveness of stationarity measure (NSI) to cued walking opens the perspective to perform short walking tests that would be adapted to patients with a reduced gait perimeter. © 2012 Philippe Terrier. Source

Discover hidden collaborations