Search

To examine the experience of people with Parkinson's disease when walking in different social situations, and improve understanding of how this affects participation in meaningful activity. A convenience sample of fourteen people with Parkinson's disease and a history of gait dysfunction was recruited. In-depth interviews and direct observations were conducted in the participants' home environments. Specific examples from community mobility were reviewed using first person interviewing techniques with the support of video footage. Interview transcripts were analyzed using an interpretive phenomenological approach to derive key themes. The feeling of 'being looked at' (le regard des autres) was the central theme in participant discourse. This sentiment was inextricably linked to the given norms of the social setting, and the relationships between participants and others within that environment. Participants sought to manage how they were perceived by others through modification of posture/gait patterns; disclosure of their neurological disease; and avoidance/withdrawal from social situations. Further to the functional aspects of mobility, gait is important for maintaining self-image in people with Parkinson's disease. Affective gaze interactions have significant consequences upon participation restriction. These findings underscore the interest of activities which strengthen self-image and validate movement diversity in PD rehabilitation. [ABSTRACT FROM AUTHOR]

Purpose: This study sought to characterize the way patients with Parkinson's disease consciously perceive and respond to their surroundings while walking in everyday situations. Method: A qualitative research program designed around an ecological data collection protocol was employed. A convenience sample of 14 patients with a diagnosis of Parkinson's disease and a history of gait difficulties were recruited. Details regarding patients' subjective experience of walking in everyday environments were obtained using first person interviewing techniques with the support of video footage from their daily-life activity. Interview transcripts were analyzed using an interpretive phenomenological approach in order to derive key themes. Results: The sense of proximity and the way in which an individual perceived themselves with respect to their surroundings appeared central to the way patients organized their locomotor behavior. Further to this, the patient relationship to different features and obstacles appeared conditioned by prior experiences in those circumstances. Patients described managing gait difficulties by consciously regulating their walking trajectory and gaze with respect to their environment. Conclusion: Perceptual challenges, visual flow and the dynamic valence of features in the patient's surroundings may have important effects upon the gait stability of patients with Parkinson's disease and warrant further attention in planning rehabilitation interventions. Walking abilities of patients with Parkinson's disease should be conceptualized in terms of perceptuomotor coupling to a given environment. The functional significance of a patient's environment is dynamic and might be seen to vary in accordance with their physical capacities. Valency, or the subjective relationship between a patient and their surrounds, appears to be an important component of the "fit" between a person and their environment. Novel rehabilitation strategies for the management of parkinsonian gait disturbances might seek to integrate psychological, sensorimotor and environmental elements in order to have individually tailored, ecologically valid home assessment and community rehabilitation programs. [ABSTRACT FROM AUTHOR]

Ground reaction force (GRF) components can be estimated using insole pressure sensors. Principal component analysis in conjunction with machine learning (PCA-ML) methods are widely used for this task. PCA reduces dimensionality and requires pre-normalization. In this paper, we evaluated the impact of twelve pre-normalization methods using three PCA-ML methods on the accuracy of GRF component estimation. Accuracy was assessed using laboratory data from gold-standard force plate measurements. Data were collected from nine subjects during slow- and normal-speed walking activities. We tested the ANN (artificial neural network) and LS (least square) methods while also exploring support vector regression (SVR), a method not previously examined in the literature, to the best of our knowledge. In the context of our work, our results suggest that the same normalization method can produce the worst or the best accuracy results, depending on the ML method. For example, the body weight normalization method yields good results for PCA-ANN but the worst performance for PCA-SVR. For PCA-ANN and PCA-LS, the vector standardization normalization method is recommended. For PCA-SVR, the mean method is recommended. The final message is not to define a normalization method a priori independently of the ML method. [ABSTRACT FROM AUTHOR]

Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure. Several multiscale variants (MPE) have been proposed in order to bring out hidden structures at different time scales. Multiscaling is achieved by combining linear or nonlinear preprocessing with PE calculation. However, the impact of such a preprocessing on the PE values is not fully characterized. In a previous study, we have theoretically decoupled the contribution of specific signal models to the PE values from that induced by the inner correlations of linear preprocessing filters. A variety of linear filters such as the autoregressive moving average (ARMA), Butterworth, and Chebyshev were tested. The current work is an extension to nonlinear preprocessing and especially to data-driven signal decomposition-based MPE. The empirical mode decomposition, variational mode decomposition, singular spectrum analysis-based decomposition and empirical wavelet transform are considered. We identify possible pitfalls in the interpretation of PE values induced by these nonlinear preprocessing, and hence, we contribute to improving the PE interpretation. The simulated dataset of representative processes such as white Gaussian noise, fractional Gaussian processes, ARMA models and synthetic sEMG signals as well as real-life sEMG signals are tested. [ABSTRACT FROM AUTHOR]

Abstract: During a sustained contraction, electromyographic signals (EMGs) undergo a spectral compression. This fatigue behaviour induces a shift of the mean and the median frequencies to lower frequencies. On the other hand, several studies conclude that the mean/median frequency can increase, decrease or remain constant with an increasing force level. Such inconsistency is embarrassing since the fatigue state may be influenced by the force level. In this paper, we propose a frequency indicator which is sensitive to the force level independently of the fatigue state evaluated at 70% of the maximal voluntary contraction. Ten healthy volunteers participated in the study and both surface EMGs (from the short head of the biceps brachii) and force signals were measured. This study compared force and fatigue effects on the EMGs during short (3-s) isometric contractions at different strength intensities and during a sustained isometric contraction until exhaustion. The EMGs partly show 1/f α spectral behaviours since their power spectral densities may experimentally fit with two linear segments in a log–log representation. The measured “right” slope produces variations of force as 20 times the variations of fatigue. 1/f α Behaviour may be related to stochastic fractals. This fractal indicator is a new frequency indicator that is thus complementary to other known classical frequency indicators when studying force during unknown fatigue states. [Copyright &y& Elsevier]

This paper introduces a new approach for electromyography (EMG) activity monitoring based on an improved version of the adaptive linear energy detector (ALED), a widely used technique in voice activity detection. More precisely, we propose a modified ALED technique (named M-ALED) to improve the method's robustness with respect to noise. To achieve this objective, M-ALED relies on the Teager-Kaiser operator for signal pre-conditioning to increase the SNR and uses the order statistics to gain robustness against the signal's impulsiveness. We propose again to exploit the order statistics for the initial signal baseline estimation to deal with the cases where such information is unavailable. Finally, since M-ALED detects the signal's activity at the frame level, we propose in a second stage to refine this detection (at the sample level) by using a constant false alarm rate (CFAR) approach leading to the fine M-ALED (FM-ALED) solution. The performance of FM-ALED is assessed via real and synthetic EMG signal recordings and the obtained results highlight its effectiveness as compared with the state-of-the-art methods (it reduces the mean error probability by a factor close to 2). [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of blind source separation (BSS) through the lens of tensor decomposition (TD). Two fundamental connections between TD and BSS are established, forming the basis for two novel tensor-based BSS methods, namely TenSOFO and TCBSS. The former is designed for a joint individual differences in scaling (INDSCAL) decomposition, addressing instantaneous (linear) BSS tasks; while the latter efficiently performs a constrained block term decomposition (BTD), aligning with the design of convolutive BSS. Leveraging the benefits of the alternating direction method of multipliers and the strengths of tensor representations, both TenSOFO and TCBSS prove to be effective in BSS. Our experimental results demonstrate the effectiveness of these two proposed methods in addressing both TD and BSS tasks, particularly when compared to state-of-the-art algorithms. • Blind source separation (BSS) is addressed through the lens of tensor decomposition. • Two connections between BSS models and tensor decompositions are established. • The proposed TenSOFO method integrates both second-order and fourth-order statistics. • The proposed TCBSS method is designed for convolutive BSS tasks. • We demonstrate their effectiveness on both synthetic and real data. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyses motorcycle educational content in a number of French motorcycle schools on the basis of a naturalistic study of riders’ and trainers’ behaviour. The aim is to specify the situations delivered in motorcycle schools and to study the rider's activity in these situations. The methodology includes ethnographic observation within the motorcycle schools and the longitudinal monitoring of 14 trainee motorcyclists during their initial training. The training situations were described by the combination of audio–visual recordings and interviews data (i.e. concomitant or interruptive verbalization, and self-confrontation data). The results permit to (1) compare the “real” and “official” durations of track and on-road training, (2) characterize the real training situations, (3) describe the preferred forms of instruction, and (4) conduct an in-depth analysis of the situations used during training in traffic. The discussion show, in first, the poverty of the training situations which are based on the repetition of the exercises in the test, and, in second, disparities between the riding situations encountered during training and the demands made by riding in natural traffic. The usefulness and the applications of this type of approach – based on the integration of the rider's point of view notably by self-confrontation interview – for understanding real riding behaviours and how such approaches could supplement vehicle-based data are discussed in a large conclusion. [Copyright &y& Elsevier]

Purpose: The aim of this study was to compare the muscle oxygenation between trained and untrained subjects during heavy exercise until exhaustion at two extreme pedaling cadences using a NIRS system.Methods: Nine untrained male subjects and nine male competitive triathletes cycled until exhaustion at an intensity corresponding to 90 % of the power output achieved at peak oxygen uptake at 40 and 100 rpm. Gas exchanges were measured breath-by-breath during each exercise. Muscle (de)oxygenation was monitored continuously by near-infrared spectroscopy on the Vastus Lateralis.Results: Muscle deoxygenation (∆deoxy[Hb + Mb], i.e., O2 extraction) and ∆total[Hb + Mb] were significantly higher at 40 rpm compared to 100 rpm during the exercise in untrained subjects but not in triathletes (p < 0.05). The time performed until exhaustion was significantly higher at 40 than at 100 rpm in untrained subjects (373 ± 55 vs. 234 ± 37 s, respectively) but not in triathletes (339 ± 69 vs. 325 ± 66 s).Conclusions: These results indicate that high aerobic fitness (1) allows for better regulation between [Formula: see text]O2M and VO2M following the change in pedaling cadence, and (2) is the most important factor in the relationship between pedaling cadence and performance. [ABSTRACT FROM AUTHOR]

In surface electromyography (sEMG), the assessment of the muscle fiber conduction velocity (CV) can be performed by estimating the time delay between sEMG signals recorded by means of electrodes. During the dynamic contractions that often occur in daily life, the sEMG signals are not stationary which, induces time-varying delays (TVD). In the present study, the problem of TVD estimation is considered using a new parametric method. First, we propose a polynomial modeling of the TVD. The model is expressed on an orthonormal polynomial basis. Then, the TVD model parameters are estimated by using a maximum likelihood (ML) strategy solved by a stochastic optimization technique, called simulated annealing (SA). The orthogonality property allows parameter decoupling, which helps enhance the estimation accuracy. The SA algorithm helps escape local minima, thereby keeping the optimality property benefit of the ML estimator. We also derive two appropriate Cramer–Rao lower bounds (CRB) for the estimated TVD model parameters and for the TVD waveforms. Simulation results show that the estimation of both the model parameters and the TVD function is unbiased and that the mean square error (MSE) obtained is close to the derived CRBs. A comparison with nonparametric approaches of TVD estimation is also presented and shows the superiority of the method proposed. [ABSTRACT FROM AUTHOR]

Surface electromyography (sEMG) is a valuable technique that helps provide functional and structural information about the electric activity of muscles. As sEMG measures output of complex living systems characterized by multiscale and nonlinear behaviors, Multiscale Permutation Entropy (MPE) is a suitable tool for capturing useful information from the ordinal patterns of sEMG time series. In a previous work, a theoretical comparison in terms of bias and variance of two MPE variants—namely, the refined composite MPE (rcMPE) and the refined composite downsampling (rcDPE), was addressed. In the current paper, we assess the superiority of rcDPE over MPE and rcMPE, when applied to real sEMG signals. Moreover, we demonstrate the capacity of rcDPE in quantifying fatigue levels by using sEMG data recorded during a fatiguing exercise. The processing of four consecutive temporal segments, during biceps brachii exercise maintained at 70% of maximal voluntary contraction until exhaustion, shows that the 10th-scale of rcDPE was capable of better differentiation of the fatigue segments. This scale actually brings the raw sEMG data, initially sampled at 10 kHz, to the specific 0–500 Hz sEMG spectral band of interest, which finally reveals the inner complexity of the data. This study promotes good practices in the use of MPE complexity measures on real data. [ABSTRACT FROM AUTHOR]

The article presents a methodology for combining psychological and physiological data through an ergonomic analysis of the activity. It mentions that two field studies have been done including the stationary suction dredger situation and the moving suction dredger situation whose focus was on bridge assistant responsible for dredging activity. It mentions that heart rate measurements were recorded at physiological level.

Abstract: Objective: The objective of this work was to study modifications in motor control through surface electromyographic (sEMG) activity during a very short all-out cycling exercise. Methods: Twelve male cyclists (age 23±4 years) participated in this study. After a warm-up period, each subject performed three all-out cycling exercises of 6s separated by 2min of complete rest. This protocol was repeated three times with a minimum of 2 days between each session. The braking torque imposed on cycling motion was 19Nm. The sEMG of the vastus lateralis was recorded during the first seven contractions of the sprint. Time–frequency analysis of sEMG was performed using continuous wavelet transform. The mean power frequency (MPF, qualitative modifications in the recruitment of motor units) and signal energy (a quantitative indicator of modifications in the motor units recruitment) were computed for the frequency range 10–500Hz. Results: sEMG energy increased (P ⩽0.05) between contraction number 1 and 2, decreased (P ⩽0.05) between contraction number 2 and 3 then stabilized between contraction number 3 and 7 during the all-out test. MPF increased (P ⩽0.05) during the all-out test. This increase was more marked during the first two contractions. Conclusions: The decrease in energy and the increase in the sEMG MPF suggest a large spatial recruitment of motor units (MUs) at the beginning of the sprint followed by a preferential recruitment of faster MUs at the end of the sprint, respectively. [Copyright &y& Elsevier]

Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter's intrinsic PE and separated its contribution from the signal's ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able to theoretically decouple the signal-specific contribution from that induced by the linear filter. Therefore, by providing a proper framework of PE linear filter characterization, we improved the PE interpretation by identifying possible artifact information introduced by the preprocessing steps. [ABSTRACT FROM AUTHOR]

Abstract: The aim of the study was to confirm the hypothesis that the longer a contraction is sustained, the larger are the changes in the spatial distribution of muscle activity. For this purpose, surface electromyographic (EMG) signals were recorded with a 13×5 grid of electrodes from the upper trapezius muscle of 11 healthy male subjects during static contractions with shoulders 90° abducted until endurance. The entropy (degree of uniformity) and center of gravity of the EMG root mean square map were computed to assess spatial inhomogeneity in muscle activation and changes over time in EMG amplitude spatial distribution. At the endurance time, entropy decreased (mean±SD, percent change 2.0±1.6%; P <0.0001) and the center of gravity moved in the cranial direction (shift 11.2±6.1mm; P <0.0001) with respect to the beginning of the contraction. The shift in the center of gravity was positively correlated with endurance time (R 2 =0.46, P <0.05), thus subjects with larger shift in the activity map showed longer endurance time. The percent variation in average (over the grid) root mean square was positively correlated with the shift in the center of gravity (R 2 =0.51, P <0.05). Moreover, the shift in the center of gravity was negatively correlated to both initial and final (at the endurance) entropy (R 2 =0.54 and R 2 =0.56, respectively; P <0.01 in both cases), indicating that subjects with less uniform root mean square maps had larger shift of the center of gravity over time. The spatial changes in root mean square EMG were likely due to spatially-dependent changes in motor unit activation during the sustained contraction. It was concluded that the changes in spatial muscle activity distribution play a role in the ability to maintain a static contraction. [Copyright &y& Elsevier]

Multiscale Permutation Entropy (MPE) analysis is a powerful ordinal tool in the measurement of information content of time series. MPE refinements, such as Composite MPE (cMPE) and Refined Composite MPE (rcMPE), greatly increase the precision of the entropy estimation by modifying the original method. Nonetheless, these techniques have only been proposed as algorithms, and are yet to be described from the theoretical perspective. Therefore, the purpose of this article is two-fold. First, we develop the statistical theory behind cMPE and rcMPE. Second, we propose an alternative method, Refined Composite Downsampling Permutation Entropy (rcDPE) to further increase the entropy estimation's precision. Although cMPE and rcMPE outperform MPE when applied on uncorrelated noise, the results are higher than our predictions due to inherent redundancies found in the composite algorithms. The rcDPE method, on the other hand, not only conforms to our theoretical predictions, but also greatly improves over the other methods, showing the smallest bias and variance. By using MPE, rcMPE and rcDPE to classify faults in bearing vibration signals, rcDPE outperforms the multiscaling methods, enhancing the difference between faulty and non-faulty signals, provided we apply a proper anti-aliasing low-pass filter at each time scale. [ABSTRACT FROM AUTHOR]

Introduction Les troubles de la marche et de l’équilibre sont fréquents au cours de la maladie de Parkinson. Leur quantification dans la vie quotidienne reste un enjeu pour l’évaluation de leur sévérité. L’objectif ici était de décrire et quantifier le pattern de l’activité musculaire des membres inférieurs des patients parkinsoniens en condition de vie réelle. Matériel et méthodes L’activité des muscles vaste latéraux (VL), biceps fémoris (BF), gastrocnémius (G), soléaire (Sol) et tibialis anterior (TA) a été enregistrée chez 8 patients parkinsoniens et 8 sujets sains, à l’aide d’un système portatif. La durée et la quantité d’activité EMG étaient mesurées pour chacune des phases du cycle de marche : double (poser et phase pré-oscillante) et simple appuis, et comparées entre les 2 groupes. Résultats La durée du cycle de marche n’était pas différente mais la durée des phases de double appui était augmentée chez les patients parkinsoniens. Chez les patients parkinsoniens, des modifications du pattern EMG du cycle de marche étaient retrouvées : – une augmentation de l’amplitude de la co-contraction des muscles agonistes-antagonistes au niveau de la cheville (Sol-TA) et du genou (BF-VL) lors du poser du pied ; – un allongement de l’activation des muscles Sol, BF et VL lors des phases d’appui simple ; – une augmentation de l’amplitude et de la durée de l’activité des muscles Sol, G et VL lors de la phase pré-oscillante ; – une diminution de la durée de l’activité des muscles TA et VL lors de la phase oscillante. Discussion–conclusion L’évaluation en condition de vie réelle représente une nouvelle approche permettant de quantifier de façon précise et spécifique les troubles de la marche des patients parkinsoniens. La dégradation du pattern EMG des membres inférieurs existe en dehors des épisodes de freezing ou de chutes, avec une augmentation de la durée de la phase de double appui associée à une co-contraction et une sur-activation des muscles antigravitaires pouvant résulter d’un phénomène adaptatif à l’instabilité posturale. [ABSTRACT FROM AUTHOR]

Permutation Entropy (PE) and Multiscale Permutation Entropy (MPE) have been extensively used in the analysis of time series searching for regularities. Although PE has been explored and characterized, there is still a lack of theoretical background regarding MPE. Therefore, we expand the available MPE theory by developing an explicit expression for the estimator's variance as a function of time scale and ordinal pattern distribution. We derived the MPE Cramér–Rao Lower Bound (CRLB) to test the efficiency of our theoretical result. We also tested our formulation against MPE variance measurements from simulated surrogate signals. We found the MPE variance symmetric around the point of equally probable patterns, showing clear maxima and minima. This implies that the MPE variance is directly linked to the MPE measurement itself, and there is a region where the variance is maximum. This effect arises directly from the pattern distribution, and it is unrelated to the time scale or the signal length. The MPE variance also increases linearly with time scale, except when the MPE measurement is close to its maximum, where the variance presents quadratic growth. The expression approaches the CRLB asymptotically, with fast convergence. The theoretical variance is close to the results from simulations, and appears consistently below the actual measurements. By knowing the MPE variance, it is possible to have a clear precision criterion for statistical comparison in real-life applications. [ABSTRACT FROM AUTHOR]