Inertial Measurement Units (IMUs) have a longlasting popularity in a variety of industrial applications, from navigation systems, to guidance and robotics. Their use in clinical practice is now becoming more common thanks to miniaturization and the ability to integrate on-board computational and decisionsupport features. IMU-based gait analysis is a paradigm of this evolving process, and in this study its use for the assessment of Parkinson's Disease (PD) is comprehensively analyzed. Data coming from 25 individuals with different levels of PD symptoms severity and an equal number of age-matched healthy individuals were included into a set of 6 different machine learning (ML) techniques, processing 18 different configurations of gait parameters taken from 8 IMU sensors. Classification accuracy was calculated for each configuration and ML technique, adding two meta-classifiers based on the results obtained from all individual techniques through majority of voting, with two different weighting schemes. Average classification accuracy ranged between 63% and 80% among classifiers and increased up to 96% for one meta-classifier configuration. Configurations based on a statistical pre-selection process showed the highest average classification accuracy. When reducing the number of sensors, features based on the joint range of motion were more accurate than those based on spatio-temporal parameters. In particular, best results were obtained with the knee range of motion, calculated with 4 IMUs, placed bilaterally. The obtained findings provide data-driven evidence on which combination of sensor configurations and classification methods to be used during IMU-based gait analysis to grade the severity level of PD.

Background. The main goal of the present study was to explore theta and alpha event-related desynchronization/synchronization (ERD/ERS) activity during shooting performance. We adopted the idiosyncratic framework of the multi-action plan (MAP) model to investigate different processing modes underpinning four types of performance. In particular, we were interested in examining the neural activity associated with optimal-automated (Type 1) and optimal-controlled (Type 2) performances.Methods. Ten elite shooters (6 male and 4 female) with extensive international experience participated in the study. ERD/ERS analysis was used to investigate cortical dynamics during performance. A 4 × 3 (performance types × time) repeated measures analysis of variance was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha, and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha) for each electrode site across the scalp. This analysis was conducted on 120 shots for each participant in three different frequency bands and the individual data were then averaged.Results. We found ERS to be mainly associated with optimal-automatic performance, in agreement with the “neural efficiency hypothesis.” We also observed more ERD as related to optimal-controlled performance in conditions of “neural adaptability” and proficient use of cortical resources.Discussion. These findings are congruent with the MAP conceptualization of four performance states, in which unique psychophysiological states underlie distinct performance-related experiences. From an applied point of view, our findings suggest that the MAP model can be used as a framework to develop performance enhancement strategies based on cognitive and neurofeedback techniques.

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The execution of rhythmical motor tasks requires the control of multiple skeletal muscles by the Central Nervous System (CNS), and the neural mechanisms according to which the CNS manages their coordination are not completely clear yet. In this study, we analyze the distribution of the neural drive shared across muscles that work synergistically during the execution of a free pedaling task. Electromyographic (EMG) activity was recorded from eight lower limb muscles of eleven healthy untrained participants during an unconstrained pedaling exercise. The coordinated activity of the lower limb muscles was described within the framework of muscle synergies, extracted through the application of nonnegative matrix factorization. Intermuscular synchronization was assessed by calculating intermuscular coherence between pairs of EMG signals from co-active, both synergistic and non-synergistic muscles within their periods of co-activation. The spatiotemporal structure of muscle coordination during pedaling was well represented by four muscle synergies for all the subjects. Significant coherence values within the gamma band (30-60 Hz) were identified only for one out of the four extracted muscle synergies. This synergy is mainly composed of the activity of knee extensor muscles, and its function is related to the power production and crank propelling during the pedaling cycle. In addition, a significant coherence peak was found in the lower frequencies for the GAM/SOL muscle pair, possibly related to the ankle stabilizing function of these two muscles during the pedaling task. No synchronization was found either for the other extracted muscle synergies or for pairs of co-active but non-synergistic muscles. The obtained results seem to suggest the presence of intermuscular synchronization only when a functional force production is required, with the observed gamma band contribution possibly reflecting a cortical drive to synergistic muscles during pedaling.

Electronic noses (e-noses), artificial sensor systems generally consisting of chemical sensor arrays for the detection of volatile compound profiles, have potential applications in respiratory medicine. We assessed within-day and between-day repeatability of an e-nose made from 32 sensors in patients with stable chronic obstructive pulmonary disease (COPD). We also compared between-day repeatability of an e-nose, fraction of exhaled nitric oxide (FENO) and pulmonary function testing. Within-day and between-day repeatability for the e-nose was assessed in two breath samples collected 30 min and seven days apart, respectively. Repeatability was expressed as an intraclass correlation coefficient (ICC). All sensors had ICC above 0.5, a value that is considered acceptable for repeatability. Regarding within-day repeatability, ICC ranged from 0.75 to 0.84 (mean = 0.80 ± 0.004). Sensors 6 and 19 were the most reproducible sensors (both, ICC = 0.84). Regarding between-day repeatability, ICC ranged from 0.57 to 0.76 (mean = 0.68 ± 0.01). Sensor 19 was the most reproducible sensor (ICC = 0.76). Within-day e-nose repeatability was greater than between-day repeatability (P < 0.0001). Between-day repeatability of FENO (ICC = 0.91) and spirometry (ICC range = 0.94-0.98) was greater than that of e-nose (mean ICC = 0.68). In patients with stable COPD, the e-nose used in this study has acceptable within-day and between-day repeatability which varies between different sensors.

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Cycling training is strongly applied in post-stroke rehabilitation, but how its modular control is altered soon after stroke has been not analyzed yet. EMG signals from 9 leg muscles and pedal forces were measured bilaterally during recumbent pedaling in 16 post-acute stroke patients and 12 age-matched healthy controls. Patients were asked to walk over a GaitRite mat and standard gait parameters were computed. Four muscle synergies were extracted through nonnegative matrix factorization in healthy subjects and patients unaffected legs. Two to four synergies were identified in the affected sides and the number of synergies significantly correlated with the Motricity Index (Spearman’s coefficient = 0.521). The reduced coordination complexity resulted in a reduced biomechanical performance, with the two-module sub-group showing the lowest work production and mechanical effectiveness in the affected side. These patients also exhibited locomotor impairments (reduced gait speed, asymmetrical stance time, prolonged double support time). Significant correlations were found between cycling-based metrics and gait parameters, suggesting that neuro-mechanical quantities of pedaling can inform on walking dysfunctions. Our findings support the use of pedaling as a rehabilitation method and an assessment tool after stroke, mainly in the early phase, when patients can be unable to perform a safe and active gait training.Electronic supplementary materialThe online version of this article (doi:10.1007/s10439-016-1660-0) contains supplementary material, which is available to authorized users.