Wearable Platform for Automatic Recognition of Parkinson Disease by Muscular Implication Monitoring

The need for diagnostic tools for the characterization of progressive movement disorders - as the Parkinson Disease (PD) - aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results

Gait analysis and quantitative drug effect evaluation in Parkinson disease by jointly EEG-EMG monitoring

This work addresses the rising need for a diagnostic tool for the evaluation of the effectiveness of a drug treatment in Parkinson disease, allowing the physician to monitor of the patient gait at home and to shape the treatment on the individual peculiarity. In aim, we present a cyber-physical system for real-time processing EEG and EMG signals. The wearable and wireless system extracts the following indexes: (i) typical activation and deactivation timing of single muscles and the duty cycle in a single step (ii) typical and maximum co-contractions, as well as number of co-contraction/s. The indexes are validated by using Movement Related Potentials (MRPs). The signal processing stage is implemented on Altera Cyclone V FPGA. In the paper, we show in vivo measurements by comparing responses before and after the drug (Levodopa) treatment. The system quantifies the effect of the Levodopa treatment detecting: (i) a 17% reduction in typical agonist-antagonist co-contractions time (ii) 23.6% decrease in the maximum co-contraction time (iii) 33% decrease in number of critical co-contraction. Brain implications shows a mean reduction of 5% on the evaluated potentials

Combined EEG/EMG Evaluation During a Novel Dual Task Paradigm for Gait Analysis

Little knowledge is available about neural dynamics during natural motor behavior and its perturbation in aging and neurological diseases. In the present study, we aim to evaluate electroencephalography-electromyography (EEG-EMG) co-registration features of rest and walking in basal condition and under cognitive tasks in normal subjects to characterize a “normal gait” and to define the possible paradigm to detect abnormal behavior. We realized EEG-EMG co-registration in 17 healthy subjects in different conditions: 1) sitting, 2) standing 3) walking. A P300 oddball paradigm was performed during 4) standing condition and 5) during walking. We found that the P300 component amplitude increases during physical activity. The negative correlation between age and P300 component vanishes during gait. The spectral width of the total alpha rhythm appears reduced in the course of P300 in a static situation, with likely phenomena of desynchronization related to cognitive task. During gait, the activity is canceled, suggesting a state of "idling" of cortical areas previously involved in the process of recognition of the target stimulus. Additionally, EMG co-contraction and μ-rhythm desynchronization (μ-ERDs) are also analyzed using wireless equipment. It is demonstrated the EMG co-contraction validity, showing the possibility to discern a normal gait (tot. steps: 60; max co-contraction time: 100ms; average: 20ms) from a perturbed one (tot. steps: 60; max co-contraction time: 260ms; mean: 70ms). μ-ERDs were detected in about 60% of the analyzed steps, showing medium variations in μ - power of about -2.4

EEG-based Signatures of Isometric Arm Forces by Females at Different Levels of Physical Exertion and Comfort

In recent years, electroencephalography (EEG) has become a valuable technique for ergonomics studies of physical activities and other real-life tasks. Since the perception of force exertion is influenced by various psychophysical, cognitive, and social factors, different subjective measures have been traditionally used to measure the perception of physical exertion and related body discomfort. Along with the subjective measures, research showed that neural signals are also necessary objective measures to understanding human perception of physical tasks. However, EEG signatures of different physical exertion levels and perceived physical comfort have not been explored. The main objective of this study was to investigate EEG activity measured by power spectral density (PSD) for isometric arm forces at different levels of physical exertion and physical comfort. The first part of the study investigated PSD changes at five predefined force exertion levels, i.e., extremely light, light, somewhat hard, hard, and extremely hard. The healthy female participants performed physical exertions and rated their level of experienced physical comfort. Significant differences in force exertion and PSD for theta, beta, and gamma waves were observed. Significant correlations were also found between PSD, force, and rating of physical comfort (RPPC). In the second part of the study, PSD changes at predefined physical comfort levels were investigated, namely at very low, moderate, fair, high, and very high comfort levels. The participants also rated the level of perceived physical exertion. Significant differences in force exertion and comfort levels for theta, beta, and gamma power were found. In addition, significant correlations were found between PSD, force, and rate of physical exertion (RPE). Overall, this is a novel study where EEG signatures of isometric efforts by females have been investigated at different force and physical comfort levels. The reported results should improve our understanding of the neural correlates of physical tasks performed by females