Applicability and tolerability of electrical stimulation applied to the upper and lower leg skin surface for cueing applications in Parkinson's disease

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson

BACKGROUND AND OBJECTIVES

Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention.

METHOD

The AiCarePWP is a wearable electronics device designed to identify instances when a patient is on the brink of experiencing a freezing episode and subsequently deliver a brief electrical impulse to the patient's shank muscles to stimulate movement. The AiCarePWP wearable device aims to identify impending freezing episodes in PD patients and deliver brief electrical impulses to stimulate movement. The study validates this innovative approach using plantar insoles with a 3D accelerometer and electrical stimulator, analysing data from the inertial measuring unit and plantar-pressure foot data to detect and predict FoG.

RESULTS

Using a Convolutional Neural Network-based model, the study evaluated 47 gait features for their ability to differentiate resting, standing, and walking conditions. Variable selection was based on sensitivity, specificity, and overall accuracy, followed by Principal Component Analysis and Varimax rotation to extract and interpret factors. Factors with eigenvalues exceeding 1.0 were retained, and 37 features were retained.

CONCLUSION

This study validates CNN's effectiveness in detecting FoG during various activities. It introduces a novel cueing method using electrical stimulation, which improves gait function and reduces FoG incidence in PD patients. Trustworthy wearable devices, based on Artificial Intelligence of Things (AIoT) and Artificial Intelligence of Medical Things (AIoMT), have been developed to support such interventions.

Study on the effect of human sympathetic nerve cold stimulation to relieve driving fatigue based on order recurrence plot

Driving fatigue is very likely to cause traffic accidents, seriously threatening the lives and properties of drivers. Therefore, accurate detection and effective mitigation of driving fatigue are crucial for ensuring the personal safety of drivers. This study proposes a method to relieve driving fatigue by properly reducing the temperature to stimulate the human sympathetic nerve. The method uses the intelligent cooling and blowing device on the car seat cushion to achieve cold stimulation of the sympathetic nerve of the driver by reducing the temperature of the driver's hip, back and neck, so as to increase the excitement of the sympathetic nerve, keep the driver alert and achieve the purpose of fighting driving fatigue. In view of the fact that the traditional fatigue detection method is easily affected by environmental factors and individual differences, this study uses the order recurrence plot (ORP) method to detect driving fatigue based on electroencephalogram (EEG) signals. The results show that ORP textures drawn by EEG signals of the two driving conditions (normal driving condition and sensory cold stimulation driving condition) are significantly different, and the quantization parameters determinism (DET) and average diagonal line length (DLL) values are significantly different. Cold stimulation of the subjects' hips, back and neck to alleviate driving fatigue was the best when the temperature was 21 °C. In addition, compared with the traditional methods of fatigue relief, the sensory cold stimulation method proposed in this study does not easily to produce tolerance and has no damage to the body.

Production of rGO-Based Electrospinning Nanocomposites Incorporated in Recycled PET as an Alternative Dry Electrode

In this work, Coca-Cola^® bottles were reused as a PET polymer (rPET) source to produce electrospun polymeric nanofibers. The nanofibers were electrospun from polymer solutions with different concentrations of reduced graphene oxide (rGO) incorporated for applications in somatosensory electrical stimulation. The rPET/rGO nanofiber mats were characterized by SEM, TEM, Raman, DSC, TGA, and DMA and the results showed that the incorporation of rGO in electrospun rPET fibers produced rPET/rGO composites. The rPET/rGO composites were then evaluated for possible application as dry electrodes. Moreover, with a preliminary test of numerous volunteers, the rPET/rGO dry electrode showed promising results. The rPET/rGO electrodes showed good performance and applicability to make dry electrodes, and these have applications as dry or wearable electrodes to produce electrochemical sensors.

Study on the Effect of Judgment Excitation Mode to Relieve Driving Fatigue Based on MF-DFA

Driving fatigue refers to a phenomenon in which a driver’s physiological and psychological functions become unbalanced after a long period of continuous driving, and their driving skills decline objectively. The hidden dangers of driving fatigue to traffic safety should not be underestimated. In this work, we propose a judgment excitation mode (JEM), which adds secondary cognitive tasks to driving behavior through dual-channel human–computer interaction, so as to delay the occurrence of driving fatigue. We used multifractal detrended fluctuation analysis (MF-DFA) to study the dynamic properties of subjects’ EEG, and analyzed the effect of JEM on fatigue retardation by Hurst exponent value and multifractal spectrum width value. The results show that the multifractal properties of the two driving modes (normal driving mode and JEM) are significantly different. The JEM we propose can effectively delay the occurrence of driving fatigue, and has good prospects for future practical applications.

Electrotactile Perception Properties and Its Applications: A Review

With the increased demands of human-machine interaction, haptic feedback is becoming increasingly critical. However, the high cost, large size and low efficiency of current haptic systems severely hinder further development. As a portable and efficient technology, cutaneous electrotactile stimulation has shown promising potential for these issues. This paper presents a review on and insight into cutaneous electrotactile perception and its applications. Research results on perceptual properties and evaluation methods have been summarized and discussed to understand the effects of electrotactile stimulation on humans. Electrotactile applications are presented in categories to understand the methods and progress in various fields such as prostheses control, sensory substitution, sensory restoration and sensorimotor restoration. State of the art has demonstrated the superiority of electrotactile feedback, its efficiency and its flexibility. However, the complex factors and the limitations of evaluation methods made it challenging for precise electrotactile control. Groundbreaking innovation in electrotactile theory is expected to overcome challenges such as precise perception control, information capacity increasing, comprehension burden reducing and implementation costs.