Bio-Heat Transfer and Monte Carlo Measurement of Near-Infrared Transcranial Stimulation of Human Brain

Transcranial photobiomodulation is an optical method for non-invasive brain stimulation. The method projects red and near-infrared light through the scalp within 600-1100 nm and low energy within the 1-20 J/cm2 range. Recent studies have been optimistic about replacing this method with pharmacotherapy and invasive brain stimulation. However, concerns and ambiguities exist regarding the light penetration depth and possible thermal side effects. While the literature survey indicates that the skin temperature rises after experimental optical brain stimulation, inadequate evidence supports a safe increase in temperature or the amount of light penetration in the cortex. Therefore, we aimed to conduct a comprehensive study on the heat transfer of near-infrared stimulation for the human brain. Our research considers the transcranial photobiomodulation over the human brain model by projecting 810 nm light with 100 mW/cm2 power density to evaluate its thermal and optical effects using bioheat transfer and radiative transfer equation. Our results confirm that the near-infrared light spectrum has a small incremental impact on temperature and approximately penetrates 1 cm, reaching the cortex. A time-variant study of the heat transfer was also carried out to measure the temperature changes during optical stimulation.Comment: The complete geometry proposed in this work is available for download. The proposed geometry is in STL and MPHBIN formats, and the model is surrounded by air. All the tissues are available and assembled, and it is recommended to use the transparency tool to acquire a better observation. Please cite this publication when referencing this materia

Design and Steering Control of a Center-Articulated Mobile Robot Module

This paper discusses the design and steering control for an autonomous modular mobile robot. The module is designed with a center-articulated steering joint to minimize the number of actuators used in the chain. We propose a feedback control law which allows steering between configurations in the plane and show its application as a parking control to dock modules together. The control law is designed by Lyapunov techniques and relies on the equations of the robot in polar coordinates. A set of experiments have been carried out to show the performance of the proposed approach. The design is intended to endow individual wheeled modules with the capability to merge and make a single snake-like robot to take advantage of the benefits of modular robotics

Evaluating Attentional Impulsivity: A Biomechatronic Approach

Executive function, also known as executive control, is a multifaceted construct encompassing several cognitive abilities, including working memory, attention, impulse control, and cognitive flexibility. To accurately measure executive functioning skills, it is necessary to develop assessment tools and strategies that can quantify the behaviors associated with cognitive control. Impulsivity, a range of cognitive control deficits, is typically evaluated using conventional neuropsychological tests. However, this study proposes a biomechatronic approach to assess impulsivity as a behavioral construct, in line with traditional neuropsychological assessments. The study involved thirty-four healthy adults who completed the Barratt Impulsiveness Scale (BIS-11) as an initial step. A low-cost biomechatronic system was developed, and an approach based on standard neuropsychological tests, including the trail-making test and serial subtraction-by-seven, was used to evaluate impulsivity. Three tests were conducted: WTMT-A (numbers only), WTMT-B (numbers and letters), and a dual-task of WTMT-A and serial subtraction-by-seven. The preliminary findings suggest that the proposed instrument and experiments successfully generated an attentional impulsivity score and differentiated between participants with high and low attentional impulsivity.Comment: 10 pages, 5 figures, 5 table

A Wearable RFID-Based Navigation System for the Visually Impaired

Recent studies have focused on developing advanced assistive devices to help blind or visually impaired people. Navigation is challenging for this community; however, developing a simple yet reliable navigation system is still an unmet need. This study targets the navigation problem and proposes a wearable assistive system. We developed a smart glove and shoe set based on radio-frequency identification technology to assist visually impaired people with navigation and orientation in indoor environments. The system enables the user to find the directions through audio feedback. To evaluate the device's performance, we designed a simple experimental setup. The proposed system has a simple structure and can be personalized according to the user's requirements. The results identified that the platform is reliable, power efficient, and accurate enough for indoor navigation.Comment: 6 pages, 6 figures, 3 table

Evaluating the Possibility of Integrating Augmented Reality and Internet of Things Technologies to Help Patients with Alzheimer's Disease

People suffering from Alzheimer's disease (AD) and their caregivers seek different approaches to cope with memory loss. Although AD patients want to live independently, they often need help from caregivers. In this situation, caregivers may attach notes on every single object or take out the contents of a drawer to make them visible before leaving the patient alone at home. This study reports preliminary results on an Ambient Assisted Living (AAL) real-time system, achieved through the Internet of Things (IoT) and Augmented Reality (AR) concepts, aimed at helping people suffering from AD. The system has two main sections: the smartphone or windows application allows caregivers to monitor patients' status at home and be notified if patients are at risk. The second part allows patients to use smart glasses to recognize QR codes in the environment and receive information related to tags in the form of audio, text, or three-dimensional image. This work presents preliminary results and investigates the possibility of implementing such a system.Comment: 5 pages, 5 figure

Driver Drowsiness Detection with Commercial EEG Headsets

Driver Drowsiness is one of the leading causes of road accidents. Electroencephalography (EEG) is highly affected by drowsiness; hence, EEG-based methods detect drowsiness with the highest accuracy. Developments in manufacturing dry electrodes and headsets have made recording EEG more convenient. Vehicle-based features used for detecting drowsiness are easy to capture but do not have the best performance. In this paper, we investigated the performance of EEG signals recorded in 4 channels with commercial headsets against the vehicle-based technique in drowsiness detection. We recorded EEG signals of 50 volunteers driving a simulator in drowsy and alert states by commercial devices. The observer rating of the drowsiness method was used to determine the drowsiness level of the subjects. The meaningful separation of vehicle-based features, recorded by the simulator, and EEG-based features of the two states of drowsiness and alertness have been investigated. The comparison results indicated that the EEG-based features are separated with lower p-values than the vehicle-based ones in the two states. It is concluded that EEG headsets can be feasible alternatives with better performance compared to vehicle-based methods for detecting drowsiness.Comment: 546 Preprint version of the manuscript published in the proceedings of the 10th RSI International Conference on Robotics and Mechatronics (ICRoM 2022), Nov. 15-18, 2022, Tehran, Ira

Connection Mechanism for Autonomous Self-Assembly in Mobile Robots

ABSTRACKThis paper presents a connection mechanism for autonomous self-assembly in mobile robots. Using this connection mechanism, mobile robots can be autonomously connected and disconnected. The purpose of self-assembly in mobile robotics is to add a new capability to mobile robots, thus, improving their performance to best fit the terrain conditions. Construction of a reconnectable joint is of primary concern in such systems. In this paper, first the geometric conditions and force equations of a general docking mechanism are studied. Then, we discuss the design details of our connection mechanism and present some experimental results that show that the proposed mechanism overcomes significant alignment errors and is considerably power efficient.7 Halama

Driver Drowsiness Detection Using Wearable Brain Sensing Headband and Three-Level Voting Model

Drowsiness is the leading cause of many fatal accidents and a substantial financial burden for the economy. Efforts have been made to develop techniques to prevent major accidents while remaining practical for everyday use. The most successful approach discovered thus far involves utilizing physiological techniques that rely on EEG signals. Despite their promising performance, the signal collection process has made them unsuitable for practical implementations. However, the emergence of low-cost commercial EEG headsets has enabled tackling this issue. Our study aimed to assess the effectiveness of machine learning models in identifying drowsiness stages using minimal EEG channels. The study was conducted with fifty sleep-deprived participants driving in a simulator. Based on the Observer Rated Drowsiness method, we divided the stages of drowsiness into three categories: alert, drowsy, and sleepy. Various features were extracted from the EEG signals in time, frequency, and time-frequency domains. Three models were trained in each domain using k-nearest neighbors and ensemble bagged tree classifiers. A majority vote among the three models determined data labels, trained using different combinations of channel data features. Three training strategies were utilized: 1) single channel, 2) temporal channels, frontal channels, left-side channels, and right-side channels separately, and 3) all channels. The results of 10-fold cross-validation showed that the frequency features of temporal channels had the highest accuracy. The best results for nearest neighbors were 97.1% (alert-sleepy), 96.6% (drowsy-sleepy), and 96.7% (alert-drowsy). The highest accuracy of ensemble bagged trees was 100% for all three models.</p