One important technological factor that restricts the advancement of wearable devices is their battery life. One possible solution to the problem of wearing electronics’ unsteady operation over long periods of time is the idea of self-powered wearable electronics, or SWE. Due to their potential in ordinary life activities, self-powered wearable devices have been gaining growing interest in recent years. Here, we lay forth a fresh strategy for using one’s own thermal energy to power electronic devices-by incorporating a thermoelectric generator into wearable gear. Thermoelectric generators that are worn by people are able to transform the temperature differential between their bodies and the surrounding environment into power. The average normal body temperature is $98.6^{\circ} \mathrm{F}$. The generated electricity can power various wearable electronic devices, such as health monitors for tracking parameters. This hybrid approach can optimize energy harvesting in various environmental conditions, ensuring a more consistent and reliable power supply.

Seeing how stroke patients frequently face sluggish and discouraging recovery processes particularly with regard to regaining hand movement inspired this effort. Repetitive, inactive, and devoid of engagement and real-time feedback, traditional motor rehabilitation techniques may make patients less likely to stick with their treatment. In order to address this and implement a more engaging and quantifiable recovery method, we decided to develop a gamified motor rehabilitation glove for stroke patients. The combination of sensor-based tracking and gamification in this glove sets it apart from other rehabilitation systems, enhancing the fun and motivation of therapy sessions. By providing patients with a game-like experience rather than just tracking movement, the glove makes therapy something they look forward to and lets them analyse their progress through ratings and rankings. Lycra is used in the glove's construction for comfort and flexibility. It measures finger coordination, movement speed, and range of motion using five A1334 Hall Effect sensors and neodymium magnets. Furthermore, an MPU 9250 sensor records the hand's stability and orientation, and FSR 402 sensors evaluate each finger's strength. To extract metrics like hand stability, range of motion, and finger strength, an Arduino Uno analyses sensor data. By reacting to the patient's hand movements, the game itself promotes active engagement in motor therapy. The glove attempts to improve performance of individuals undergoing rehabilitation by fusing technology

The safety of people in a wide range of professions, such as pilots, spacecraft, drivers, physicians, industrial workers, computer users, etc., is seriously threatened by fatigue. Measuring human weariness is essential for improving workplace productivity, safety, and efficiency. The survey looks at the variety of techniques that have been used to measure and determine a person’s level of fatigue. Researchers have used a number of physiological signal features to study human fatigue. Using a variety of physiological markers, this study provides a comprehensive examination of how stress and fatigue might be identified. Additionally, it looks for the best characteristics and techniques for assessing human fatigue and stress.

For workers in numerous industries, fatigue is a serious safety risk. Measuring human weariness is crucial to improving an individual's productivity at work, increasing overall safety, and attaining more work efficiency. By knowing an athlete's fatigue threshold, they can design workouts that push them to adapt without leading to overtraining or injury. Fatigue can compromise muscle control and joint stability, significantly increasing the risk of strains, sprains, and other injuries. Unexplained or persistent muscle fatigue can be a symptom of various medical conditions, including neurological disorders, metabolic syndromes, or chronic fatigue syndrome. In the workplace, particularly for physically demanding jobs, measuring muscle fatigue can help identify tasks that are a high risk for musculoskeletal disorders. Many studies analyze human weariness using various physiological signal characteristics. To identify a person's level of stress and exhaustion, several physiological signals are studied, including Blood Volume Pulse (BVP), Electromyogram (EMG), Galvanic Skin Response (GSR), Electrocardiogram (ECG), and Respiratory signals. It also goes over the several classification strategies that studies have employed to determine when someone is fatigued. This study offers a thorough analysis of the identification of stress and exhaustion using a range of physiological indicators. It also seeks to identify the best attributes and methods for effectively evaluating human tiredness and stress.

INTRODUCTION: While the use of zinc nanoparticles (ZnNPs) as an antibacterial agent in the biomedical industry has recently attracted significant attention, collagen has aroused significant interest as a biomaterial in medical and tissue engineering applications. OBJECTIVES: In order to create biofilm loaded with biosynthesized ZnNPs for use in chronic wound healing applications, type-I collagen was extracted from the study's subject. by the acid soluble collagen technique, collagen was isolated from the fish skin of the trevally and identified by SDS-PAGE. Aqueous extract from Cassia fistula leaves was also used to greenly manufacture stable ZnNPs, which were then characterized by UV-Vis, FTIR, and XRD measurements. METHODS: Collagen and ZnNPs were then added to polyvinyl alcohol (PVA), creating a thin biofilm that had a high biocompatibility due to the production method's absence of a chemical reducer and crosslinking agent. When tested against the harmful bacteria, both ZnNPs alone and PVA/Collagen/ZnNPs biofilms showed potent antibacterial activity. RESULTS: By using the MTT test, the cytotoxic effects of collagen and ZnNPs on the Vero cell line were evaluated. With 97.76% wound closure, the PVA/Collagen/ZnNPs biofilm demonstrated strong in vitro wound scratch healing efficacy. CONCLUSION: The findings show that the PVA/Collagen/ZnNPs film dramatically increased cell migration by 40.0% at 24 hours, 79.20% at 48 hours, and 97.76% at 74 hours.

The main problems encountered when working on the lower extremities are; squat, kneel, pedal and when a person is keeping on prolonged standing for a long period of time, and sitting for long periods of time. Factors associated with lower extremity disease are not specific to any part of the lower extremity; They are often associated with diseases in other parts of the body, such as the limbs and trunk. To overcome these problems, in this project we will create small models using a 3D printer. The design also integrates servo motors. These servo motors are connected to a microcontroller and a power supply. The microcontroller board also interfaces with the myoelectric sensor. When a signal occurs within it, it causes the branch structure to move. Therefore, this project will help create effective lower limbs for paralyzed people using myoelectric sensors.

Blood collection monitor is used for accurate collection of blood. This compact device counts the amount of blood and gently rocks the blood to ensure that it is mixed evenly with the anticoagulant, which keeps blood clots from forming when blood is drawn from a donor. Blood collection process happens in different environments, the main challenge to the phlebotomist (one who draws blood for analysis or transfusion) is to make each blood collection process more comfortable and safer without compromising quality. Blood collection monitor comes into role here; it is specially designed to for standardized high quality blood collection with reduced work load of phlebotomist. A blood collection monitor makes ensuring that the right amount of blood is collected while maintaining continual movement to improve component output. So, the correct volume of blood collection and mixing of the blood with anticoagulant and periodically during collection of blood is done by blood collection monitor. In this project a more cost effective and easily portable model of the blood collection monitor will be designed.

Anemia is a medical disorder that arises when an individual's blood is deficient of sufficient mature, developed red blood cells with normal hemoglobin level. One of the main components of erythrocytes, hemoglobin, has a strong affinity for binding oxygen, which is necessary for cell survival. The body's cells won't get enough oxygen if there are any aberrant red blood cells or low hemoglobin levels, and this condition eventually leads to a condition known as hypoxia. The conventional method of diagnosing anemia involves pricking of finger and using analytical reagents to investigate is quite time consuming and requires skilled laboratory procedures and personnel. To overcome these limitations, a novel approach for the automated non-invasive detection of anemia is developed. In this work, a method for diagnosing anemia by analyzing changes in anemic individuals' anterior conjunctival pallor based on processed eye images is proposed. Nowadays, patients with anemia disease present in the world increased by around 60-70% respectively. This proposed work has successfully characterized to introduce novel approach for early and accurate anemia disease diagnosis. It employs LBP texture analysis for classification of eyelid images. There are several features which are considered based on extracted statistical analysis. The classification results demonstrate that these features are utilized to identify normal and abnormal patients successfully with an accuracy rate of 91%.

A new era in healthcare has begun with precision medicine, which tailors medications to the specific characteristics of each individual patient. This shift is being driven by the advent of advanced machine learning (ML) algorithms that can parse and make sense of biological data culled from diverse sources like clinical records, proteomics, and genomes. This study aims to give a thorough evaluation of ML algorithms employed in biomedical analysis for precision medicine, with a particular emphasis on these algorithms' integration, interpretation, and practical uses. In this first step, we take stock of biological data and its current state, drawing attention to issues like data volume, privacy concerns, and data heterogeneity that pose obstacles to its integration. We continue by taking a look at the most recent developments in supervised and unsupervised learning as well as deep learning algorithms used in biological analysis. In the context of precision medicine, we discuss the pros and cons of these algorithms and emphasise their ability to handle complex data and extract valuable insights. Methods for feature selection, data preprocessing, and model validation are among the topics covered in this investigation of how to integrate ML algorithms with biological data. To make sure that healthcare providers and patients can understand and trust the results of machine learning (ML) models, we investigate how explainable AI (XAI) can help interpret these decisions. We also provide case examples that show how ML algorithms have been used for precision medicine to diagnose diseases, predict how treatments will work, and create individualised treatment plans. These examples show how ML has the ability to revolutionise healthcare by enhancing patient outcomes while decreasing expenses. Lastly, we discuss potential future developments and areas for future research in the field, such as improving XAI methods, integrating multi-omics data, and creating more sophisticated ML models. The research highlights the significance of integrating knowledge from several disciplines to progress precision medicine, specifically computer science, data science, and biomedical sciences.

Multimodal medical image fusion aims to aggregate significant information based on the characteristics of medical images from different modalities. Existing research in image fusion faces several major limitations, including a scarcity of paired data, noisy and inconsistent modalities, a lack of contextual relationships, and suboptimal feature extraction and fusion techniques. In response to these challenges, this research proposes a novel adaptive fusion approach. Our knowledge distillation (KD) model extracts informative features from multimodal medical images using various key components. A teacher network is employed to emphasise the suitability and complexity of capturing high-level abstract features. The soft labels are utilised to transfer the knowledge between the teacher network as well as the student network. During student network training, we minimise the divergence between these soft labels. To enhance the adaptive fusion of extracted features from different modalities, we apply a self-attention mechanism. Training this self-attention mechanism minimises the loss function, encouraging attention scores to capture relevant contextual relationships between features. Additionally, a cross-modal consistency module aligns the extracted features to ensure spatial consistency and meaningful fusion. Our adaptive fusion strategy effectively combines features to enhance the diagnostic value and quality of fused images. We employ generator and discriminator architectures for synthesising fused images and distinguishing between real and generated fused images. Comprehensive analysis is conducted on the basis of diverse evaluation measures. Experimental results demonstrate improved fusion outcomes with values of 0.92, 41.58, 7.25, 0.958, 0.759, 0.947, 0.90, 7.05, 0.0726, and 76 s for SSIM, PSNR, FF, VIF, UIQI, FMI, EITF, entropy, RMSE, and execution time, respectively.

Chronic pain is a common problem among stroke patients, resulting from neurological damage to the central nervous system. This discomfort is primarily caused by the improper use of unaffected limbs or musculoskeletal issues. It can be challenging to differentiate neuropathic pain resulting from central nervous system damage. To address these challenges, researchers have developed a cutting‐edge technology called a Brain‐Computer Interface (BCI) based on electroencephalogram (EEG) data. In this paper, a novel BCI classifier has been developed using the Weighted Incremental‐Decremental Support Vector Machine (WIDSVM) classification method. The classifier has been trained using EEG‐based motor images from patients with central nervous system damage. The Quantum Chaos Butterfly Optimization Algorithm (QCBOA) has been used to enhance the performance of the WIDSVM classifier by creating a new dataset. The efficiency of the proposed model has been evaluated by comparing the results obtained from normal participants and those who developed chronic pain. The classification accuracy has been calculated for different regions, including the left hand, right hand, and feet, among the different participant groups. A total of 28 participants have been separated into three groups with pain in different regions, such as the lower abdomen and legs. The classifier has been tested using both 3‐channel bipolar montages and Common Spatial Patterns (CSPs). The results have shown that the proposed model offers higher classification accuracy and statistical significance in identifying the patient's risk of developing central neuropathic pain. However, it is important to note that further studies with larger sample sizes and different types of chronic pain are needed to validate the efficacy of the proposed model.

A brain cancer is an unexpected growth of nerves within the brain that interferes with the brain's normal function. Numerous lives have been lost as a result of it. It will take time to protect individuals from this illness by prompt discovery and the appropriate treatment. The search for tumor-affected brain cells is a difficult and time-consuming process. However, detecting brain cancer with the precision and speed necessary is a significant hurdle in the field of image processing. This study suggests a brand-new, precise, and enhanced method for finding brain cancer. Preprocessing, segmentation, feature extraction, optimization, and identification are some of the processes the system uses. A skull scripping constitutes one of the first steps in the procedure of finding anomalies in the brain and is used in the initial processing method. Discrete wavelet transform (DWT) is employed for feature extraction, while K-means algorithms are used for picture segmentation. In this section the optimised CNN method is used, which selects the best characteristics via dragon fly optimisation. The CNN classifier is used for identifying brain tumours. Utilising reliability, precision, and recall characteristics, this system evaluates its efficacy with that of another contemporary optimisation approach and declares that its work is superior.

Stroke recovery is the subsequent goal of stroke medicine. Rehabilitation and recovery research is exponentially increasing. However, several impediments impede the progress in the design of neurorehabilitation technology for stroke patient recovery. The conventional rehabilitation techniques for stroke recovery have some limitations like the absence of standardized terminology, poorly described methods, lack of consistent time frames and recovery biomarkers, reduced participation, and inappropriate measures to examine outcomes. Stroke recovery is challenging for many survivors. They require highly functioning and quick treatment accompanied by a gradual acceptance of brain improvement and human behavior. Therefore, there is an immediate need for neurorehabilitation technology to improve the quality of activities of daily life (ADLs) of those disabled. The method adopted is the design of neurorehabilitation technology using game-based systems that enhances the motor activities of hemiparesis patients.

Normally atmospheric air contains mixture of gases like nitrogen (78%), oxygen (21%), and small amount of lot of other gases like carbon-di-oxide (0.03%), hydrogen. Oxygen concentrator is a device that concentrate (or) separate oxygen from ambient air by specifically removing nitrogen by process of Pressure Swing Adsorption (PSA). Pressure Swing Adsorption is a technique that separate oxygen from a mixture of gases depending on pressure to which molecular characteristics and affinity for a zeolite (Natrolite). The proposed output of Oxygen concentrator with Pressure Swing Adsorption technology is economical and high-efficient. The experimental results of the proposed system supplied the oxygen at the purity of about 94.7% for the low velocities of 0.5-3 L/min. It is concluded that the system provides a good performance when considered that a patient with the COPD should take the oxygen at the purity of 90% or more for the low velocities of 0.5-3 L/min. On comparing with other oxygen concentrator available in the market, this proposed system is highly cost effective and comparatively lesser in weight. As per the experiments conducted using the proposed system provides comparatively less noise and works efficiently for long time and oxygen purity is also high. Since, this proposed system has these many features. This is so much helpful for peoples who can't able to afford market available oxygen concentrator.

There are currently many people all around the world who are suffering from chronic kidney infections. Today, everyone is attempting to be health-conscious, even though, owing to overwork and a hectic schedule, one only pays attention to one's health when symptoms appear. A few factors, for example, dietary habits, temperature, and expectations for daily luxuries, cause large numbers of people to be afflicted unexpectedly and without knowledge of their condition. Finding persistent kidney disease is often intrusive, costly, time-consuming, and dangerous. The main reasons why many people die without receiving care are especially in many developing countries since resources are few. As a result, early diagnosis and recognition of illness remains important, particularly in non-industrialized countries where illnesses are typically studied in late stages. However, if it does not show any symptoms at all, or if it does not show any disease-specific symptoms, it is very difficult to find &predict the disease type, detect and prevent such a disease, and this could lead to permanent health damage as well as the formation of new diseases, but machine learning can be a hope, as it is the best way for prediction and disease analysis. We will utilize data from CKD patients with 14 variables, as well as several machine learning approaches such as Decision Tree, SVM, and CNN model. To create an efficient machine learning model with the highest accuracy (by comparing several machine learning models) in predicting whether or not a person has CKD and, if so, how severe it is.

Each and everyone in this world should take care of elderly patients and treat them how they need to be treated. Due to aging, most of the elderly persons are facing health related issues and sent out to homes and orphanages. Elders cannot be able to visit doctors regularly to monitor health status. This article comes up with a solution for the elderly patients around by assisting them and helping them to be fearless. Here the proposed system makes use of different parameters like GPS and GSM modules, Pulse Oximeter, Accelerometer, Touch Sensor, Bluetooth that interface with the Arduino. The proposed smart glove is used to measure temperature, fall detection, pulse rate for the elderly patients, if they are facing any health-related troubles. This will help these patients to escape by altering the caretaker.

Abstract: The main objective of this project work is to determine the oxygen saturation level in the cerebrum. Smart head band is based on infra-red used during the preoperative period of cardiovascular operations and on stroke patients. It is a non-invasive technology that can monitor the regional oxygen saturation of the frontal cortex. This proposed method helps us to monitor health status of the patient’s oxygen saturation level especially for stroke and cardiac patients. It provides continuous information about brain oxygenation. This review focuses on the clinical validity and applicability of this monitor for cardiac surgical patients’ .This method is designed as a wearable device in the form of Head band.

A species of sand lobster available at the Bay of Bengal in abundance was chosen for the bio mineral extraction. Therefore, our aim was to prepare a film through bio mineral extraction from the species, then us orientalis. The lobsters were collected from the shore and its hard portions were separated. Then the hard portion was dried under sun for few days till it had become completely dry. The same was ground into powder in a mixer and taken for Hydroxyapatite (HAp) product preparations. The obtained bio mineral, HAp powder was white and crystalline. The HAp powder was added to Chitosan solution and casted in Petri dish and obtained as film. The film was removed and kept in a sealed cover for further analyses. The HAp and Chitosan film was characterised for their chemical functional group analyses by FTIR. The thermal behaviour and thermal stability were evaluated by DSC and TGA respectively. The future works such as good film formation of the blend and incorporating them into 3D printing for making film shall be worked upon. Both the conventionally prepared film and by 3D Printed films properties will be compared and studied.

About 42 million people suffer from thyroid diseases in India. The thyroid gland is a vital hormone gland which plays a major role in the metabolism, growth and development of the human body. It helps to regulate many body functions by constantly releasing a steady amount of thyroid hormones into the blood stream. If too much hormones is produced, it is called hyperthyroidism, if less amount is produced, it is called hypothyroidism. If these diseases are left untreated, the symptoms become severe therefore it is crucial to diagnose it at earliest. In this paper, a non invasive method is proposed to diagnose thyroid diseases through skin reflectance as well as to monitor the physiological parameters. Laser diode is used to measure the skin reflectance, as thyroid hormone causes changes in the skin. The light is emitted on to skin surface and the reflected light is measured by the infrared detectors. Based on the values from the detectors, it is classified as hyperthyroidism, hypothyroidism or euthyroidism. It has the advantage of being non-invasive compared to the invasive blood test, it can also be used to monitor the patient continuously in duration of treatment for every six weeks using Zigbee module. The proposed system is compact, cost effective, and easy to use at home environment

This study aimed to improve working memory performance by the enhancement of upper alpha band of electroencephalography (EEG) using Neurofeedback Training (NFT). Twenty five healthy subjects were trained on five sessions by means of feedback provided in the form of visual cues. The EEG signal is acquired using single channel electrode and connected to the system through Data Acquisition Device (DAQ). The upper alpha band (10-13 Hz) signal is extracted using LabVIEW software. Then the subject is asked to stay relaxed by viewing the natural scenes displaying in the system. With appropriate protocol designed for NFT, the end results showed that the participants were able to learn to increase the relative amplitude levels in individual alpha band during neurofeedback training and working memory performance was significantly improved by the enhancement of upper alpha band. On the first and last session, cognitive skill is tested by a conceptual span test and the scores are compared. In this study, 25 subjects showed significant success of training.