Automatic Detection of Irritable Bowel Syndrome for 3D Images Using Supervoxel and Graph Cut Algorithm

Medical image processing is a complex exercise and involves a number of stages to identify the disease in the arena of medical imaging. Irritable bowel syndrome is an acute disorder that causes intense abdominal pain and leads to changes in the bowel system. It gives rise to various indications like bleeding, bloating, celiac disease, gastric cancer, ulcer, etc. The system proposed here seeks to segment and classify each symptom of the irritable bowel syndrome individually with the aid of supervoxel segmentation algorithm. Features are extracted depending on the color, shape, and texture of the object. The extracted features are fed into the multi-support vector machine to identify the specific region in the medical image. The experiment provides the result of a test set 100 images stored in the data set which improves accuracy that refines the final output.

Gait Event Detection System for the Control of Lower Limb Exoskeleton

Locomotion is an essential aspect of day-to-day human life. Advancement in wearable robotic technology enhances capabilities for maintaining the locomotion of people with disabilities. The exoskeleton, being one of them, meets the growing demands in the rehabilitation industry and enhanced locomotion requirements. Depending on the need and disability, various types of exoskeletons are designed. The design aspect of the exoskeleton includes various sensor systems, mechanical structure, mechanism, and control strategy used. Detection of gait events depends on the disability of the wearer and is very critical to decide the appropriate gait event that needs to be activated either by powering the actuators actively or passively. These interfaces should have a minimum possible response time to control the exoskeleton system to follow the wearer's gait. This review paper describes various sensing system incorporated in the control of various exoskeleton systems for the detection of gait events.

A WBAN-Based Framework for Health Condition Monitoring and Faulty Sensor Node Detection Applying ANN

Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.

Treatment Case Studies and Emissions Analysis of Wood in Yagya

The mantra becomes more powerful when that sound is chanted in front of purified fire and light; sound and heat energy mixtures are converted into high level of energy and spread around the atmosphere. Through this paper, the well experienced author team of various domains is continuously working in experimenting in joint collaboration with different GoI departments. They have observed the slow but continuous progress in different ails on many subjects through scientific study and approach. The main case studies where the patients got significant benefits through this alternate therapy have been systematically presented here. The power of yajna and mantra has attracted the intellectuals of this era. In the future, we may expect some automated intelligent healthcare expert system using this way of life. Yagya science and its treatment power of different diseases is surprising; the need is that current science should accept it logically with an open mind and heart and let the humanity take the complete benefit of it.

Performance Assessment of Ensemble Learning Model for Prediction of Cardiac Disease Among Smokers Based on HRV Features

Smoking impacts the pattern of heart rate variability (HRV); HRV therefore acts as a predictor of cardiac diseases (CD). In this study, to predict CD non-invasively among smokers, ensemble machine learning methods have been used. A single model is created based on ensemble voting classifier with a combined boosting technique to improve the accuracy of predictive model. The final ensemble model shows an accuracy of 95.20%, precision of 97.27%, sensitivity of 92.35%, specificity of 98.07%, F1 score of 0.95, AUC of 0.961, MCE of 0.0479, kappa statistics value of 0.9041, and MSE of 0.2189. The obtained accuracy by using the proposed method is the highest value achieved so far for the prediction of CD among smokers using HRV data.

Artificially Intelligent Physiotherapy

Physiotherapy is the trending medication for curing bone-related injuries and pain. In many cases, due to sudden jerks or accidents, the patient might suffer from severe pain. Therefore, it is the miracle medication for curing patients. The aim here is to build a framework using artificial intelligence and machine learning for providing patients with a digitalized system for physiotherapy. Even though various computer-aided assessment of physiotherapy rehabilitation exist, recent approaches for computer-aided monitoring and performance lack versatility and robustness. In the authors' approach is to come up with proposition of an application which will record patient physiotherapy exercises and also provide personalized advice based on user performance for refinement of therapy. By using OpenPose Library, the system will detect angle between the joints, and depending upon the range of motion, it will guide patients in accomplishing physiotherapy at home. It will also suggest to patients different physio-exercises. With the help of OpenPose, it is possible to render patient images or real-time video.

Relationship Between Speed of Performing Leg Extension With 30 RM Load and the Selected EMG Variables of Selected Quadricep Muscles

The aim of the study was to assess the effects of speed of leg extension exercise with 30 RM load on the EMG activity of Vastus Medialis (VM) and Vastus Lateralis (VL). Seventeen healthy male subjects performed 30 repetition of leg extension exercise with 30 RM load in minimum time possible and EMG activity from VM and VL of right lower extremity was recorded simultaneously. Two new criteria, namely gross load speed of movement (GLSM) and relative load speed of movement (RLSM) were developed as the variables of speed of exercise. Selected variables of EMG activity were median frequency (MDF), mean frequency (MNF), root mean square (RMS), and integrated EMG (IEMG). Product moment and rank order correlation were calculated between the selected variables of EMG activity and selected variables of speed of exercise. The selected EMG variables, namely MDF and MNF, reported a negative significant relationship (p<0.05) with selected variables of speed of exercise whereas a selected EMG variables, namely RMS reported a positive significant relationship (p<0.05) with selected with the selected variables of speed of exercise.

Design of Low-Cost Solar Parabolic Through Steam Sterilization

The objective of this study is to develop a low cost solar parabolic trough that can be used for steam sterilization of medical instruments in small clinics where electricity is scarce and expensive. On the basis of theoretical concepts of parabola and focus-balanced parabola, the assembly of ribs and reflector sheet with evacuated tube and heat pipe has been done. The parabolic trough has been mounted on a trolley so that it can be moved easily according to direction of sun light. The designed solar parabolic trough was integrated with pressure cooker under various setups and experiments were conducted to test whether sterilization is taking place or not. To validate sterilization process, tests were also conducted by placing the infected medical instruments. The solar parabolic trough developed was able to generate and maintain steam at 121 degrees Celsius at pressure 15 psig (101.3 kN/m2) for 15 minutes. The solar parabolic trough developed was effective in sterilizing the medical instruments.

Reconstruction of EIT Images Using Fish School Search and Non-Blind Search

Breast cancer is the most common type of cancer among women, affecting 2.1 million women per year worldwide. The best strategy for decreasing disease morbidity and mortality is early detection. Mammography is the most used exam for the diagnosis of breast cancer. However, this technique uses ionizing radiation and causes discomfort to the patient. One promising technique that can be used for early detection of breast cancer is electrical impedance tomography (EIT), which is an imaging technique free of ionizing radiation. Yet, its images still have low resolution, making it difficult to use in breast cancer diagnosis. Thus, the development of new reconstruction methods aiming better resolution is necessary. This work evaluates the performance of the reconstruction algorithm based on fish school search with non-blind search in a 3,190 finite element mesh.

Using Eye Tracking to Explore Visual Attention in Adolescents With Autism Spectrum Disorder

Video social stories are used to facilitate understanding of social situations for adolescents with autism spectrum disorder (ASD). This study explored the use of eye tracking technology to understand how adolescents with and without ASD visually attend to video social story content and whether visual attention is related to content comprehension. Six adolescents, with and without ASD, viewed a video social story of visiting a dental office. Eye gaze metrics, including fixation duration and count, and visit duration were collected to measure visual attention, and a knowledge assessment was administered for comprehension. Results indicated adolescents with ASD fixated and maintained visual attention at rates lower than peers without ASD. Adolescents with ASD scored higher (M=77.78) than peers without ASD (M=72.22) on the assessment indicating no relationship between eye gaze metrics and knowledge accuracy. Impact and implications of visual image type on frequency and duration of visual attention generated by participants is discussed.