The multifold advancements in technology have provided an easy access of healthcare in terms of “e-healthcare” at least in the developed and developing nations. However, the ground-truth reality of e-healthcare scenario in the rural segments is still facing many challenges. Whereas the e-healthcare segment in developed nations is a tremendous success, it has not been that efficacious for the rural parts of developing nations. One of the major challenges, among other, is the population in this region, particularly in the countries like India, Bangladesh, Sri Lanka, Thailand, and Malaysia. With regard to technology, poor internet connectivity is the biggest issue in these regions. People’s perception toward e-healthcare/telemedicine also plays an important role in its success. This chapter addresses such e-healthcare challenges and their possible solutions in the rural regions of South Asia.

Prior to Internet of Things (IoT), patient’s communications with doctors were restricted to visits, or in form of text or telephonic modes. There was no technique for clinicians or hospitals to monitor patient’s health in a continuous mode and provide suggestions accordingly. IoT-enabled devices have made it possible to monitor remotely. This has unleashed the potential to keep patients healthy and safe, permitting the doctors to deliver excellent care. It has also improved patients’ rate of satisfaction as consulting physicians are available easier and are more effective. Additionally, monitoring the patient’s health remotely reduces the duration of hospital stay and stops readmissions. IoT also has a key influence on dropping healthcare expenses greatly and improving treatment results.

Biomedical signals like ECG, EEG, EMG, EOG, ERG, etc., and images such as ultrasound, MRI, CT, PET, etc., are very useful for assessing the wellbeing of a human being. In order to determine the abnormality in a particular organ or part of the body, physicians use these signals and images. Although today’s signal recorders and image scanners are of excellent resolution and quality, sometimes they fail to convey the actual scenario of the body part/organ [1 5].

This paper presents a comprehensive review of the wearable healthcare monitoring systems proposed by the researchers to date. One of the earliest wearable recorders, named “a silicon locket for ECG monitoring”, was developed at the Indian Institute of Technology, Bombay, in 2003. Thus, the wearable health monitoring systems, started with the acquisition of a single signal/ parameter to the present generation smart and affordable multi-parameter recording/monitoring systems, have evolved manifolds in these two decades. Wearable systems have dramatically changed in terms of size, cost, functionality, and accuracy. The early-day wearable recorders were with limited functionalities against today’s systems, e.g., Apple’s iWatch which comprises abundant health monitoring features like heart rate monitoring, breathing app, accelerometers, smart walking/ activity monitoring, and alerts. Most of the present-day smartphones are not only capable of recording various health features like body temperature, heart rate, photoplethysmograph (PPG) signal, calory consumption, smart activity monitoring, stress measurement, etc. through different apps, but they also help the user to get monitored by a family physician via GSM or even internet of things (IoT). One of the latest, state-of-the-art real-time personal health monitoring systems, Wearable IoT-cloud-based health monitoring system (WISE), is a beautiful amalgamation of body area sensor network (BASN) and IoT framework for ubiquitous health monitoring. The future of wearable health monitoring systems will be far beyond the IoT and BASN.

Soft computing techniques have found numerous applications in various domains of image processing and computer vision. This paper represents a survey on various soft computing methods'- fuzzy logic, neural network, neuro-fuzzy systems, genetic algorithm, evolutionary computing, support vector machine etc. - applications in various image processing areas. There are numerous applications of SC ranging from industrial automation to agriculture and from medical imaging to aerospace engineering, but this paper deals with the relevance and feasibility of soft computing tools in the area of image processing, analysis and recognition. The techniques of image processing stem from two principal applications namely, improvement of pictorial information for human interpretation and processing of scene data for automatic machine perception. The different tasks involved in the process include enhancement, filtering, noise reduction, segmentation, contour extraction, skeleton extraction etc. Their ultimate aim is to make understanding, recognition and interpretation of the images from the processed information available from the image pattern. There are many hybridized approaches like neuro-fuzzy system (NFS), fuzzy-neural network (FNN), genetic-fuzzy systems, neuro-genetic systems, neuro-fuzzy-genetic system exist for various image processing applications. Tools like genetic algorithms (GAs), simulated annealing (SA), and tabu search (TS) etc. have been incorporated with soft computing tools for applications involving optimization.

Optimization metrics for compiled code are not always measured in execution clock cycles on the target architecture. Modern cellular telephone or wireless devices, which may download executables over a wireless network connection or backhaul infrastructure, it is often advantageous for the compiler to reduce the size of the compiled code that must be downloaded to the wireless device. By reducing the size of the code, savings are achieved in terms of bandwidth required for each wireless point of download. These are metrics correlated to the dynamic run-time behaviour of not only the compiled code on the target processor, but also the underlying memory system, caches, DRAM, and buses, etc. Despite new generation of embedded systems are getting innovative and computationally powerful with upcoming embedded processors, the market demands more computational-intensive embedded software to be developed on embedded systems. It is very essential to implement efficient embedded software to meet the market demand of embedded systems. These embedded systems are special-purpose computing systems and built to perform very specific embedded applications. And, these embedded applications mainly use three key resources of embedded systems: (1) CPU (2) Run-time memory (3) Persistent memory i.e. NAND/NOR flash memory. This paper summarizes several effective embedded software optimization techniques to optimize CPU usage, Run-time memory, and Persistent memory.

Load frequency control (LFC) is one of the most profitable ancillary services of power system market presently. The main goal of LFC is to reduce zero steady-state error for frequency deviations. In the present review paper of LFC problem, worldwide history of various types of controllers, control strategy, smart techniques and methodologies which are implemented and which can be implemented at generation, transmission and distribution areas of a power system. Moreover, ABT mechanism in India is implemented in 2002 to regulate grid frequency. Out of this in deregulated power system energy storage with the potential of different types of energy storage at distribution area in the power system has been highlighted. In a smart grid due to intermittent nature of the renewable energy sources (wind and solar), unpredictable daily and seasonal variations there may be an imbalance between supply and demand which results in deviations in the grid frequency. To eliminate the problem of LFC Electrical energy storage (EES) is technologies are listed. LFC problem with different soft computing techniques such as Genetic Algorithm (GA), Neural Network (NN), Fuzzy Logic (FG), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFP), Tabu Search Algorithm (TSA) has been carried out. This literature review will help the new researcher to give the guideline to work in the area of load frequency control.

In this work, arrhythmia detection and classification from ECG signals has been performed using a digital signal processor- TMS320C6713. Two of the predominant ECG arrhythmias- premature ventricular contraction (PVC) and atrial fibrillation (AF) have been addressed in this work. In order to distinguish the PVC and AF beats from normal ECG beats, algorithms based on the morphological characteristics of arrhythmias have been applied. The PVC and AF beats present in ECG signals have been classified using correlation-based algorithm, in which a PVC or AF beats are compared/correlated with a normal ECG beat. The correlation coefficient value for normal ECG beats for a particular ECG signal is above 0.9 (highly correlated) whereas for a PVC or AF beats its value is in the range of 0.09 to 0.3 (highly uncorrelated). Another algorithm, based on slope/amplitude, has been implemented for detecting the PVC beats from ECG signals. The slope/ amplitude-based algorithm detects the PVC beats with 98.94% accuracy as compared to 65.20% accuracy by correlation-based algorithm. Thus, slope/amplitude-based algorithm outperforms the correlation-based algorithm as two parameters -the slope of QRS complex and R wave amplitude- are considered for detecting the abnormal beats. This work presents a DSP processor-based system, ideal for use in real time applications, for detecting PVC and AF beats from ECG signals.

In this paper, CS based medical images compression framework has been presented. The discrete cosine transform (DCT) as well as discrete wavelet transform (DWT) have been used for finding the sparsity in the medical images. The comparison of the results obtained by combining DCT and DWT with CS for medical imagesCT and MRIhas been presented.The CS & DWT compression scheme achieved a maximum compression ratio (CR) of 3.01 with PSNR of 31.6732 dB and average CoC value of 0.9983 for an MRI image, whereas for CT image these values are 2.89, 31.7935 dB and 0.9987, respectively.The CS and DCT based algorithm could obtain a maximum CR value of 1.5789 with average values (for both types of images) of PSNR, RMSE and CoC of 13.1774 dB, 56.0007 and 0.8384, respectively. Correspondence to: Rahul Kher, EC Department,G H Patel College of Engineering & Technology, Vallabh Vidyanagar, Gujarat, India; E-mail: rahul2777@gmail.com

In medical treatment, diagnosis plays very important role. Here we are proposing a reliable, affordable wireless patient monitoring system. We have designed a system that will measure the physiological parameters body temperature, oxygen saturation in blood (SPO2), heart rate, glucose measure in blood, as well as two bioelectrical signals electrocardiogram (ECG) signals and electroencephalogram (EEG). The recorded parameters and signals will then be transferred via bluetooth communication protocol to an android based smartphone. We also intended to generate an SMS alert in case of emergency situation with location information.

In the past decade, wearable sensors and devices have evolved as key technological objects which have dramatically revolutionized the next generation healthcare solutions. This is the era of cut-throat competition filled with immense stress which leads to the detection of various diseases even in the people of early age. Moreover, people are becoming more health conscious in developed as well as developing countries. The miniaturization of sensors and devices and tendency of people to be physician-independent have lead the researchers across the globe to come up with numerous healthcare solutions which are not only ubiquitous but are affordable also. Wearable medical devices (WMDs) capable of monitoring some of the most common physiological parameters- pulse, blood oxygen saturation, 2 lead ECG, heart rhythm, skin temperature etc. have already been popular. Ambulatory and long-term health monitoring for elderly people is another aspect, particularly for countries like China and Japan where population aging is increasing. With the development of networks, collected health information can be sent to the cloud server of the nearest clinic or hospital. Doctors can then provide patients with medical advice. Thus, WMDs or WMD-based applications can help both patients and doctors in daily health monitoring. The paradigm has gradually shifted to the mobile health (mHealth) - an integration of ever advancing wireless communication, ubiquitous computing, and wearable device technologies. The mHealth can be regarded as the most advanced version of healthcare monitoring.

The electroencephalogram (EEG) is an important bioelectric signal for studying human brain characteristics as well as detection of abnormalities like epilepsy. However, the EEG recorded from frontal channels, often contain strong artifacts produced by eye movements. Existing regression-based methods for removing artifacts require various procedures for pre-processing and calibration that are inconvenient and time consuming. This paper describes a method for removing the EOG artifacts contained in EEG signal based on adaptive filtering. The method uses separately recorded noisy EEG and clean EEG as two reference inputs. The noisy EEG signals with three types of EOG artifacts-horizontal eye movement, vertical eye movement and eye blinks have been recorded for five subjects. The adaptive filter, based on a least mean square (LMS) algorithm, adapts its coefficients to produce an output which matches the reference input.

Wearable ambulatory ECG (A-ECG) signals obtained using wearable ECG recorders inherently contain the motion artifacts due to various physicals activities of the subject. Classification of four such physical activities (PAs) - left arm up-down, right arm up-down, waist twisting and walking- of five healthy subjects has been performed using neuro-fuzzy classifier (NFC). The Gabor energy feature vectors have been used to train the NFC. The overall PA classification accuracy achieved by the NFC classifier is almost 95% for single-fold as well as ten-fold experiments.

Ambulatory ECG signal (A-ECG) is useful when long term cardiac monitoring of a patient is necessary. Ambulatory ECG monitoring provides electrical activity of the heart while a person is involved in doing his or her normal routine activities. Thus, the recorded ECG signal consists of cardiac signal along with motion artifacts introduced due to person's body movements during routine activities. This motion artifact has spectral overlap with cardiac signal in 1-10 Hz which corresponds to ECG features like P wave and T wave. These artifacts due to different physical activities (PA) might help in further cardiac diagnosis. For Classification of body movements, first the motion artifacts from A-ECG have been extracted using Adaptive filtering and discrete wavelet transform (DWT) approaches. The statistical parameters such as mean, median, variance, max value of extracted motion artifact signals are calculated. After that feature vector is created by combining principal components and above four parameters of respective motion artifacts signals. These combine features are fed to multilayer feed-forward neural network (MLPFNN) for classification. For this work the ECG signals of six healthy subjects (aged of 19 to 26 years) were recorded while the person performs various body movements activity like (i) up and down movement of left hand, (ii) up and down movement of right hand, (iii) waist twisting movement while standing and (iv) change in position from sitting down on chair to standing up movement in lead I configuration by using BIOPAC MP 36 signal acquiring system.

Communication with surrounding world is necessary for the well-being of individuals with severe nervous system injuries or disorders. For instance, amyotrophic lateral sclerosis (ALS) deprives patients of their ability to speak and to move their extremities to control different devices, biomedical signals like EEG, EMG, EOG can be used. EOG based HCI has advantage over other signal based HCI. The current research paper focuses on EOG based Human computer interface system for controlling different devices. For that, it is required to develop the EOG signal acquisition system. The EOG signals are very small in amplitude, typically in millivolts. Hence, in order to generate the signals which are capable of controlling various devices, an amplifier with appropriate filters (for noise removal purpose) is required to be designed. This paper focuses on development of low cost EOG acquisition system and HCI system for controlling devices.

The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)-left arm up down, right arm up down, waist twisting and walking-have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time-frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects.

Healthcare monitoring is now becoming part of everyday life.Today's healthcare industry aims to provide better healthcare to people anywhere, anytime in the world in economical and patient friendly manner.In this paper the physiological parameters like heart rate, body temperature, SpO 2 and ECG signal are obtained and displayed using PIC18F45K22 Microcontroller.Body temperature, SpO 2 and heart rate are displayed on LCD display and ECG signal is monitored on PC through Labview.If the abnormalities in the recorded parameters are detected, then an SMS alert will be sent to the physician.The system comprises of low cost ECG, SpO 2 and temperature sensors along with the GSM modem.