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Many heart diseases can be identified and cured at very early stage by studying the changes in the features of electrocardiogram (ECG) signal. In this paper, an algorithm to detect Atrial Fibrillation (AF) in the ECG signal is developed. For correct detection of AF, pre-processing and feature extraction of ECG signal shall be performed before it detects AF. After considering the ECG signal from the database, in the preprocessing stage de-noising of the ECG signal is carried out in order to obtain a clean ECG signal. After pre-processing, before feature extraction R peak detection is carried out for the signal. Since R peak is having the highest amplitude, and therefore it is detected in the first round and subsequently location of other peaks of the ECG signals are performed. After completing preprocessing and feature extraction using Discrete Wavelet Transform (DWT) applied based on inverted T wave logic and ST-segment elevation. Our classification algorithm was demonstrated to successfully acquire, analyze and interpret ECGs for the presence of AF indicating its potential to support m-Health diagnosis, monitoring, and management of therapy in AF patients.

The energy depletion is the major drawback in wireless sensor networks (WSNs). Sensor nodes use utilize tiny sized batteries, which can neither be replaced nor recharged. Hence, the energy must be optimally maintained in such battery-operated networks. One of the popular method of optimizing the energy efficiency is through clustering. In clustering methodology, all the sensor hubs are assembled into number of groups and each group is allocated with a group head called cluster head (CH). The CH consumes high energy than remaining sensor nodes (SNs) as they are responsible for data collection and data transmission. The cluster heads are usually overloaded with large number of sensing nodes and thereby leads to faster death. Thereby degrades the network execution. In this paper, we propose three different protocols—distributed energy-efficient clustering (DEEC), developed DEEC (DDEEC) and enhanced distributed energy-efficient clustering (E-DEEC). These protocols are compared with energy depletion of cluster head and lifetime. Simulations are carried by considering 500 sensor nodes and 30 cluster heads within a communication range of \( 200\times 200\) m for 10,000 iterations. It is observed that number of nodes alive are 70% more in E-DEEC than DEEC and DDEEC. The packets transmitted to the BS are 80% more in E-DEEC than DEEC and DDEEC. The E-DEEC protocol enhances the network lifetime than DECC and DDEEC.

An Epileptic Seizure is a sudden and uncontrolled symptom originated in brain. They could be due to abnormal electrical activity start in brain for very brief period and also recurring seizures due to a brain disorder. Therefore during the event there is an effect due to bumps, cuts /bruises are commonly observed occurrences. The effect of injuries are seriously caused due to falling and losing alertness or realization during or after a seizure, for instance a broken bone or injury. This project used an Arduino based airbag protection system for the people suffering from seizures by using an accelerometer, which will detect the fall of the person and triggers the airbag. A push pull solenoid is used to puncture a canister that is present in the airbag. A relay is used to operate the solenoid. When the fall is detected, the Arduino will trigger the airbag. This project helps the people who are suffering from epilepsy and protect them from injuries. This project objectives to design and development of precise system for fall detection based on Arduino technology through noticeable perfection in accuracy and specificity of system. Initial phase a close approximation and selection of its relevant threshold values and prototype design. Later Phase consist of application on test subjects to visualise the accuracy in system design.

Wireless sensor networks have plenty of small sensor nodes. These sensor nodes direct the sensed data to the Base Station (BS). The transmission of data to the BS utilizes more energy. Many clustering algorithms are available for improving energy efficiency in homogenous and heterogeneous networks. This paper investigates the protocol which minimizes the energy consumed with respect to network lifetime and stability period. In this paper it is considered the Distributed Energy Efficient Clustering (DEEC) and Threshold Distributed Energy Efficient Clustering (TDEEC) in different heterogeneous network scenarios. The performance of TDEEC and DEEC is compared by varying the parameters-advanced nodes, super nodes, fraction of advance and super nodes. The network life and stability period is approximated for 100 nodes in 10000 rounds. It is observed that numbers of nodes alive are more in TDEEC than DEEC after completion of 10000 rounds. The packets transmitted to the BS in TDEEC increases from 2lakhs to 7 lakhs for 1700 to 7000 rounds but in DEEC packets transmitted to the BS are maintained constant 2 lakhs after 7000 rounds. It is observed the TDEEC is the best protocol when compared to DEEC based on stability period and network life-time.