S. Radhakrishnan

Elderly Patient Monitoring System Using a Wireless Sensor Network

With demographic changes of the aging population and an increasing number of people living alone, pervasive home monitoring is set to play an important role in maintaining the independence and improving the quality of life for elderly persons at a lower cost. The present development of the demography of elderly people in the Western world will generate a shortage of caretakers for elderly people in the near future. The new concept of health monitoring is advanced by which health parameters are automatically monitored at home without disturbing daily activities. The proposed system is a network that supports various wearable sensors and contains on-board general computing capabilities for individual event detection, alerts, and communications with various medical informatics services. The purpose of our system is to provide extended monitoring for elderly patients under drug therapy after infarction, data collection in some particular cases, and remote consultation for elderly people.

Sensor grid applications in patient monitoring

Wireless Sensor Networks (WSNs) are finding an important role in patient monitoring in diverse environments including hospitals for post-operative patients and nursing homes for elderly patients. Sensor networking devices in WSNs are resource constrained since they have limited processing power and communication bandwidth. However, with a large number of such devices being deployed and aggregated over a wide area, WSNs have substantial data acquisition and processing capability. Thus, WSNs are important distributed computing resources that can be shared by different groups of patients in different environments. The emerging domain of WSNs with the grid extends the grid computing paradigm to the sharing of sensor resources in WSNs. In this perspective, by their very demand requirements and their socioeconomic impact, medical applications are certainly the most pertinent domain for using a wireless sensor grid. In this paper, we propose a wireless sensor grid architecture for monitoring the health status of different groups of patients to provide a platform for physicians and researchers to share information with distributed database and computational resources to facilitate analysis, diagnosis, prognosis and drug delivery.

Remote monitoring of post-operative patients using wireless sensor networks

Patients recovering from surgery are at risk of complications due to reduced mobility as a result of post-operative pain. The ability to monitor the recovery of this group of patients and identify those at risk of developing complications is, therefore, clinically desirable and may result in an early intervention to prevent adverse outcomes. We have developed a real-time patient monitoring system that integrates a vital signs sensor, a sensor network, electronic patient records and web portal technology to allow remote monitoring of post-operative patient status in the hospital and homecare conditions and to alert medical personnel when life-threatening events occur. This system will facilitate communication among patients, medical professionals at local hospitals and specialists available for consultation from distant places through a secured web server. The most valued features of this system are to prevent the patient from re-hospitalisation and to avoid possible critical events, thus reducing global healthcare costs.

Efficient Resource Scheduler for Parallel Implementation of MSA Algorithm on Computational Grid

Multiple sequence alignment is the most common task in computational biology. This multiple sequence alignment is computationally difficult and classified as a NP-Hard problem; so approximate algorithm(s) are generally required for most multiple alignment tasks. The Molecular Biologist may require the alignment of thousands of sequences that each can be of many hundreds of amino acids or even several millions of nucleotides. The approximation algorithm requires a long processing period of time to compute near optimal alignment. Thus, one step to reduce the processing time is to parallelize the algorithm. In order to have solution over parallelism method, we can either use expensive multiprocessor programming or cheaper cluster/Grid programming. Multiprocessor systems are specialized expensive hardware and are not commonly available. An alternative cheapest way is to use either a computer cluster or a Computing Grid. A cluster can be used for amino acid sequences and will be very slow for multiple sequence alignment of DNA molecule. So, the computing grid is the only cheapest alternative for performing multiple sequence alignment of DNA molecules. We have designed an efficient grid scheduler to perform the parallel tasks in grid that minimizes the communication cost and time complexity and also implemented parallel algorithm on computing grid. The experimental results show enhanced speedup.

Ambulatory monitoring of free living patients affected by Chronic Obstructive Pulmonary Disease (COPD) and Parkinson's Disease (PD) using Wireless Sensor Networks

Ambulatory investigations have become more and more important as many medical or physiological investigations that can be performed under the real situations of daily life. To perform complex physiological investigations under ambulatory conditions over long period of daily life, we have developed a real-time monitoring system for ambulatory patients that consist of a vital signs sensor, sensor network, electronic patient records and web portal technology to allow monitoring of ambulatory patients status and to alert medical personnel when life-threatening events occur. Ambulatory patients include the patients with Chronic Obstructive Pulmonary Disease (COPD), patients with Parkinsons disease (PD) during rehabilitation period. This system will facilitate communication among patients, medical professionals at local hospitals and provide knowledge discovery through integrated data mining. It also prevents the patient from re-hospitalisation and to avoid possible critical events, thus reducing global healthcare costs.

Resource prediction and management in active networks

Active networks provide a programmable user-network interface that supports dynamic modification of the networks behavior. Network nodes, in addition to forwarding packets, perform customized computation on the messages flowing through them. Resources in an active network mainly consist of CPU and bandwidth. The inherent unpredictability of processing times of active packet poses a significant challenge in CPU scheduling. It has been identified that prior estimation of the resource requirements of a packet is very difficult since it is platform dependent and also depends on processing load at the time of execution, operating system scheduling, etc. An efficient allocation is required for the optimal utilization of resources. In this paper, resources are estimated using prediction techniques such as single exponential smoothing (SES), adaptive-response-rate single exponential smoothing (ARRSES) and Holts two-parameter estimation models. The estimated results agreed most with the actual requirements. The estimation models were compared with model criteria. An algorithm was also designed for the allocation of resources. Effectiveness of the algorithm was measured through simulation and achieved almost perfect fairness for all flows and also provided much superior delay guarantees under a highly dynamic environment.