Telemedicine is the use of telecommunication and information technologies in order to provide clinical health care at a distance. It helps eliminate distance barriers and can improve access to medical services that would often not be consistently available in distant rural communities. It is also used to save lives in critical care and emergency situations. Telemedicine allows remote diagnoses and monitoring of patients [1]. It guarantees agility, safety, and reliability in modern health-care institutions. There are several challenges associated to automation in this sort of environment [2], viz: heterogeneity of devices, protocols, and programming interfaces; the requirement for flexible, impact-free deployment; the requirement for easy to configure, easy to manage, scalable and, if possible, self-adjusting systems, and others. We focus on the problem of patients’ vital data collection, distribution, and processing. We suggest that current solutions based on manual note taking are slow, time consuming, and labor resource intensive. Besides, it imposes an obstacle to real-time data access that curbs the ability of clinical diagnostics and monitoring. We present a solution to automate this process from bedside data collection to information distribution and remote access by medical staff. Our solution is based on concepts of wireless sensor networks and utility computing. “Sensors” are attached to existing medical equipments that are inter-connected to exchange services; these are integrated to the institution’s computing network infrastructure. The information becomes available in the “cloud”, from where it can be processed by expert systems and/or distributed to medical staff for analysis. We argue that these technologies provide desirable features for automation in telemedicine environment addressing the challenges listed above. Our contribution is two-folded in social and scientific fields. In social we demonstrate an innovative and low cost solution to improve the quality of medical assistance delivery and; in scientific field we address the challenges of how to integrate sensors connected to legacy medical devices which cloud computing services to collect, process and delivery patient’s vital data. I. Motivation: As described below the process works based on manual notes. The interactions are described below. (i) A staff member collects patient's data at bedside, writing it down to a paper spreadsheet; (ii) The notes are typed in a data entering terminals; (iii) The data is transmitted to a database server that organizes, indexes, and make it accessible through a database interface; and (iv) At this point, medical staff can access this information through an interface application. It is clear that there is latency between (i) data gathering and (iv) information accessibility. This is undesirable and prevents real-time monitoring of vital patients’ data, restricting the clinician’s monitoring capabilities. Moreover, this process is error prone, as there is a possibility of incorrectly input. We suggest the following high-level requirements for the solution: 1. It must implement the methods to collect, process and distribute patient’s vital data, from bedside to remote accessibility. 2. It must be open, flexible and extensible; that is, it must support heterogeneous equipments in different numbers that can be added to system on ad hoc basis. 3. It must be secure; that is, the system must guarantee the integrity and confidentiality of medical data. 4. It must be manageable; that is, it must provide control over the myriad of computing devices connected to the environment. 5. It must be reliable; that is it must guarantee system availability despite of fluctuations of operational conditions and punctual issues. 6. It must be scalable, to support the deployment in large health –care environments and the integration of different institutions. Health Monitoring System Using MSP 430 www.iosrjournals.org 48 | Page 7. It must be optimized for computing resources; that is, the application must run in inexpensive, low profile computing devices. Here device is plugged in and begin to operate, i.e. to collect and transmit data; computer resources available to receive, store, process, and distribute the information. Different than multi-layered engineered computing environments, resources can be plugged in and out the micro controller and configured for the different aspects of the operation sharing the common infrastructures for communication, management, and security.