Mobile phone based health monitoring allows caregivers a better way to monitor their patients and the cost is also less compared to staying in hospitals. The objective of this research work is to develop a mobile platform that has the capability to collect sensor data from heterogeneous wearable sensors, process the data, analyze the data and trigger the warning messages to doctors, relatives etc. The system will provide the capability to visualize the signal in doctors mobile. The system also considering the involvement of the doctors for assigning the risk levels of the patients. The doctor will get the warning message in his mobile. The involvement of the doctors will help this system to be more accurate and acceptable for health monitoring. This system also proposes developing dynamic algorithms for reduced power consumption during real time continuous monitoring of the patients.

The system is able to collect the sensor data to monitor the basic vital parameters such as ECG from the wireless body sensors to the patient's Smartphone using high speed Bluetooth technology. The collected data should be buffered temporarily in the mobile itself, here in smart phone data can be saved in the SQLite database. The data can be analyzed in the phone itself to find the warning level. If the result is above a threshold a warning message should be send to the doctor and caregivers. The warning message and the stored data will be transmitted to the central database situated in the hospital. If the doctor needs to see the patients ECG Signal in his or her phone, the system should also provide a provision to view the ECG report in his or her smart phone. So no matter where the user is she or he can view the needed data in their mobile. System Architecture is shown in the Fig 1.


Apart from the healthcare application there are other functionalities and applications that run in smart phone that consume more energy. For high risk patients the reception of the data from the sensor is more frequent than the low risk patients. For the low risk patients the data will be transmitted data to the central database server when the memory gets full or during the reception of a warning level. For a high risk patients the Energy consumption will change based on the energy required to transmit the data to the central database server and also the amount of energy required to transmit the warning message. The state transition diagram of the Energy optimization algorithm is in the Fig 2. The state transition diagram of the Active state is shown in the Fig 3.

Team Members

Leader Of the Team Faculty Student


Maneesha V. Ramesh


Rekha P.


Sruthy Anand