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Sometimes mental stress needs to be control as it results in different dangerous suffering. Timely mental stress detection can help to prevent stress related health problems. The aim of this paper is to design an IoT base wearable, cost effective and low power smart band for health care that detect mental stress based on skin conductance. This band can monitor user's mental stress continuously and transmit the stress related data wirelessly to user's smart phone. It not only help the users in better understanding their stress patterns but also provide the physician with reliable data for a much better treatment. Inputs to this device are various signals from different sensors. By intelligently analyzing the correlation between these signals using machine learning algorithm, this band predicts that whether the subject is suffering from stress or not.

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The availability of inexpensive hardware such as CMOS cameras and microphones has fostered the development of wireless multimedia sensor networks (WMSNs). In WMSNs, wirelessly interconnected devices enable ubiquitously retrieving multimedia contents such as video and audio streams, and still images along with scalar data from surroundings for wide range of applications are constrained by processing, memory, and power resources. Image compression via low-complexity and resource efficient transforms has been addressed by several researchers to prolong network lifetime where energy conservation is achieved through sharing computational load among sensor nodes and by adjusting the transmission ranges of camera nodes. However, those schemes are not adaptive to the presence and changes of energy level of computational sensor nodes and to the amount of computational load. We propose a resource and energy efficient distributed image compression algorithm that dynamically configures according to the energy levels and the forwarding strategy that is based on the entropy of the image. The simulation results show that our adaptive distributed image compression scheme significantly prolongs the network lifetime and improves the network utilization efficiency, while maintaining adequate image quality.