Abstract—Internet of Things (IoTs) is the emerging Technology as the third wave in the development of the internet. IOT is accepted to have massive impact sensors, surveillance devices; wearable’s and body area networks with advanced interface technique. This leads to the need for a hardware-software co-design approach. We present a highly parameterized hardware design allowing configurability and the ability to evaluate different design choices in a short amount of time. This paper aims to analyze the use of Deep learning algorithm for building such reconfigurable low power devices. We develop the hardware architecture for such a system and provide experimental results obtained for the case study of Posture detection for body worn cameras used for law enforcement. Our proposed system, implemented on a Raspberry picamera platform uses minimal power consumption with an accuracy of more than 90% for the selected case study of Posture Detection. 
 

Index Terms—IOT, deep learning algorithm, human posture, picamera

Cite: J. Preethi and V. Keerthana, "Human Posture Detection in Always-on Body Worn Cameras," International Journal of Signal Processing Systems, Vol. 5, No. 1, pp. 24-27, March 2017. doi: 10.18178/ijsps.5.1.24-27