Low-energy scheduling algorithms for wearable fall pre-impact detection system

Abstract

In this paper, novel low-energy static and dynamic scheduling algorithms with low computational complexities for heterogeneous multiprocessor systems are proposed. Since battery life of the system plays a critical role in wearable embedded systems, the algorithms are useful for energy consumption reduction in Body Area Network (BAN)-based wearable multiprocessor systems in healthcare applications. Our developed BAN-based fall pre-impact detection system is used in this investigation. Based on simulation results using the algorithms, it is found that the battery life can be extended up to 41.6 percent more of its normal life without the algorithms.