Smart activity sequence generator in wearable IoT

Abstract

Sensors in activity based computing enable continuous monitoring of numerous physiological signals when attached to the human body. This finds wide application in areas of activity monitoring, bio-medical rehabilitation, and fitness tracking. Primary challenges in embedded application development for smart wearable include high energy efficiency and user compatibility. Existing algorithms and applications are still unable to fully utilize the true power of the data being collected. They provide lot of descriptive data analytics but lack in predictive analysis. Energy efficiency of computing as predicted by Koomey’s is expected to strike the second law of thermodynamics based on Launder’s Limit within few decades. In this work an energy efficient computing technique for next generation mobile applications is developed. Proposed Artificial Intelligence based energy-efficient embedded algorithm that provide personalized training sequence recommendation in order to achieve desired calorie goals. Suggested training sequence of 6 activities fall under high, medium and low calorie burn with achieved median for 234C:535C:688C respectively. The crux of this implementation is Calorie Matrix Regeneration via state feedback technique using Markov Decision Process (MDP) and Genetic Algorithm (GA). Number of generations required by the GA to reach a suboptimal solution is optimized. While Machine learning algorithms are written in C/C++ for effective embedded implementation, certain computationally expensive modules like MDP and GA are coded in Python with proposed IoT cloud based implementation thereby improving battery efficiency to 12–16 h. This implementation is first of its kind and a step ahead of available state of the art fitness training algorithms/applications.