Motion-Based Activities Monitoring through Biometric Sensors Using Genetic Algorithm

Sensors and physical activity evaluation are quite limited for motion-based commercial devices. Sometimes the accelerometer of the smartwatch is utilized; walking is investigated. The combination can perform better in terms of sensors and that can be determined by sensors on both the smartwatch and phones, i.e., accelerometer and gyroscope. For biometric efficiency, some of the diverse activities of daily routine have been evaluated, also with biometric authentication. The result shows that using the different computing techniques in phones and watch for biometric can provide a suitable output based on the mentioned activities. This indicates that the high feasibility and results of continuous biometrics analysis in terms of average daily routine activities. In this research, the set of rules with the real-valued attributes are evolved with the use of a genetic algorithm. With the help of real value genes, the real value attributes cab be encoded, and presentation of new methods which are represents not to cares in the rules. The rule sets which help in maximizing the number of accurate classifications of inputs and supervise classifications are viewed as an optimization problem. The use of Pitt approach to the ML (Machine Learning) and Genetic based system that includes a resolution mechanism among rules that are competing within the same rule sets is utilized. This enhances the efficiency of the overall system, as shown in the research.