Low back pain (LBP) is a morbid condition that has afflicted several citizens in Europe. It has negatively impacted the European economy due to several man-days lost, with bed rest and forced inactivity being the usual LBP care and management steps. Direct models, which incorporate various regression analyses, have been executed for the investigation of this premise due to the simplicity of translation. However, such straight models fail to completely consider the impact of association brought about by a mix of nonlinear connections and autonomous factors.In this paper, we discuss a system that aids decision-making regarding the best-suited support system for LBP, allowing the individual to avail of reinforcement and improvement in its self-management. These activities are monitored with the help of a wearable sensor that helps in their detection and their classification as those that soothe or aggravate LBP and hence, should or should not be performed. This system helps the patients set their own boundaries and milestones with respect to suitable activities. This system also does windowing and feature extraction. The present study is an empirical and comparative analysis of the most suitable activities that patients suffering from low back pain can select. The evaluation shows that the system can distinguish between nine common daily activities effectively and helps self-monitor these activities for the efficient management of LBP.

Sensors and physical activity evaluation are quite limited for motionbased 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.

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