Wearable energy efficient fitness tracker for sports person health monitoring application

Continuous evaluation of biological and physiological metrics of sports personalities, evaluating general health status, and alerting for life-saving treatments, is supposed to enhance efficiency and healthy performance. Wearable devices with acceptable form factors compact, flexibility, minimal power consumption, etc., are needed for continuous monitoring to avoid affecting everyday operations, thereby retaining functional effectiveness and consumer satisfaction. This research focuses on the acceleration tracker for particularizing the work. Acceleration data is typically collected on battery-powered sensors for activity detection, referring to an exchange between high-precision detection and energy-efficient processing. From a feature selection perspective, the paper explores this trade-off. It suggests an Energy-Efficient Behavior Recognition System with a comprehensive energy utilization model and the Multi-objective Algorithm of Particle Swarm Optimization (EEBRS-MPSO). Therefore, using Random Forest (RF) classifiers, the model and algorithm are tested to measure the precision of identification and obtain the task’s best performance with the lowest energy consumption, among other biologically-inspired algorithms. The findings indicate that energy consumption for data storage and data processing is minimized with magnitude relative to the raw data method by choosing suitable groups of attributes. Thus, the platform allows a scalable range of feature clusters that require the authors to provide an adequate power adjustment for given target use.

Introduction to Biologically Inspired Algorithms

In order to find more sophisticated ways to remain in competition in the stock market, investors and analysts are finding procedures based on nature-inspired artificial intelligence-based algorithms. It is seen that interest of researchers has grown in these technologies in the past years. These newer techniques have changed the investment arena of the stock market. A lot of thought process, hard work, creativeness, and knowledge about these algorithms are required to implement them in the stock investment area. In the past, few people have had the privilege to implement and obtain better results by using these algorithms. But with the access to affordable computing systems and experts with the knowledge of these computing systems, we can take advantage of making profit from the market. This chapter explains the detail working of these AI techniques such as chaos theory, neural networks, fuzzy logic, and genetic algorithms in detail.

Carrier optimization using CAD software and biologically inspired algorithms

This paper presents a comparative analysis of the application of biologically inspired algorithms and methodologies in CAD software packages, for determining the optimal profile values in carriers with two independent variables. As an example of a biologically inspired algorithm to solve this optimization problem, the scroll algorithm was applied, while the SolidWorks software package was used as an example of CAD.