Application of Object Detection Algorithm Based on Fiber Optic Sensors in Checking Body Fat Rate during Home Exercise

Abstract

Abstract With the increasing popularity of home fitness, people's attention to exercise effectiveness and physical health is also increasing. Body fat percentage directly reflects the body's fat content, and compared to obesity evaluation indicators such as weight or BMI, it can more scientifically and accurately evaluate the degree of obesity in the human body. In order to address the limitations of traditional body fat detection methods, this study chose fiber optic sensors as the means of body fat detection. The fiber optic sensors were in contact with the detected object, and the signals perceived by the fiber optic sensors during the motion process were converted into electrical signals. The signals were then digitized and algorithmic calculated. Using object detection algorithms to process the converted electrical signals, analyzing and extracting useful features from complex electrical signals, and accurately calculating the body fat percentage of the detected object. The results show that the algorithm proposed in this paper can accurately detect body fat percentage during home exercise, providing a convenient and fast monitoring method for sports enthusiasts, which helps improve fitness effectiveness and maintain physical health.