Design of a Resident Physical Fitness Data Monitoring System Based on the Sensor and Fuzzy Algorithm

Abstract

This article first introduces the working principle and research progress of motion sensors and analyzes the advantages and disadvantages of motion tracking sensors to provide a reference for in-depth research on motion sensing technology. By combining the fuzzy judgment method to identify the kinematic parameters, this paper establishes the statistical identification of features to predict the kinematic parameters and uses the fuzzy identification method to model the human behavioral characteristics. At the same time, the article designs a set of real-time data monitoring system for athletes’ real-time monitoring behavior under actual sports conditions, which is used to obtain residents’ sports data. Then, based on such requirements, the overall system architecture was designed to integrate the data acquisition module, data storage and processing module, and real-time data presentation module. The motion monitor can initiate a real-time monitoring request for a specific system group, the server can monitor individual groups to add real-time individual motion status, and the system will issue an alert if the system monitors a potentially dangerous situation.

Exploration of branches of physics for handling several cases in sports applications: A systematic literature review

“…The importance of kinematic and kinetic analysis in understanding sports biomechanics, with a focus on the impact of training on performance and potential injury risk (Dong et al, 2022;Menzel & Potthast, 2021;Rivadulla et al, 2020), through specific exercises, such as plyometrics, can influence athletes' biomechanics, including their landing techniques and energy utilization during physical activities (Edwards et al, 2023;Kotsifaki et al, 2022;Letafatkar et al, 2020;Welis et al, 2022;Gusril et al, 2022). In their research, Ma & Ma (2022) contribute to the development and validation of algorithms for biomechanical analysis, demonstrating how data-driven approaches can enhance our understanding of athletic performance and training interventions.…”

Design and testing of a microwave Doppler-based yield estimation system for machine-picked seed cotton

“…verified the existence of a certain relationship between the total mass of flowing solids and the power of the echo signal in the laboratory, which provided a new way of thinking about the detection of solid mass flow, and triggered the study of solid mass flow detection using similar methods. For example, Ma Xin et al [16] designed a microwave-based yield monitoring sensor to establish a linear relationship between power spectral density and soybeans, and after experimental verification, the average error of measurement is low 8%; Zou Jing et al [17] designed a microwave sensor to establish a model for the relationship between echo power and particle weight, and the experimental results show that the measurement error of the flow mass is within 13.01%. Yang Liwei et al [18] developed a microwave method based on the particle fertiliser mass flow measurement system, the establishment of two kinds of compound fertiliser mass flow and sensor output value of the linear regression model, the test detection of the relative error is not more than 10.04%.…”