Limb Sensor Training System for Real-Time Impact Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for martial arts and contact sports training lack a comprehensive solution that effectively measures and analyzes critical parameters during training, such as impact detection, physiological data, and spatial movement, while also providing real-time feedback and comparative analysis.
Innovation Solution
A system comprising limb sensors on the athlete's limbs, a training area sensor, and mobile applications that collect and process data in real-time, identifying and classifying blows, tracking spatial movement, and offering comparative analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are placed on each limb of the athlete, then measurement precision of training parameters is improved, but device complexity increases
Solution Approach 1:
The system divides the monitoring function into multiple independent sensor units, each attached to specific limbs (wrist, ankle, knee, elbow). Each sensor unit independently detects local parameters such as acceleration, velocity, and impact forces, then transmits data to a central processing unit. This segmentation enables precise measurement of limb-specific movements while maintaining modular system architecture that manages complexity.
Solution Approach 2:
The sensor units are designed with multi-functionality, capable of detecting multiple parameters (acceleration, velocity, impact force, positional data) using the same hardware platform. The universal sensor design reduces the number of different device types needed, simplifying the overall system structure while comprehensive parameter detection maintains high measurement precision.
2Productivity
If real-time data collection and processing is implemented, then productivity of training analysis is improved, but use of energy increases
Solution Approach 1:
The system implements periodic data sampling at optimized intervals rather than continuous monitoring. Sensors collect data at specific time points during training exercises, transmitting information in periodic bursts to the processing unit. This approach maintains sufficient analysis productivity while significantly reducing energy consumption compared to continuous real-time processing.
Solution Approach 2:
The sensor units are equipped with onboard processing capabilities that perform preliminary data filtering and validation before transmission. Each sensor unit autonomously determines which data points meet transmission criteria, reducing unnecessary data transmission and processing energy consumption while maintaining high productivity for critical training parameters.
3Reliability
If comprehensive sensor coverage is provided, then reliability of training monitoring is improved, but ease of operation deteriorates
Solution Approach 1:
The sensor units feature self-calibration and automatic identification capabilities. When attached to the athlete's limbs, each sensor automatically detects its position and configures its monitoring parameters without manual intervention. The system performs self-diagnosis and calibration routines, ensuring reliable comprehensive coverage while eliminating complex setup procedures for operators.
Solution Approach 2:
The system dynamically adjusts monitoring parameters based on the detected training phase and intensity. Sensors automatically modify sampling rates, threshold values, and data transmission frequencies according to the current training context, maintaining high reliability across different exercise types while simplifying operation through adaptive behavior rather than manual configuration.
Data Source
AI summary
The present invention relates to a training system for martial arts and contact sports that has three basic groups for the proper functioning thereof. The first group consists of a series of sensors called limbs, since they are coupled to each of the four limbs of the individual who is training. The second group consists of a series of sensors that is called the ring or training area. The third group consists of two analytics applications with two functional levels, the data of the individual who is training and comparative data from groups of individuals.