Cloud Sports Analytics Platform with Wearable Sensor Integration
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Solution Overview
Problem
Current sports data collection and analytics systems are limited by manual data entry, insufficient viewing angles, and lack of real-time, customizable, and intelligent data analysis, particularly in competitive sports, where timely and accurate data is crucial for player performance, team strategy, and medical assessments.
Innovation Solution
A cloud-based platform that aggregates and synchronizes data from wearable sensors, cameras, and other sources, providing real-time analytics and intelligence to various stakeholders, including coaches, trainers, and medical staff, through API access and push notifications, using RFID technology for accurate player tracking and integrating disparate data sources for comprehensive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry is used to collect sports statistics, then data collection is simple to implement, but data collection efficiency is low and real-time analytics are not achieved
Solution Approach 1:
The system uses automated sensor devices worn by athletes that self-collect and transmit performance data without requiring manual data entry. The sensors automatically measure metrics such as heart rate, speed, distance, and other physiological parameters, eliminating the need for human operators to manually record statistics while maintaining simple implementation through off-the-shelf sensor integration
Solution Approach 2:
The patent replaces manual mechanical data collection methods with electronic sensor-based automated systems. Wearable sensors with processors and transmitters substitute for human observers and manual recording, enabling real-time wireless transmission of performance data to coaches and analytics systems without mechanical intervention
2Measurement precision
If traditional video recording techniques are used for injury detection, then equipment requirements are simple, but viewing angles are insufficient and real-time monitoring is not achieved
Solution Approach 1:
The wearable sensor devices perform multiple functions including performance tracking, injury detection, and physiological monitoring simultaneously. The same sensors that measure speed and distance also detect impact forces and abnormal movement patterns that indicate injuries, eliminating the need for separate specialized equipment while improving detection accuracy
Solution Approach 2:
The system uses processors and algorithms as intermediaries to analyze raw sensor data and automatically identify injury events. The processors interpret complex sensor signals to detect impact forces, abnormal gait patterns, and other indicators of injury, providing accurate real-time detection without requiring complex manual video analysis
3Measurement precision
If comprehensive sensor data collection is implemented, then data accuracy and real-time analytics are improved, but data synchronization and integration from multiple sources become complex
Solution Approach 1:
The system merges data from multiple sensor sources (accelerometers, gyroscopes, heart rate monitors, GPS) into a unified data stream processed by centralized processors. The system combines performance data, physiological data, and location data from various sensors worn by different athletes, synchronizing them through time stamps and player identification to create integrated real-time analytics
4Speed
If real-time data transmission is implemented, then decision-making speed is improved, but energy consumption of wearable devices increases
Solution Approach 1:
The sensor devices transmit data periodically rather than continuously, sending performance metrics at regular intervals (e.g., every second or every few seconds) rather than maintaining constant real-time transmission. This periodic transmission maintains adequate decision-making speed for coaching and medical personnel while significantly reducing energy consumption compared to continuous streaming
Data Source
AI summary
Systems and methods for integrated automated sports data collection and analytics are disclosed. Different types of data, for example but not limited, location data, movement data, impact data and biometric data for individual players are collected via wearable sensors in real time during a sports activity and transmitted to a cloud-based platform together with other sports data, including video, timing, scoring, statistics, and events with time code. The cloud-based platform is operable to aggregate, correlate, organize and synchronize various data related to the sports activity; store, query and retrieve various live data and historical data in and from a proprietary database; and perform analytics and provide intelligence to different parties involved in a sports activity, including coaches, trainers, medical staff, live announcers, broadcasters, displays, viewers, and fans and etc. These different parties may subscribe to licensed access to the cloud-based platform for tailored data feeds with real time push.