Wearable Accelerometer Activity Recognition Across Gym Equipment
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Solution Overview
Problem
Existing fitness tracking systems lack integration with gym equipment and wearable devices for accurate user authentication, data collection, and real-time activity classification, failing to adapt to new devices and activities.
Innovation Solution
A system integrating wearable accelerometers with gym equipment, biometric authentication, and machine learning models for real-time and batch data processing, allowing user association, data segmentation, and activity classification, with adaptive machine learning for new devices and activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable devices and gym equipment are integrated for comprehensive fitness tracking, then measurement precision and data accuracy are improved, but device complexity increases
Solution Approach 1:
The system divides the fitness tracking functionality into separate modules: wearable devices for motion detection, gym equipment for workout data, and a central processing system for integration. This segmentation allows each component to specialize in specific tasks while maintaining overall system accuracy without excessive complexity.
Solution Approach 2:
The system creates a universal platform that can work with multiple types of wearable devices and gym equipment through standardized APIs and protocols. This multi-functionality approach enables comprehensive tracking across different device types without proportionally increasing system complexity.
2Productivity
If machine learning models are used for real-time activity classification, then productivity and response time are improved, but use of energy and computational resources increase
Solution Approach 1:
The system applies partial machine learning processing by using simplified classification algorithms for real-time decisions and reserving more computationally intensive model training and updates for offline batch processing. This approach maintains real-time responsiveness while reducing continuous energy consumption.
Solution Approach 2:
The system performs intensive machine learning model updates and retraining periodically rather than continuously. Real-time inference uses pre-trained models with low computational overhead, while model improvement occurs during scheduled batch processing periods, balancing productivity with energy efficiency.
3Adaptability or versatility
If the system adapts to new devices and activities dynamically, then adaptability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system automatically detects new wearable devices and gym equipment through standardized identification protocols, automatically configures appropriate tracking parameters, and begins data collection without manual intervention. This self-service capability improves adaptability to new devices while minimizing the complexity of system configuration for users.
Solution Approach 2:
The system dynamically adjusts tracking parameters, sampling rates, and classification thresholds based on the detected device type and activity context. This parameter adaptation allows the system to optimize performance for each specific device-activity combination without requiring complex reconfiguration or additional hardware.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate, real-time fitness activity tracking, adaptable to new devices and activities, with comprehensive user profiles and performance evaluation.
Implementation Method 1
wearable accelerometer data
Data Source
AI summary
The present invention pertains to an advanced method for automatically detecting and recognizing wellness activities and kinetic movements using accelerometer data from wearable devices. This method involves creating a user profile, linking the user to their wearable device via specific identifiers (make, type, and unique device ID), and processing accelerometer data to develop and continuously update a supervised machine learning model. This model accurately identifies and classifies various physical activities by utilizing detailed device information and user preferences. Designed for scalability, the system integrates new wearable devices and models as they emerge. It is particularly effective in tracking activities across diverse environments, including homes and fitness centers. Moreover, it identifies and associates ICD-10 activity codes with the performed activities and integrates with Electronic Medical Records (EMR) systems. This method offers valuable applications for fitness centers, healthcare providers, government agencies, and payers interested in monitoring or promoting physical wellness through reliable activity tracking.