Activity Classification via Sensor Template Matching
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Solution Overview
Problem
Individuals face challenges in maintaining regular exercise programs, particularly those involving repetitive motions, and existing systems often fail to motivate users by separating athletic activities from daily life, leading to decreased interest and participation.
Innovation Solution
A system utilizing accelerometers and other sensors to classify user activities through template matching, allowing for personalized feedback and motivation by monitoring and analyzing athletic movements, thereby integrating exercise into daily life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exercise is separated from daily life as a distinct chore, then athletic activity can be monitored and measured, but user motivation and engagement decrease
Solution Approach 1:
The patent merges athletic activity monitoring with daily life activities by using sensors to detect and classify both exercise and non-exercise movements through a unified template matching system, eliminating the separation between sport and daily life
Solution Approach 2:
The system provides universal activity recognition that works for multiple types of activities (running, walking, cycling, swimming, and daily life movements) using the same sensor platform and classification algorithms, making the system adaptable to various user needs
2Measurement precision
If activity monitoring focuses on specific athletic activities, then measurement accuracy improves, but user interest and participation decrease due to narrow scope
Solution Approach 1:
The system employs universal template matching algorithms that can recognize multiple activity types (running, walking, cycling, swimming, and daily life movements) with comparable accuracy, expanding the activity range while maintaining measurement precision
Solution Approach 2:
The classification system dynamically adapts to different activity types and user behaviors by adjusting template parameters and thresholds based on detected movement patterns, allowing accurate recognition across diverse activity ranges
3Measurement precision
If repetitive exercise motions are monitored in detail, then activity classification accuracy improves, but user motivation decreases due to monotony
Solution Approach 1:
The system provides automated feedback through activity classification results, performance metrics, and personalized recommendations that acknowledge user achievements and suggest variations, transforming monotone repetitive exercise into engaging challenges with measurable progress
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
Enhances user engagement and accuracy in activity classification, improving motivation and participation in physical activities by providing personalized feedback and integrating exercise into daily routines.
Implementation Method 1
One or more devices may use an accelerometer and/or other sensors to monitor activity of a user
Data Source
AI summary
Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.