Cycling Workout Detection via Speed Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current activity tracking systems are complex and require intense user engagement, often necessitating multiple devices or applications, and struggle to accurately differentiate between cycling and other activities like running or automotive transit using GPS speed data alone.
Innovation Solution
A health and fitness monitoring system that includes an electronic device with a GPS receiver and processor, which processes GPS data into speed segment streams, organizes them into minute buckets, and uses cycling-specific speed classifications to automatically detect cycling activities by determining patterns and thresholds, minimizing battery drain and differentiating between activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If GPS speed data alone is used to detect cycling activity, then the system is simple to implement, but it cannot accurately differentiate between cycling and other activities like running or automotive transit
Solution Approach 1:
The patent segments the GPS speed data into multiple speed segments and organizes them into minute buckets, dividing the continuous data stream into discrete analytical units. This segmentation enables detailed analysis of speed patterns throughout the day to distinguish cycling from other activities.
Solution Approach 2:
The patent introduces multiple dimensions of analysis beyond simple speed measurement, including time-based patterns, speed segment distributions, and temporal clustering. By analyzing data across multiple dimensions (speed, time, pattern recognition), the system achieves accurate activity differentiation without adding physical sensors.
2Adaptability or versatility
If activity tracking applications include multiple features and information to track, then the tracking capability is comprehensive, but the interface becomes complicated requiring intense user engagement
Solution Approach 1:
The system performs automatic activity detection and classification without requiring user input or manual configuration. The algorithm autonomously processes GPS data, identifies cycling activities, and provides tracking results, eliminating the need for users to navigate complex interfaces or configure tracking parameters.
Solution Approach 2:
The patent creates a universal activity tracking system that can detect multiple types of activities (cycling, running, automotive transit) using a single integrated algorithm. This multi-functional approach consolidates what would otherwise require multiple separate applications into one unified system.
3Measurement precision
If the system continuously processes GPS data to detect cycling patterns, then the detection accuracy is improved, but battery consumption increases
Solution Approach 1:
The patent processes GPS data in periodic minute buckets rather than continuously analyzing every data point in real-time. By organizing data into discrete time intervals and processing them in batches, the system maintains detection accuracy while reducing computational frequency and associated battery consumption.
Solution Approach 2:
The system applies partial processing by analyzing only the necessary portions of GPS data (speed segments within specific ranges) rather than processing all available data uniformly. This selective processing approach reduces computational load and energy consumption while maintaining sufficient detection accuracy.
Data Source
AI summary
Devices, systems, and methods can be used including receiving motion data including comparing time-stamped speed data with predetermined cycling speed ranges, categorizing the speed data into portions of a minute that indicate each of the predetermined cycling speed ranges, determining portions of a minute indicate a cycling activity has begun, determining portions of a later minute indicate a cycling activity has paused or ended, confirming a minimum time inactive indicating that a cycling activity has ended, and determining that a minimum time active has elapsed between the indication a cycling activity has begun and indication a cycling activity has ended such that a cycling activity is categorized as a cycling workout.