Athletic Activity Sensor Data Processing for Pace Measurement
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Solution Overview
Problem
Current athletic activity monitoring systems using accelerometers face challenges in accurately determining contact time and pace, especially in varying environments and user conditions, and often require frequent calibration, with limitations in measuring pace below certain speeds.
Innovation Solution
The system processes foot-based sensor data using techniques such as identifying event triplets, filtering outliers, and employing Fast Fourier Transform methodologies to calculate contact time and subsequently determine pace, speed, and distance, while also correlating effort with sensor output magnitude.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If accelerometer-based algorithms are used to determine contact time and pace, then athletic activity measurement is enabled, but measurement precision varies significantly between different environments and users
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the threshold values used to detect foot strike events based on the magnitude of acceleration signals. The system monitors the acceleration magnitude and adapts the detection threshold accordingly, allowing accurate contact time measurement across varying environments and user conditions without requiring frequent recalibration. This resolves the contradiction by making the measurement system adaptable to different environments while maintaining precision.
2Measurement precision
If accelerometer-based systems are used to measure pace, then athletic metrics can be tracked, but the systems require frequent calibration and re-calibration to maintain accuracy
Solution Approach 1:
The patent implements self-service through automatic threshold adaptation that eliminates the need for frequent manual calibration. The system autonomously monitors acceleration signal characteristics and adjusts detection parameters in real-time based on the user's current activity state. This self-adjusting mechanism maintains measurement accuracy without requiring user intervention or calibration time, resolving the contradiction between precision and calibration time.
3Speed
If traditional accelerometer algorithms are used, then basic pace measurement is possible, but measurement is only able to occur when user is running or moving above a certain speed
Solution Approach 1:
The patent applies dynamics by making the detection threshold adaptive rather than fixed. The system dynamically adjusts the threshold based on the real-time magnitude of acceleration signals, enabling accurate detection of foot strike events across a wide range of speeds including walking, jogging, and running. This dynamic adaptation allows the system to measure pace in diverse activity types rather than being limited to high-speed running, resolving the contradiction between speed range and activity coverage.
Data Source
AI summary
Determining pace or speed based on sensor data may include determining an amount of contact time a user's foot has with a workout surface such as the ground. Contact time may be determined by identifying samples in the sensor data that correspond to various events such as a heelstrike, a toe-off and a subsequent heelstrike. In one example, these events may be identified by determining a sequence of three sample values (e.g., a triplet) that exceeds corresponding thresholds. The validity of an identified triplet (e.g., heelstrike, toe-off and heelstrike) may be confirmed by determining whether a difference between a last event sample and a middle event sample is greater than a difference between the middle event sample and an initial event sample. Once confirmed, a contact time may be determined from the triplet. A linear or non-linear relationship may then be applied to the contact time to determine a speed or pace.