Wearable Device Walking Workout Detection via Mechanical Work
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Solution Overview
Problem
Existing wearable devices struggle to accurately distinguish between outdoor walking workouts and casual walking activities due to similar walking motions and varying levels of exertion, making it challenging to infer user intent effectively.
Innovation Solution
The wearable device employs motion data from sensors like accelerometers and gyroscopes, combined with GPS and pressure data, to estimate mechanical work and detect patterned movements, sending notifications for user confirmation of workout start and end, using a mechanical work threshold and movement pattern analysis to differentiate between workout and casual walking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the wearable device uses simple motion detection to identify walking activities, then the device complexity is reduced, but the accuracy of distinguishing workouts from casual walking deteriorates
Solution Approach 1:
The patent changes multiple parameters simultaneously: introduces mechanical work threshold, adds movement pattern classification criteria, incorporates bout duration requirements, and uses direction change analysis. These parameter changes transform simple step counting into a multi-dimensional detection system that accurately distinguishes workouts from casual walking without requiring complex machine learning algorithms
Solution Approach 2:
The patent adds new dimensions to the detection problem: mechanical work dimension (using barometric pressure and step data), movement pattern dimension (analyzing direction changes and bout structure), and temporal dimension (requiring minimum bout duration). This multi-dimensional approach enables accurate workout detection while keeping the algorithm computationally efficient
2Measurement precision
If the wearable device uses multiple sensors and calculations to improve workout detection accuracy, then the measurement precision is improved, but the use of energy by the device increases
Solution Approach 1:
The detection process is segmented into distinct stages: step detection phase (low power), bout detection phase (moderate power), mechanical work calculation phase (higher power), and classification phase (moderate power). By segmenting the processing, the device can manage energy consumption more effectively while maintaining high detection accuracy
Solution Approach 2:
The device uses its existing sensor suite (accelerometer, barometric pressure sensor, GPS) for workout detection purposes, rather than requiring additional dedicated sensors. This self-service approach leverages already-power-consuming components for dual purposes, avoiding the energy cost of adding new hardware
3Reliability
If the wearable device requires user confirmation for workout start, then the reliability of workout tracking is improved, but the ease of operation deteriorates
Solution Approach 1:
The system provides feedback to the user by sending a notification when it detects the start of a workout. This feedback loop allows the user to confirm or deny the detected workout, ensuring high reliability while maintaining ease of operation through automatic detection with optional confirmation rather than requiring manual input for every workout
Data Source
AI summary
Disclosed embodiments include wearable devices and techniques for detecting walking workouts. By accurately and promptly detecting the start of walking workouts activities and automatically distinguishing between walking workout and causal walking activities, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording walking workouts.