Wearable Exercise Intensity Assessment Beyond Lab-Based Monitoring
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Solution Overview
Problem
Existing exercise monitoring systems are cumbersome and limited to lab-based settings, making it difficult for the general population to access accurate exercise metrics.
Innovation Solution
A personal training system that utilizes a network of interconnected devices, including body area networks, local area networks, and wide area networks, to collect and analyze athletic data from sensors worn by users, providing real-time feedback on exercise intensity and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If formal lab-based settings with cumbersome equipment are used to monitor exercise metrics, then measurement precision is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The monitoring system is divided into multiple independent sensor components (accelerometer, gyroscope, magnetometer, barometer) that can be distributed across different devices worn by the user. Each sensor independently collects specific physiological or motion data, and the processor integrates these segmented data streams to reconstruct comprehensive exercise metrics, thereby reducing the complexity of any single device while maintaining measurement precision.
Solution Approach 2:
A processor acts as an intermediary between the various sensors and the final exercise metric output. The processor receives raw data from multiple sensor sources, applies algorithms to interpret and integrate this data, and generates accurate exercise metrics without requiring complex laboratory equipment. This intermediary processing layer simplifies the overall system architecture while preserving measurement accuracy.
2Measurement precision
If formal lab-based settings with cumbersome equipment are used to monitor exercise metrics, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The sensor assembly is designed to be universally applicable across multiple exercise activities and contexts. The same set of sensors (accelerometer, gyroscope, magnetometer, barometer) can monitor various types of exercise including running, cycling, swimming, and strength training. This multi-functionality allows the system to maintain measurement precision across diverse applications while being easy to operate, as users simply wear the device without needing to learn complex laboratory procedures.
Solution Approach 2:
The system automatically processes sensor data and generates exercise metrics without requiring user intervention or manual calibration. The processor continuously analyzes data from the sensors and provides real-time feedback on exercise intensity, duration, and type. This self-service capability eliminates the need for users to interact with complex equipment or understand measurement principles, thereby improving ease of operation while maintaining accuracy.
3Productivity
If multiple sensors are integrated into clothing and accessories for continuous monitoring, then productivity is improved, but use of energy worsens
Solution Approach 1:
The processor periodically samples data from the sensors rather than continuously processing all sensor outputs at maximum rate. During steady-state exercise conditions, the sampling rate is reduced, while during transitions or intense activity, the sampling frequency increases. This periodic action allows the system to maintain effective exercise monitoring productivity while significantly reducing average power consumption compared to continuous high-rate sampling.
Solution Approach 2:
The system discards redundant or low-value sensor data that does not contribute meaningfully to exercise metric calculation. For example, during certain types of exercise, magnetometer data may be discarded as it provides minimal additional information beyond what accelerometers and gyroscopes already capture. This selective discarding of unnecessary data reduces processing requirements and energy consumption while maintaining monitoring efficiency for the essential exercise parameters.
Data Source
AI summary
Aspects relate to systems, methods, and apparatuses relating to a portable device that may be used to identify a critical intensity and an anaerobic work capacity of an individual. The device may utilize muscle oxygen sensor data, speed data, or power data. The device may utilize data from multiple exercise sessions, or may utilize data from a single exercise session. The device may additionally estimate a critical intensity from a previous race time input from a user.