Heat Consumption Estimation Using Motion Stage Segmentation
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Solution Overview
Problem
Existing methods for estimating heat consumption during motion processes, such as those used in wearable devices, are not accurate enough, particularly in complex motion scenarios like cross-country running and mountaineering, due to the use of single linear regression models that fail to consider the different stages of motion.
Innovation Solution
A heat consumption estimation method that determines the motion state feature information of a user, distinguishing between motion steady-state and recovery stages, and calculates total heat consumption based on this information and the user's heart rate, using a combination of heart rate decline analysis and physiological characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single linear regression model is used to estimate heat consumption, then the estimation process is simple, but the accuracy is insufficient especially in complex motion scenarios
Solution Approach 1:
The motion process is segmented into different stages (steady-state stage and recovery stage) based on heart rate changes. Different linear regression models are applied to each stage, with the steady-state stage using one model and the recovery stage using another model with different parameters. This segmentation allows the system to capture the distinct physiological characteristics of each motion phase, thereby improving overall estimation accuracy without requiring a single complex model to handle all scenarios.
2Reliability
If motion stages are not considered, then the estimation method is simple, but estimation errors increase in complex motion scenarios
Solution Approach 1:
The system continuously monitors heart rate and uses feedback to detect transitions between motion stages. When the heart rate decreases below a threshold value, the system switches from the steady-state model to the recovery stage model. This feedback mechanism ensures that the appropriate model is applied at the appropriate time, improving reliability by adapting to changing motion conditions in real-time without requiring overly complex predictive algorithms.
Data Source
AI summary
A heat consumption estimation method includes: obtaining the heart rate of a target user at a current moment; determining motion state feature information of the target user at the current moment, where the motion state feature information is configured to represent whether the target user is in a motion steady-state stage or a motion recovery stage at the current moment; and determining, according to the motion state feature information and the heart rate at the current moment, total heat consumption information of the target user in a motion process, where the motion process includes a process from a motion starting moment to the current moment.