Hearing Instrument Battery Life Estimation via Feature Duty Cycles
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Solution Overview
Problem
Determining the battery life of hearing instruments is challenging due to varying feature duty cycles and power consumption rates, especially when features are not tailored to the user's specific needs, leading to unnecessary battery drain.
Innovation Solution
A system and method that determine a feature duty cycle for each feature of hearing instruments based on user data, such as questionnaire answers and historical usage, to calculate battery life by estimating energy costs and optimizing feature usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple features are activated in hearing instruments, then device functionality and user experience are improved, but battery life is reduced due to increased power consumption
Solution Approach 1:
The system dynamically adjusts feature duty cycles based on real-time analysis of user data, historical usage patterns, and environmental conditions. Each feature's activation status is continuously optimized to balance functionality with power consumption, allowing the device to adapt its feature set rather than maintaining static on/off states
Solution Approach 2:
The system changes operational parameters by adjusting duty cycles for different features based on calculated energy costs. By modifying the duty cycle parameter for each feature individually, the system optimizes the balance between providing desired functionality and conserving battery power throughout the day
2Adaptability or versatility
If features are activated without tailoring to user's specific needs, then device versatility is maintained, but unnecessary battery drain occurs
Solution Approach 1:
The system applies different duty cycle settings to different features based on their individual energy costs and user-specific needs. Rather than uniformly enabling or disabling all features, each feature receives a customized activation schedule tailored to its importance to the user and its power consumption characteristics
Solution Approach 2:
The system automatically analyzes user data and historical usage patterns to determine which features should be activated and when, without requiring manual user configuration. The device self-optimizes its feature activation strategy based on learned user behavior and environmental conditions
3Measurement precision
If accurate battery life estimation is achieved through detailed feature analysis, then battery management is improved, but system complexity increases
Solution Approach 1:
The system segments the battery life estimation problem by analyzing each feature independently with its own duty cycle and energy cost characteristics. By breaking down the overall power consumption into discrete feature-level components, the system achieves accurate total energy cost calculation through summation of individual feature costs
Data Source
AI summary
A system may obtain data related to hearing instruments, such as data indicating answers of a user to a questionnaire or historical usage data of the hearing instruments. For each respective feature of one or more features, the system may determine a feature duty cycle corresponding to an amount of time during a period in which the respective feature is anticipated to be active based on the data related to the hearing instruments. The system may further determine an energy cost for the respective feature at least based on the respective feature duty cycle for the respective feature and a power consumption rate of the respective feature. The system may calculate a battery life of one or more batteries in the hearing instruments at least based on the energy costs for each feature of the one or more features.