Wireless Earbud Wear Detection Using Motion and Neural Networks
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Solution Overview
Problem
Conventional wireless earphone wearing status detection methods suffer from high false detection rates in complex scenarios due to interference, such as when the earphones are in pockets or held by hands, as they rely solely on contact and blockage status.
Innovation Solution
Utilizing a sensor system with acceleration sensors to detect the moving and vibration status of the earphone housing, combined with a neural network model to analyze the sensor outputs, to accurately distinguish between actual wearing and interference scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If optical sensing is used to detect wearing status, then the detection is simple and non-contact, but the false detection rate increases in complex scenarios
Solution Approach 1:
The patent combines optical sensing with acceleration sensing to detect wearing status. The optical sensor detects blockage status while the acceleration sensor detects movement characteristics, and the results are fused through a neural network model to achieve more accurate wearing status recognition, resolving the contradiction between simple detection and low false detection rate
Solution Approach 2:
The patent introduces an acceleration sensor as an intermediary to provide additional movement information. This intermediary sensor captures motion characteristics that help distinguish between actual wearing and false detection scenarios (such as earphones being held or placed in pockets), thereby reducing false detection rate while maintaining operational simplicity
2Device complexity
If contact status and blockage status are used to determine wearing state, then the detection method is simple, but the accuracy decreases in interference scenarios
Solution Approach 1:
The patent merges optical sensing (for blockage status) with acceleration sensing (for movement status) and processes the combined data through a neural network model. This fusion of multiple sensing modalities significantly improves wearing status accuracy in interference scenarios while keeping the overall system architecture relatively simple
Solution Approach 2:
The patent changes the detection parameters from simple binary states (contact/blockage) to multi-dimensional features including movement characteristics captured by acceleration sensors. By transforming the detection space to include temporal and motion dimensions, the system achieves higher measurement precision without substantial complexity increase
3Reliability
If acceleration sensing is added to improve detection accuracy, then the false detection rate decreases, but the device complexity increases
Solution Approach 1:
The patent makes the acceleration sensor serve multiple functions: detecting movement status, characterizing motion patterns, and providing data for neural network inference. By maximizing the utility of the added sensor, the system achieves improved reliability while minimizing the increase in device complexity
Solution Approach 2:
The patent replaces complex mechanical or multiple sensor solutions with a neural network-based processing approach. Instead of using additional mechanical sensors or complex signal processing circuits, the system uses software-based neural networks to fuse data from existing sensors, achieving high accuracy without proportional increases in hardware complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of identifying the wearing status of wireless earphones by differentiating between genuine wearing and interference scenarios, reducing false detection rates.
Implementation Method 1
obtaining a first output of the sensor system, where the first output indicates a moving status of the housing
Data Source
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AI summary
This application discloses a method for determining a wearing status of a wireless earphone and a wireless earphone. The method includes: obtaining a first output of a sensor system, where the first output indicates a moving status of a housing, and determining, based on the first output, whether a body portion is put in a user's ear. This application further discloses a method for determining a double-tapped status of a wireless earphone and a wireless earphone. The method includes: obtaining a first output of a sensor system, where the first output indicates a moving status of a housing, and determining, by using a neural network model and by using the first output as a model input, whether the housing is double-tapped by an external object. This application improves accuracy of identifying the wearing status of the wireless earphone and improves accuracy of identifying the double-tapped status of the wireless earphone.