Vibration-Based Machine Learning for Personal Care Device Location
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Solution Overview
Problem
Location tracking in personal care devices is costly due to the use of expensive sensors like IMUs, and their accuracy is limited.
Innovation Solution
A processing system that utilizes a personal care device's vibration response, captured through sensors like accelerometers and machine-learning algorithms, to estimate its location within a user's body without requiring IMUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive sensors like IMUs are used for location tracking, then location tracking capability is achieved, but manufacturing cost increases
Solution Approach 1:
The patent replaces expensive mechanical inertial measurement units (IMUs) with a computational approach using vibration sensors and machine learning algorithms. The system captures vibration data from the personal care device and uses a trained machine learning model to estimate location, eliminating the need for costly IMU hardware while achieving comparable or superior location tracking accuracy.
Solution Approach 2:
The patent creates a virtual model of location information by training a machine learning algorithm on vibration data correlated with known locations. Instead of directly measuring location with expensive sensors, the system learns to predict location from vibration patterns, effectively creating a computational copy of the location determination function that is much cheaper to implement.
2Measurement precision
If expensive sensors like IMUs are used for location tracking, then location tracking capability is achieved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical IMU systems with a combination of simple vibration sensors and software-based machine learning processing. This substitution reduces hardware complexity while maintaining or improving location tracking accuracy through computational methods.
Solution Approach 2:
The patent makes existing vibration sensors serve multiple functions: they continue to monitor device operation and now also enable location tracking through machine learning. This multi-functionality eliminates the need for dedicated IMU hardware, reducing overall device complexity while achieving location tracking capability.
3Measurement precision
If IMUs are used to determine location, then location data is obtained, but accuracy is limited
Solution Approach 1:
The patent changes the parameter used for location determination from raw IMU measurements to vibration signal characteristics processed through machine learning. By transforming the input data and using advanced pattern recognition, the system achieves higher location determination accuracy and reliability compared to direct IMU-based methods.
Solution Approach 2:
The patent employs machine learning algorithms that learn from training data containing vibration patterns and corresponding locations. This feedback mechanism allows the system to continuously improve location estimation accuracy by recognizing patterns in vibration data that correlate with specific locations, overcoming the limitations of traditional IMU-based approaches.
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
Accurately determines the location of personal care devices with reduced costs and improved precision by leveraging vibration data and machine-learning algorithms.
Implementation Method 1
an actuator that causes the personal care device to vibrate while in use
Implementation Method 2
sensors that are conventionally included in personal care devices
Data Source
Figure 1~2
Figure 3~4
AI summary
A system and method for determining a location of a personal care device that vibrates during use. A dataset describing an operating parameter of the personal care device during vibration of the personal care device is received and processed to generate a spectrogram representative of the dataset. A machine-learning algorithm is used to estimate a location of the personal care device based on the spectrogram.