Machine learning identification of functional accessories in personal care devices
By combining multi-axis motion sensors and current sensors with machine learning models, handheld personal care devices can accurately identify and adjust operating parameters, solving the problem of inaccurate accessory type identification in existing technologies and improving the device's operational adaptability and user experience.
CN122422115APending Publication Date: 2026-07-17KONINKLIJKE PHILIPS NV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- KONINKLIJKE PHILIPS NV
- Filing Date
- 2024-12-17
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing handheld personal care devices struggle to accurately and automatically identify and differentiate between various types of functional accessories, leading to improper adjustment of operating parameters.
Method used
By combining multi-axis motion sensors and current sensors with a machine learning model, multi-axis spectral feature vectors are constructed to identify functional accessories coupled to the housing. The machine learning model is then used to generate control signals to adjust the operation of the equipment.
Benefits of technology
It enables highly accurate automatic identification of a wider range of functional accessories and appropriate adjustment of operating parameters, improving equipment efficiency and user experience.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN122422115A_ABST
Abstract
本文公开了一种手持个人护理设备（100），其具有壳体（200）和一组不同的功能附件（102、104、106、108、110），这些功能附件中可以各自被单独地耦合到壳体。该手持个人护理设备包括被布置在壳体内的电动马达（206）和电流传感器。它还包括在壳体中的多轴运动传感器（212）。机器可执行指令（220）的执行使计算系统（214）：激活（300）电动马达；从电流传感器接收（302）电流传感器数据（224）；从运动传感器接收（304）运动传感器数据（226）；从运动传感器数据计算（306）多轴频谱（228）；使用电流传感器数据和来自多轴频谱的至少两个轴的至少一个频率仓来构建（308）特征向量（230）；以及响应于将特征向量输入到机器学习模型（222）中，接收（310）描述该组不同的功能附件中的哪个功能附件（204）被实际耦合到壳体的控制信号（232）。
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Citation Information
Patent Citations
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