Body Part Identification Using Multi-Sensor Personal Care Signals
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Solution Overview
Problem
Existing hand-held devices lack accurate identification of the body part being treated, which is crucial for optimizing personal care operations and user guidance.
Innovation Solution
A hand-held device equipped with sensors that measure parameters such as skin contact, tone, and proximity, combined with a trained machine learning model (MLM) to analyze measurement signals over time, enabling precise identification of the body part being treated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and machine learning models are used to identify body parts, then measurement precision and identification accuracy are improved, but device complexity increases
Solution Approach 1:
The device segments the identification task by using multiple specialized sensors (skin contact sensor, skin tone sensor, proximity sensor) that each measure specific parameters independently. Their signals are then combined through a machine learning model to achieve accurate body part identification, resolving the contradiction by dividing the complex measurement function into simpler sensor components.
Solution Approach 2:
The hand-held device integrates multiple sensors and a machine learning model into a single multi-functional system that can identify different body parts (face, neck, ear, scalp, arm, leg) using combined sensor data, thereby achieving high identification accuracy while consolidating functionality into one device.
2Adaptability or versatility
If different attachments are provided for different body parts, then adaptability is improved, but device complexity and user operation become more complicated
Solution Approach 1:
The device automatically identifies the body part being treated by analyzing sensor measurements through a machine learning model, eliminating the need for users to manually select attachments or configure settings. The system self-determines the appropriate treatment parameters based on detected body part characteristics, improving ease of operation while maintaining adaptability.
Solution Approach 2:
Instead of requiring users to select attachments based on body parts, the invention inverts the approach by having the device automatically detect body parts and adapt treatment accordingly, using sensor data and machine learning to determine appropriate settings without user intervention.
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
The solution allows continuous and accurate identification of body parts during personal care operations, enhancing device operation customization and user guidance, and improving treatment scheduling and personalization.
Implementation Method 1
a skin tone sensor, and the parameter measured by the skin tone sensor is indicative of a skin tone of skin that is adjacent to the skin that the head portion is in contact with
Implementation Method 2
a proximity sensor, and the parameter measured by the proximity sensor is indicative of a distance from the proximity sensor to skin adjacent to the skin that the head portion is in contact with
Data Source
AI summary
A computer-implemented method of identifying a subject's body part on which a hand-held device is used to perform a personal care operation. The hand-held device comprises a head portion for contacting the subject's skin one or more sensors for measuring a parameter and generating measurement signals as the hand-held device is used on the subject's skin. The sensor(s) comprise(s) at least one of a skin contact sensor, skin tone sensor, and/or proximity sensor. The method includes obtaining a measurement signal from each sensor; analyzing the obtained measurement signal using a trained machine learning model (MLM) to identify the subject's body part that the hand-held device is used on. The trained MLM analyzes a plurality of measurements in a time window on each of the obtained measurement signals to identify the body part. An indication of the identified body part is then outputted.