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Related Experiment Video

Updated: Jul 10, 2026

A Detailed Protocol for Perspiration Monitoring Using a Novel, Small, Wireless Device

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Published on: November 24, 2016

Estimating Scalp Moisture in a Hat Using Wearable Sensors.

Haomin Mao¹, Shuhei Tsuchida², Tsutomu Terada¹

¹Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Hyogo, Kobe 657-8501, Japan.

Sensors (Basel, Switzerland)

|July 11, 2023

Summary

This study presents a wearable hat with sensors to estimate scalp moisture using machine learning. This affordable device helps monitor hair health and prevent issues like hair loss and dandruff.

Keywords:

machine learning scalp care scalp moisture content wearable sensor

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Area of Science:

Biomedical Engineering
Wearable Technology
Machine Learning Applications

Background:

Scalp moisture content significantly impacts hair quality, with dryness leading to hair loss and dandruff.
Continuous monitoring of scalp moisture is crucial for maintaining hair health.

Purpose of the Study:

To develop a cost-effective, hat-shaped device with wearable sensors for continuous scalp moisture estimation.
To evaluate the efficacy of machine learning models in predicting scalp moisture using sensor data.

Main Methods:

Development of a hat-shaped device integrated with wearable sensors for data collection.
Implementation and comparison of four machine learning models (two non-time-series, two time-series).
Data collection in a controlled environment for model training and validation.

Main Results:

Inter-subject evaluation using Support Vector Machine (SVM) achieved a Mean Absolute Error (MAE) of 8.50.
Intra-subject evaluation using Random Forest (RF) demonstrated an average MAE of 3.29 across all subjects.
The developed device provides a viable alternative to expensive moisture meters and professional scalp analyzers.

Conclusions:

The hat-shaped wearable device effectively estimates scalp moisture content using machine learning.
This technology offers an accessible and affordable solution for individuals to monitor their scalp health.
Continuous monitoring can aid in the early detection and prevention of scalp-related issues.