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

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Energy expenditure prediction using a miniaturized ear-worn sensor.

Louis Atallah¹, Julian J H Leong, Benny Lo

¹Institute of Biomedical Engineering, Imperial College London, and University College Hospital, London, United Kingdom.

Medicine and Science in Sports and Exercise

|January 5, 2011

Summary

A novel ear-worn sensor accurately predicts human energy expenditure and activity type using a pattern recognition algorithm. This lightweight device offers reliable, non-intrusive monitoring for various daily activities.

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Area of Science:

Biomedical Engineering
Wearable Technology
Human Physiology

Background:

Accurate measurement of human energy expenditure is crucial for health and performance monitoring.
Existing methods for tracking physical activity and energy expenditure can be cumbersome or intrusive.
Novel sensor technologies are needed for unobtrusive and continuous assessment of daily activities.

Purpose of the Study:

To develop and validate a miniature, ear-worn inertia sensor for predicting human energy expenditure.
To utilize a novel pattern recognition algorithm for accurate activity detection and classification.
To assess the sensor's performance in both task-known and task-blind energy expenditure estimations.

Main Methods:

A protocol involving 11 diverse activities of daily living was established.
25 healthy subjects (18 males, 7 females) participated in the study.
An ear-worn inertia sensor recorded posture and acceleration, while indirect calorimetry (Cosmed K4b) served as the gold standard for energy expenditure measurement.

Main Results:

The proposed algorithm demonstrated good agreement between predicted and measured METs (Metabolic Equivalents) with low systematic bias across all tested activities.
Task-blind prediction also showed good agreement, with an overall activity recognition success rate of 88.99%.
Individual activity classification rates were high, particularly for activities like standing/computer work (99.10%) and running (98.96%).

Conclusions:

The developed ear-worn sensor is a lightweight, novel device for predicting energy expenditure.
The system effectively predicts energy expenditure across a range of activities.
This technology allows for non-interfering and non-modifying monitoring of behavior and energy expenditure.