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Artificial Intelligence-Based System for Detecting Attention Levels in Students

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Using accelerometers for physical actions recognition by a neural fuzzy network.

Shing-Hong Liu¹, Yuan-Jen Chang

¹Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan. shliu@cyut.edu.tw

Telemedicine Journal and E-Health : the Official Journal of the American Telemedicine Association

|November 19, 2009

Summary

This study used triaxial accelerometers and a neural fuzzy network to recognize human actions like walking, sitting, and falling. Optimal sensor placement on the abdomen achieved high accuracy in classifying these physical activities.

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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

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Published on: April 21, 2017

Area of Science:

Biomedical Engineering
Human-Computer Interaction
Wearable Technology

Background:

Monitoring human actions is crucial for healthcare and human-computer interaction.
Accurate classification of physical activities using wearable sensors remains a challenge.
Optimizing sensor placement and classification algorithms is key for reliable human activity recognition.

Purpose of the Study:

To determine optimal sensor placement and classification schemes for recognizing human physical actions.
To evaluate the effectiveness of triaxial accelerometers and a specific neural network for activity recognition.
To assess the accuracy of classifying walking, sitting down, and falling actions.

Main Methods:

Three triaxial accelerometers were placed on the chest, waist, and thigh.
Features were extracted from accelerometer signals using an autoregression (AR) model.
A self-constructing neural fuzzy inference network (SONFIN) was employed for action classification.

Main Results:

The SONFIN achieved a stable total classification accuracy of 96.3% for training data and 88.7% for testing data.
Optimal performance was observed when accelerometers were placed on the abdomen.
Using a 60-order AR model for feature extraction yielded high accuracy.

Conclusions:

The SONFIN, combined with AR model features and abdominal accelerometer placement, is effective for recognizing basic human physical actions.
This approach demonstrates potential for reliable human activity monitoring systems.
Further validation with diverse populations, including elderly individuals (80.4% confirmation), is warranted.