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Optimizing Sensor Position with Virtual Sensors in Human Activity Recognition System Design.

Chengshuo Xia¹, Yuta Sugiura²

¹Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan.

Sensors (Basel, Switzerland)

|October 26, 2021

Summary

This study introduces a novel method for optimizing sensor placement in human activity recognition (HAR) systems. It uses virtual data to find ideal positions, reducing training costs and improving accuracy.

Keywords:

activity recognition human–computer interaction optimization virtual sensor

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Published on: December 11, 2015

Area of Science:

Computer Science
Machine Learning
Sensor Networks

Background:

Human Activity Recognition (HAR) systems typically rely on fixed sensor positions.
Variations in sensor placement significantly impact HAR system performance and necessitate new training datasets.
Optimizing sensor position is crucial for efficient HAR system design.

Purpose of the Study:

To design an optimization scheme for determining optimal sensor positions in HAR systems.
To leverage virtual sensor data for cost-effective dataset generation.
To enhance the accuracy and reduce the cost of HAR system training and deployment.

Main Methods:

Developed an optimization scheme utilizing virtual sensor data.
Enabled generation of optimal sensor positions for a given number of sensors.
Implemented a feedback mechanism for accurate sensor position selection.

Main Results:

The system can identify optimal sensor positions from all possible locations.
Virtual sensor data allows for low-cost access to training datasets.
The proposed method accurately aids sensor position selection and classifier output.

Conclusions:

The developed system effectively optimizes sensor placement in HAR.
Virtual sensor data significantly reduces the cost of HAR system training.
This approach offers a more accurate and cost-efficient alternative to conventional HAR training models.