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Related Experiment Video

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

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

Published on: December 11, 2015

A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition.

Bandar Almaslukh¹, Abdel Monim Artoli², Jalal Al-Muhtadi³

¹Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia. 434108029@student.ksu.edu.sa.

Sensors (Basel, Switzerland)

|November 4, 2018

Summary

This study introduces a deep learning model for position-independent human activity recognition (HAR) using smartphones. The new model significantly improves HAR accuracy and position detection, enabling reliable real-time applications.

Keywords:

convolution neural networks deep learning human activity recognition position detection position-independent smartphone

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Area of Science:

Computer Science
Machine Learning
Signal Processing

Background:

Modern smartphones offer cost-effective platforms for human activity recognition (HAR) using embedded sensors like accelerometers and gyroscopes.
Existing HAR systems are often position-dependent, requiring users to hold devices in specific orientations, limiting practical application.
Current position-independent HAR methods struggle with reliability and performance.

Purpose of the Study:

To develop a robust, position-independent human activity recognition system using deep learning.
To enhance the accuracy and reliability of HAR on smartphones regardless of device placement.
To evaluate the proposed model's performance against state-of-the-art methods and assess its real-time applicability.

Main Methods:

A deep convolution neural network (CNN) model was designed for position-independent HAR.
The model was trained and evaluated using the public RealWorld HAR dataset.
Performance metrics included overall HAR accuracy, position detection accuracy, and recognition time.

Main Results:

The proposed deep learning model achieved an 88% performance for position-independent HAR, an improvement from 84% with traditional methods.
Position detection accuracy significantly improved from 89% to 98%.
The model demonstrated efficient recognition times, validating its suitability for real-time applications.

Conclusions:

The developed deep convolution neural network model offers a robust solution for position-independent human activity recognition.
The system demonstrates superior performance in both activity recognition and position detection compared to existing approaches.
The model's efficiency makes it a viable option for real-time HAR applications on smartphones.