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Related Experiment Video

Updated: Feb 8, 2026

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

Transfer Learning for Improved Audio-Based Human Activity Recognition.

Stavros Ntalampiras¹, Ilyas Potamitis²

¹Music Informatics Laboratory, Department of Computer Science, Università degli Studi di Milano, via Comelico 39, 20135, Milan, Italy. stavros.ntalampiras@unimi.it.

|June 27, 2018

Summary

This summary is machine-generated.

This study introduces a novel data augmentation method using transfer learning to improve automated human activity recognition, especially for imbalanced audio datasets. The approach enhances models by leveraging data from similar sound events when limited data is available.

Keywords:

echo state network generalized audio recognition hidden Markov model multidomain features transfer learning

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Area of Science:

Signal Processing
Machine Learning
Pattern Recognition

Background:

Automated human activity recognition (HAR) often relies on audio event processing.
Limited audio data for specific activities leads to imbalanced datasets, hindering model performance.

Purpose of the Study:

To develop a novel data augmentation technique for HAR systems facing imbalanced audio datasets.
To improve the accuracy and robustness of HAR models with scarce data for certain activities.

Main Methods:

A transfer learning-based data augmentation approach is proposed.
It identifies statistically similar classes to those with limited data.
A multiple input, multiple output transformation learns to adapt data from related classes for under-represented ones.

Main Results:

The method effectively augments data for classes with limited audio samples.
It demonstrates improved performance across various generative recognition schemes.
Feature extraction from diverse domains (temporal, spectral, wavelet) is integrated.

Conclusions:

The proposed data augmentation strategy significantly enhances HAR performance on imbalanced datasets.
Transfer learning offers a viable solution for leveraging related data to model under-represented activities.
The framework's effectiveness is validated through extensive experimental evaluations.