Effective Human Activity Recognition Based on Small Datasets

04/29/2020
by   Bruce X. B. Yu, et al.
13

Most recent work on vision-based human activity recognition (HAR) focuses on designing complex deep learning models for the task. In so doing, there is a requirement for large datasets to be collected. As acquiring and processing large training datasets are usually very expensive, the problem of how dataset size can be reduced without affecting recognition accuracy has to be tackled. To do so, we propose a HAR method that consists of three steps: (i) data transformation involving the generation of new features based on transforming of raw data, (ii) feature extraction involving the learning of a classifier based on the AdaBoost algorithm and the use of training data consisting of the transformed features, and (iii) parameter determination and pattern recognition involving the determination of parameters based on the features generated in (ii) and the use of the parameters as training data for deep learning algorithms to be used to recognize human activities. Compared to existing approaches, this proposed approach has the advantageous characteristics that it is simple and robust. The proposed approach has been tested with a number of experiments performed on a relatively small real dataset. The experimental results indicate that using the proposed method, human activities can be more accurately recognized even with smaller training data size.

READ FULL TEXT

page 1

page 2

page 4

page 6

research
11/05/2022

Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?

Skeleton-based Motion Capture (MoCap) systems have been widely used in t...
research
03/25/2019

Few-Shot Learning-Based Human Activity Recognition

Few-shot learning is a technique to learn a model with a very small amou...
research
07/12/2017

Deep Learning for Sensor-based Activity Recognition: A Survey

Sensor-based activity recognition seeks the profound high-level knowledg...
research
05/27/2019

A Platform to Collect, Unify, and Distribute Inertial Labeled Signals for Human Activity Recognition

Human activity recognition (HAR) is a very active research field. Recent...
research
01/23/2021

B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows

Human Activity Recognition (HAR), based on machine and deep learning alg...
research
01/17/2022

Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition

Several techniques have been proposed to address the problem of recogniz...
research
05/04/2023

Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity Recognition

The development of robust, generalized models in human activity recognit...

Please sign up or login with your details

Forgot password? Click here to reset