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MYNursingHome: A fully-labelled image dataset for indoor object classification.

Asmida Ismail^1,2, Siti Anom Ahmad^1,3, Azura Che Soh¹

¹Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia.

|September 28, 2020

Summary

This study introduces the MYNursingHome dataset, featuring 37,500 labeled images of 25 common indoor objects. This resource supports the development of AI-powered assistive technologies for elderly care and computer vision research.

Keywords:

Deep learning Image dataset Indoor objects Object classification Object detection

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning
Deep Learning
Gerontology

Background:

Reproducible research in AI requires large, annotated image datasets.
Aging research aims to improve elderly quality of life through technology.
Indoor object detection is crucial for autonomous systems in care settings.

Purpose of the Study:

To introduce the MYNursingHome dataset for indoor object recognition in elderly care environments.
To provide a valuable resource for training and testing computer vision models.
To facilitate the development of recognition aids for the elderly.

Main Methods:

Collected 37,500 digital images from 25 indoor object categories.
Images were sourced from multiple nursing homes in Malaysia.
Dataset is fully labeled for object detection and classification tasks.

Main Results:

The MYNursingHome dataset comprises 37,500 images.
It covers 25 distinct categories of objects commonly found in elderly care settings.
The dataset is suitable for training AI models for indoor object recognition.

Conclusions:

The MYNursingHome dataset is a significant contribution to AI research in elderly care.
It enables advancements in computer vision applications for assistive technologies.
This dataset will foster more robust and reliable AI systems for supporting the elderly.