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primary topic: Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models

Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models
type: AcademicArticle
type: Article
abstract: Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems.
date: 2026-02
volume: 26
issue: 5
is part of: Sensors
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models (Texto)
has document: Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models (Otro)
subject: Engineering

Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models (Texto)
type: Document
has file: sensors-26-01516-v2.pdf
has part: sensors-26-01516-v2.pdf

Sensors
type: Collection
issn: 14248220

Yini Airet Miró Vera
type: Person
given name: Yini Airet
family name: Miró Vera

Muhammad Amjad Raza
type: Person
given name: Muhammad Amjad
family name: Raza

Nasir Mehmood
type: Person
given name: Nasir
family name: Mehmood

Isabel de la Torre Díez
type: Person
given name: Isabel de la Torre
family name: Díez

Adil Ali Saleem
type: Person
given name: Adil Ali
family name: Saleem

Hafeez Ur Rehman Siddiqui
type: Person
given name: Hafeez Ur Rehman
family name: Siddiqui

Roberto Marcelo Álvarez
type: Person
given name: Roberto Marcelo
family name: Álvarez

Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models (Otro)
type: Document
is version of: Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models (Texto)
is volatile version of: Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models (Texto)
is index codes version of: Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #27968 Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models
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primary topic: Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models

Engineering
type: Concept
preferred label: Ingeniería @es