TY  - JOUR
TI  - Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models
IS  - 5
N2  - 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.
ID  - unincol27968
KW  - deep learning; human activity recognition; LSTM; PoseNet; skeleton-based recognition; smart home;
            Transformer
JF  - Sensors
Y1  - 2026/02//
SN  - 1424-8220
A1  - Raza, Muhammad Amjad
A1  - Mehmood, Nasir
A1  - Siddiqui, Hafeez Ur Rehman
A1  - Saleem, Adil Ali
A1  - Álvarez, Roberto Marcelo
A1  - Miró Vera, Yini Airet
A1  - Díez, Isabel de la Torre
UR  - http://doi.org/10.3390/s26051516
AV  - public
VL  - 26
ER  -