Open Access

ARTICLE

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

by Majdy M. Eltahir¹, Adil Yousif², Fadwa Alrowais³, Mohamed K. Nour⁴, Radwa Marzouk⁵, Hatim Dafaalla⁶, Asma Abbas Hassan Elnour⁶, Amira Sayed A. Aziz⁷, Manar Ahmed Hamza^8,*

1 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
2 Faculty of Arts and Science, Najran University, Sharourah, Saudi Arabia
3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
6 Department of Computer Science, College of Applied Sciences, King Khalid University, Muhayil, 63772, Saudi Arabia
7 Department of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt
8 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2023, 75(2), 3239-3255. https://doi.org/10.32604/cmc.2023.034037

Received 05 July 2022; Accepted 13 October 2022; Issue published 31 March 2023

Abstract

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon installation and operation are high, whereas the technology is relevant only for indoor environments. Currently, commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements. Against this background, the current study develops an Improved Whale Optimization with Deep Learning-Enabled Fall Detection for Disabled People (IWODL-FDDP) model. The presented IWODL-FDDP model aims to identify the fall events to assist disabled people. The presented IWODL-FDDP model applies an image filtering approach to pre-process the image. Besides, the EfficientNet-B0 model is utilized to generate valuable feature vector sets. Next, the Bidirectional Long Short Term Memory (BiLSTM) model is used for the recognition and classification of fall events. Finally, the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method, which shows the novelty of the work. The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

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Keywords

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