Mesfer Al Duhayyim^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2473-2489, 2023, DOI:10.32604/iasc.2023.033585 - 15 March 2023

Abstract Falls are the most common concern among older adults or disabled people who use scooters and wheelchairs. The early detection of disabled persons’ falls is required to increase the living rate of an individual or provide support to them whenever required. In recent times, the arrival of the Internet of Things (IoT), smartphones, Artificial Intelligence (AI), wearables and so on make it easy to design fall detection mechanisms for smart homecare. The current study develops an Automated Disabled People Fall Detection using Cuckoo Search Optimization with Mobile Networks (ADPFD-CSOMN) model. The proposed model’s major aim… More >

Madhuri Agrawal^*, Shikha Agrawal

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2653-2667, 2023, DOI:10.32604/iasc.2023.033493 - 15 March 2023

Abstract

Suspicious fall events are particularly significant hazards for the safety of patients and elders. Recently, suspicious fall event detection has become a robust research case in real-time monitoring. This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving backgrounds in an indoor environment; it is further proposed to use a deep learning method known as Long Short Term Memory (LSTM) by introducing visual attention-guided mechanism along with a bi-directional LSTM model. This method contributes essential information on the temporal and spatial locations of ‘suspicious fall’ events in learning the

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Sarah B. Basahel¹, Saleh Bajaba², Mohammad Yamin³, Sachi Nandan Mohanty⁴, E. Laxmi Lydia^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1353-1369, 2023, DOI:10.32604/cmc.2023.036453 - 06 February 2023

Abstract The current advancement in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT) transformed the traditional healthcare system into smart healthcare. Healthcare services could be enhanced by incorporating key techniques like AI and IoT. The convergence of AI and IoT provides distinct opportunities in the medical field. Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population. Therefore, earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support. Lately, the emergence of IoT,… More >

Nabil Almalki^1,2, Mrim M. Alnfiai^1,3, Fahd N. Al-Wesabi⁴, Mesfer Alduhayyem⁵, Anwer Mustafa Hilal^6,*, Manar Ahmed Hamza⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6719-6736, 2023, DOI:10.32604/cmc.2023.034417 - 28 December 2022

Abstract Mobile communication and the Internet of Things (IoT) technologies have recently been established to collect data from human beings and the environment. The data collected can be leveraged to provide intelligent services through different applications. It is an extreme challenge to monitor disabled people from remote locations. It is because day-to-day events like falls heavily result in accidents. For a person with disabilities, a fall event is an important cause of mortality and post-traumatic complications. Therefore, detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary… More >

Mohamed Esmail Karar^1,2,*, Omar Reyad^1,3, Hazem Ibrahim Shehata^1,4

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2377-2389, 2023, DOI:10.32604/csse.2023.032931 - 21 December 2022

Abstract This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis… More >

Ala Saleh Alluhaidan¹, Masoud Alajmi², Fahd N. Al-Wesabi^3,4, Anwer Mustafa Hilal⁵, Manar Ahmed Hamza^5,*, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2713-2727, 2022, DOI:10.32604/cmc.2022.025202 - 29 March 2022

Abstract Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based More >

Ramon Sanchez-Iborra¹, Luis Bernal-Escobedo², Jose Santa^3,*, Antonio Skarmeta²

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3869-3885, 2022, DOI:10.32604/cmc.2022.022610 - 07 December 2021

Abstract A new wave of electric vehicles for personal mobility is currently crowding public spaces. They offer a sustainable and efficient way of getting around in urban environments, however, these devices bring additional safety issues, including serious accidents for riders. Thereby, taking advantage of a connected personal mobility vehicle, we present a novel on-device Machine Learning (ML)-based fall detection system that analyzes data captured from a range of sensors integrated on an on-board unit (OBU) prototype. Given the typical processing limitations of these elements, we exploit the potential of the TinyML paradigm, which enables embedding powerful More >

G. Anitha^1,*, S. Baghavathi Priya²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 87-103, 2022, DOI:10.32604/csse.2022.020361 - 02 December 2021

Abstract Human fall detection plays a vital part in the design of sensor based alarming system, aid physical therapists not only to lessen after fall effect and also to save human life. Accurate and timely identification can offer quick medical services to the injured people and prevent from serious consequences. Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments. At present times, deep learning (DL) models particularly convolutional neural networks (CNNs) have gained much importance in the fall detection tasks. With this motivation, this paper presents a new vision based… More >

Ming-Chih Chen, Yin-Ting Cheng^*, Ru-Wei Chen

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1103-1119, 2021, DOI:10.32604/cmes.2021.015589 - 11 August 2021

Abstract This work presents a fall detection system based on artificial intelligence. The system incorporates miniature wearable devices for fall detection. Fall detection is achieved by integrating a three-axis gyroscope and a three-axis accelerometer. The system gathers the differential data collected by the gyroscope and accelerometer, applies artificial intelligence algorithms for model training and constructs an effective model for fall detection. To provide easy wearing and effective position detection, it is designed as a small device attached to the user’s waist. Experiment results have shown that the accuracy of the proposed fall detection model is up More >

Jiancheng Zou¹, Na Zhu^1,*, Bailin Ge¹, Don Hong²

Journal of New Media, Vol.3, No.1, pp. 1-10, 2021, DOI:10.32604/jnm.2021.017763 - 15 March 2021

Abstract We propose an improved a single-shot detector (SSD) algorithm to detect falls of the elderly. The VGG16 network part of the SSD network is replaced with the MobilenetV2 network. At the same time, we change the infrastructure of MobilenetV2 network, the three layers that were not downsampled at the end were removed, which can make the model structure simpler and faster to detect. The complete Intersection-over-Union (CIoU) loss function is introduced to get a good regression of the target borders that have different sizes and different proportions. We use Feature Pyramid Network (FPN) for upsampling, More >