Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Syed, Liyakathunisa; Alsaeeedi, Abdullah; Jabeen, Sadia; Kolivand, Hoshang

doi:10.3233/ais-210162

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…As a result, this method did not discover the three-dimensional information. Liyakathunisa et al (2022) implemented AAL for aging care using the IoMT and smart sensors along with the GRU deep learning method. With the fast growth of the elderly population, they face various challenges in day-to-day life, such as social problems, weak walking speeds, medical problems, etc.…”

Section: Literature Surveymentioning

confidence: 99%

SMGR-BS: Stacking Multiple Gated Recurrent Butterfly Search Model-Based Innovative AAL for Aging and Disabled Individuals

Abidi,

Alkhalefah,

Mohammed

et al. 2024

Journal of Disability Research

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Ambient assisted living (AAL) for aging and disabled people involves creating supportive environments that leverage technology to improve the quality of life and independence of these individuals. Traditional methods for developing AAL solutions for aging and disabled people face several challenges, such as scalability, high costs, and privacy concerns. To tackle these complexities, this article proposed a novel method named stacking multiple gated recurrent-based butterfly search (SMGR-BS) for the development of AAL for aging and disabled people. In this study, stacking multiple gated recurrent units are utilized to capture intricate temporal dependencies in sensor data, and the deep recurrent neural network extracts the features from the variety of sensor inputs. Also, the butterfly optimization algorithm with a local search strategy is employed to fine-tune the parameters and enhance the effectiveness of the SMGR-BS method. In this work, the experiments are conducted on the Mobile HEALTH dataset, and the performance evaluation of the SMGR-BS method involves analyzing its effectiveness based on evaluation metrics, namely specificity, F1-score, recall, precision, and accuracy, and comparing its performance against existing methodologies to assess its effectiveness. The experimental results illustrate the effectiveness of the SMGR-BS method for developing AAL for aging and disabled people.

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Section: Literature Surveymentioning

confidence: 99%

SMGR-BS: Stacking Multiple Gated Recurrent Butterfly Search Model-Based Innovative AAL for Aging and Disabled Individuals

Abidi,

Alkhalefah,

Mohammed

et al. 2024

Journal of Disability Research

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“…The framework's practicality and effectiveness make it a valuable resource for researchers and practitioners [15]. To leverage technologies for elderly care, a comprehensive approach was offered in [16]. By integrating IoT, smart sensors, and GRU deep learning, it presented a promising framework that addresses the challenges of providing enhanced assistance to the elderly population.…”

Section: Introductionmentioning

confidence: 99%

Gated recurrent unit decision model for device argumentation in ambient assisted living

Kumar,

Prakash,

Krinkin

2024

IJECE

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The increasing elderly population worldwide is facing a variety of social, physical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people prefer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among devices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.

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“…Such an increase of the global aging population is associated with age-related challenges, such as reduced mobility, falls, difficulties in performing daily activities, memory-related and social isolation issues, which have led the society and the different national health care systems to face ever-growing demand for monitoring, assistance, and medical care. Moreover, the recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, deep learning solutions are trained on the data collected from a sensor, or a set of sensors, in order to automatically identify the user's activities [14]. The most attractive deep learning architectures for skeleton-based HAR are Recurrent Neural Networks (RNN) [4,[15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Network Solutions for Human Posture Recognition Based on Kinect Skeletal Data

Guerra

Ramat

Beltrami

et al. 2023

Sensors

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Ambient Assisted Living (AAL) systems are designed to provide unobtrusive and user-friendly support in daily life and can be used for monitoring frail people based on various types of sensors, including wearables and cameras. Although cameras can be perceived as intrusive in terms of privacy, low-cost RGB-D devices (i.e., Kinect V2) that extract skeletal data can partially overcome these limits. In addition, deep learning-based algorithms, such as Recurrent Neural Networks (RNNs), can be trained on skeletal tracking data to automatically identify different human postures in the AAL domain. In this study, we investigate the performance of two RNN models (2BLSTM and 3BGRU) in identifying daily living postures and potentially dangerous situations in a home monitoring system, based on 3D skeletal data acquired with Kinect V2. We tested the RNN models with two different feature sets: one consisting of eight human-crafted kinematic features selected by a genetic algorithm, and another consisting of 52 ego-centric 3D coordinates of each considered skeleton joint, plus the subject’s distance from the Kinect V2. To improve the generalization ability of the 3BGRU model, we also applied a data augmentation method to balance the training dataset. With this last solution we reached an accuracy of 88%, the best we achieved so far.

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Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Cited by 7 publications

References 27 publications

SMGR-BS: Stacking Multiple Gated Recurrent Butterfly Search Model-Based Innovative AAL for Aging and Disabled Individuals

SMGR-BS: Stacking Multiple Gated Recurrent Butterfly Search Model-Based Innovative AAL for Aging and Disabled Individuals

Gated recurrent unit decision model for device argumentation in ambient assisted living

Recurrent Network Solutions for Human Posture Recognition Based on Kinect Skeletal Data

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