Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population

Taramasco, Carla; Rimassa, Carla; Martinez, F. Eduardo

doi:10.3390/s23010268

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…The proposed ensemble method demonstrates promising results in accurately identifying various activities, contributing to the advancement of assisted living technologies. A compelling clinical trial demonstrating the positive impact of ambient-assisted living on the quality of life among elderly individuals in Chile was presented in [18]. The findings highlight the potential benefits of this technology in enhancing the well-being of older adults.…”

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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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 view of everyday circumstances enables the characterization of human-computer interaction as classified by restrained, anticipatory communications, and pervasive. The safety, health, and hygiene of the elderly can be effectively improved by AAL; early detection of potential health impairment, continuous monitoring of the condition of the elderly, and detection of hazardous events are supported by wireless sensor network architectures (Taramasco et al, 2022). AAL technologies are used in mobile emergency response systems, video surveillance, reminders, mobility and automation assistance tools, fall detection systems, and more.…”

Section: Introductionmentioning

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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“…On the other hand, actimetry is also known as actigraphy, and it is characterized as the assessment of an individual's active work through the discovery of body development. This makes it conceivable to screen an individual's action through light and development sensors (Taramasco et al, 2022). The motivation for this research work stems from the interest in fostering our day-to-day way of living and improving healthcare.…”

Section: Introductionmentioning

confidence: 99%

Ambient Assisted Living for Enhanced Elderly and Differently Abled Care: A Novel Attention Transfer Learning-based Crossover Chimp Optimization

Abidi,

Mohammed,

Alkhalefah

2024

Journal of Disability Research

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Ambient assisted living (AAL) is a groundbreaking approach that harnesses the power of smart technology to offer all-encompassing care and support for elderly and differently abled individuals in their day-to-day lives. Progressive innovation in AAL solutions can facilitate and support day-to-day routines, expanding the time they can live autonomously and supporting proficiency. This research mainly analyzes AAL’s significant role in tending to the exceptional difficulties these populations face. AAL frameworks incorporate an array of sensors, gadgets, and intelligent calculations that help monitor current circumstances and exercises, empowering early recognition of peculiarities, fall counteraction, and customized help. This research introduces a novel attention transfer learning-based crossover chimp (ATL-CC) algorithm for AAL, which combines crossover-based chimp optimization with a transformer-based model for transfer learning, integrating an attention mechanism. The ATL-CC algorithm aims to enhance activity recognition and classification within AAL environments. Precision, accuracy, recall, root mean square error, and F1-score are evaluated, where accuracy attains the value of 98.9%, precision attains the value of 97.4%, recall attains the value of 98%, and F1-score attains the value of 96%. Overall, AAL arises as a promising arrangement that upholds the deprived and advances respect, independence, and inclusivity in maturing and various societies.

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Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population

Cited by 8 publications

References 42 publications

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

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

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

Ambient Assisted Living for Enhanced Elderly and Differently Abled Care: A Novel Attention Transfer Learning-based Crossover Chimp Optimization

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