Smart Home Based Sleep Disorder Recognition for Ambient Assisted Living

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend. INDEX TERMS abnormal behavior detection, activities of daily living

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“…[13]- [16], [18], [22], [23], [52], [53], [68], [94], [98]-[100] Electrical appliances [30], [63] Lost [101]…”

Section: Fall Detectionmentioning

confidence: 99%

A Review of Abnormal Behavior Detection in Activities of Daily Living

et al. 2023

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“…In addition, sleep behavior is found to be an effect on adolescent health. In the present scenario, most people pay attention to their quality of sleep [4]. Moreover, to enhance individual sleep quality, they want to distinguish the accurate condition of their sleep; that is they require techniques to observe their sleep conditions.…”

Section: Introductionmentioning

confidence: 99%

Modified Bald Eagle Search Algorithm With Deep Learning-Driven Sleep Quality Prediction for Healthcare Monitoring Systems

Alabdan,

Mengash,

Maray

et al. 2023

IEEE Access

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Sleep habits are strongly related to health behaviors, with sleep quality serving as a major health indicator. Current approaches for evaluating sleep quality, namely polysomnography and questionnaires, are often time-consuming, costly, or invasive. Thus, there is a pressing need for a more convenient, nonintrusive, and cost-effective method. The applications of deep learning (DL) in sleep quality prediction represent a groundbreaking technique for addressing sleep-related disorders. In this aspect, the article offers the design of a Modified Bald Eagle Search Algorithm with Deep Learning-Driven Sleep Quality Prediction (MBES-DLSQP) for Healthcare Monitoring Systems. The MBES-DLSQP technique combines the strengths of a DL model with a hyperparameter tuning strategy to provide precise sleep quality predictions. At the primary stage, the MBES-DLSQP technique undergoes data pre-processing. Besides, the MBES-DLSQP technique uses a stacked sparse autoencoder (SSAE)-based prediction model, which can extract and encode high-dimensional sleep data. The MBES-DLSQP incorporates MBESA-based hyperparameter tuning which assures its optimal configurations to further boost the efficiency of the SSAE model. The experimental outcome of the MBES-DLSQP algorithm is tested on the sleep dataset from the Kaggle repository. The experimental value infers that the MBES-DLSQP technique shows promising performance in sleep quality prediction with a maximum accuracy of 98.33%.

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Smart Home Based Sleep Disorder Recognition for Ambient Assisted Living

Cited by 2 publications

References 17 publications

A Review of Abnormal Behavior Detection in Activities of Daily Living

A Review of Abnormal Behavior Detection in Activities of Daily Living

Modified Bald Eagle Search Algorithm With Deep Learning-Driven Sleep Quality Prediction for Healthcare Monitoring Systems

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