Smart Home-Based Complex Interwoven Activities for Cognitive Health Assessment

Alsubai, Shtwai; Alqahtani, Abdullah; Sha, Mohemmed; Abbas, Sidra; Almadhor, Ahmad; Veselý, Peter; Mughal, Huma

doi:10.1155/2022/3792394

Cited by 5 publications

(3 citation statements)

References 22 publications

(21 reference statements)

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“…This capability facilitates the collection of comprehensive data regarding the health-related activity patterns of users, particularly in relation to their daily activities. 3 In this article, an exercise refers to a subset of physical activity that is planned and executed with a specific goal and has as its ultimate or intermediate goal of improvement or maintenance of exercise. 4 , 5 The digital health service, through the utilization of real-time situation triggered reminders, pushes, and notifications, can assist the user in improving the effectiveness of daily exercise.…”

Section: Introductionmentioning

confidence: 99%

Enhancing digital health services: A machine learning approach to personalized exercise goal setting

Fang,

Lee,

et al. 2024

DIGITAL HEALTH

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Background The utilization of digital health has increased recently, and these services provide extensive guidance to encourage users to exercise frequently by setting daily exercise goals to promote a healthy lifestyle. These comprehensive guides evolved from the consideration of various personalized behavioral factors. Nevertheless, existing approaches frequently neglect the users’ dynamic behavior and the changing in their health conditions. Objective This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior trajectory. Methods We conducted a methodological study by designing a deep reinforcement learning algorithm to evaluate exercise performance, considering fitness-fatigue effects. The deep reinforcement learning algorithm combines deep learning techniques to analyze time series data and infer user's exercise behavior. In addition, we use the asynchronous advantage actor-critic algorithm for reinforcement learning to determine the optimal exercise intensity through exploration and exploitation. The personalized exercise data and biometric data used in this study were collected from publicly available datasets, encompassing walking, sports logs, and running. Results In our study, we conducted the statistical analyses/inferential tests to compare the effectiveness of machine learning approach in exercise goal setting across different exercise goal-setting strategies. The 95% confidence intervals demonstrated the robustness of these findings, emphasizing the superior outcomes of the machine learning approach. Conclusions Our study demonstrates the adaptability of machine learning algorithm to users’ exercise preferences and behaviors in exercise goal setting, emphasizing the substantial influence of goal design on service effectiveness.

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Section: Introductionmentioning

confidence: 99%

Enhancing digital health services: A machine learning approach to personalized exercise goal setting

Fang,

Lee,

et al. 2024

DIGITAL HEALTH

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show abstract

“…The IoMT includes stationary devices such as hospital screens and imaging equipment, implantable cardiac and insulin pumps, and ambient devices such as smart beds and detectors. Together, these devices collect and transmit data, which can be utilized to monitor patients’ health, identify ailments, and design personalized treatment plans for them [ 19 , 20 , 21 ]. With wearable technology, precise and durable data estimation is feasible, and this information can be utilized to estimate different factors of human fitness, such as stress status.…”

Section: Introductionmentioning

confidence: 99%

“…Healthcare is increasingly adopting AI techniques to improve diagnostics, monitoring, and overall patient care. However, the success of AI algorithms heavily relies on the availability of high-quality and diverse datasets [ 20 , 33 , 34 , 35 ]. In the case of stress detection, access to large-scale and labeled datasets is limited, which hinders the development and evaluation of accurate AI models.…”

Section: Introductionmentioning

confidence: 99%

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Almadhor

Sampedro

Abisado

et al. 2023

Sensors

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Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.

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Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

et al. 2023

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The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field.

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Smart Home-Based Complex Interwoven Activities for Cognitive Health Assessment

Cited by 5 publications

References 22 publications

Enhancing digital health services: A machine learning approach to personalized exercise goal setting

Enhancing digital health services: A machine learning approach to personalized exercise goal setting

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

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