Sleep Monitoring Wearables: Present to Future

Dutta, Kusumika Krori; Sharma, Sumit; Sasidharan, Arun; Mukundan, Chetan S.

doi:10.1007/978-981-16-5324-7_6

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…are carried out with different pre-processing techniques [19]. Even some of the wearable devices are developed based on AI techniques to aid diagnosis procedure [20].…”

Section: Literature Surveymentioning

confidence: 99%

Supervised learning techniques for detection of Lung Carcinoma

Jalall,

Harsha,

Dutta

et al. 2023

J. Phys.: Conf. Ser.

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Lung diseases are the most common ailments seen among people with the history of smoking. Prompt and timely recognition and diagnosis may help in saving many lives. In order to detect cancer at early stages machine learning algorithms can be employed. Use of simple machine learning algorithms will help identify the carcinoma faster with high accuracy and lesser expense. This work shows the use three of simple machine learning (ML) algorithms like Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). ML models were built using lung cancer patients’ dataset. The dataset was used to train the model as well as test the model. The three classifiers will detect the presence of lung cancer. For each classifier the Accuracy, Mean Square Error(MSE), precision, and Recall (R2) was calculated. A comparative study of the classifiers was done to identify which among the three was the best one. The main objective of the paper is to identify the best efficient machine-learning algorithm in terms of confusion matrices, accuracy, and precision for the prediction and diagnosis of lung cancer

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“…are carried out with different pre-processing techniques [19]. Even some of the wearable devices are developed based on AI techniques to aid diagnosis procedure [20].…”

Section: Literature Surveymentioning

confidence: 99%

Supervised learning techniques for detection of Lung Carcinoma

Jalall,

Harsha,

Dutta

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…are used as feature extractor based on their energy compaction, using recurrence plots, based on dictionary learning and sparse representations, using Random forest (RF) classifier, using different layers of CNN, transfer learning, using deep batch normalization, using neural memory networks, etc. are tried out with different researcher using cleaned and raw data from different datasets [30][31][32][33][34][35][36][37][38][39][40][41][42]. Slowly real time clinical diagnostics is tried out [43].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2023

AMLAI

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Background: Epileptic Seizure type diagnosis is done by clinician based on the symptoms during the episode and the Electroencephalograph (EEG) recording taken during inter-ictal period. But main challenge is, most of the time with the absence of any attendee, the patients are unable to explain the symptoms and not possible to find signature in inter-ictal EEG signal. Aims: This paper aims to analyze epileptic seizure Electro-encephalograph (EEG) signals to diagnose seizure in pre-ictal, ictal and inter-ictal stages and to classify into seven different classes. Methods: Temple University Hospital licensed dataset is used for study. From the seizure corpus, seven seizure types are pre-processed and segregated into pre-ictal, ictal and inter-ictal stages. The multi class classification performed using different machine and deep learning techniques such as K-Nearest Neighbor (KNN) and Random Forest, etc.Results: Multiclass classification of seven type of epileptic seizure with 20 channels, with 80-20 train-test ratio, is achieved 94. 7%, 94.7%, 69.0% training accuracy and 94.46%, 94.46% 71.11% test accuracy by weighted KNN for pre-ictal, ictal and inter-ictal stages respectively. Conclusion:Seven epileptic seizure type classification using machine learning techniques carried out with MATLAB software and weighted KNN shows better accuracy comparatively.

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“…Due to issues of cognitive limitations and other risk factors associated with aging, studies have suggested that elderly users, especially PWDs exhibit resistance to the use of healthcare devices [ 2 , 13 ]. Notwithstanding the existence of these studies, other research and projects have demonstrated that, with the right approach to design considerations, this resistance could be reduced tremendously, leading to the adoption of wearable devices by PWDs [ 14 , 15 , 16 , 17 , 18 , 19 ]. For instance, studies have revealed that GPS-based wireless tracking devices, such as GPS Smart Sole, Freedom GPS Locator Watch, Safe-link, Mindme Locate, MXLOCareTM, iTraq3, and PocketFinder+, and others, such as Fitbit, Mi band, Oura ring, and Alice PDx, have been preferred and widely used by PWDs [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of Actual Use Behavior of Healthcare Wearable Devices on Quality of Life: A Cross-Sectional Survey of People with Dementia and Their Caregivers in Ghana

et al. 2022

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The increasing prevalence of dementia has necessitated the introduction and use of healthcare wearable devices to augment the healthcare services delivered to people with dementia (PWDs). Unfortunately, evidence on user feedback and the real impact of the actual use of these devices on the quality of life of PWDs has not been fully explored, especially in the context of developing countries. This study, therefore, seeks to evaluate the impact of actual use behavior (AUB) of healthcare wearable devices on the quality of life (QoL) of PWDs in Ghana by using constructs from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model and the SF-36 quality of life instrument. Data were collected from patients with dementia and, in some cases, caregivers (acting as proxies) in three administrative regions of Ghana using structured questionnaires. Of the 420 questionnaires distributed, 356 were returned, giving a response rate of (356/420, 84.8%). Three hundred and twenty (320) responses were used for further analysis, employing the Partial Least Squares based on Structural Equation Modeling. The results showed a significant positive relationship between AUB and QoL (p < 0.001). Other predictors such as behavioral intention, facilitating conditions, effort expectancy and social influence were also found to significantly impact the actual use behavior of healthcare wearable devices among PWDs. The study also revealed that 4% (14) of patients rated their reported health transition associated with the use of healthcare wearable devices as excellent, 42.5% (136) as very good, and 39.4% (126) as good, respectively at the time of the study compared to a year ago. This study provided empirical evidence of the positive impact of the actual use of healthcare wearable devices on the quality of life of PWDs and further highlighted the factors that drive actual use behavior among PWDs in Ghana. Based on the findings of this study, stakeholders need to create the necessary conditions that will facilitate the adoption and use of healthcare wearable devices, as this will help improve the quality of life, reduce the burden of the disease on caregivers, and enable independent living of PWDs.

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Sleep Monitoring Wearables: Present to Future

Cited by 6 publications

References 10 publications

Supervised learning techniques for detection of Lung Carcinoma

Supervised learning techniques for detection of Lung Carcinoma

Untitled

Impact of Actual Use Behavior of Healthcare Wearable Devices on Quality of Life: A Cross-Sectional Survey of People with Dementia and Their Caregivers in Ghana

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