DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Sharma, Sumit; Dudeja, Rajan Kumar; Aujla, Gagangeet Singh; Bali, Rasmeet Singh; Kumar, Neeraj

doi:10.1007/s00521-020-05327-2

Cited by 50 publications

(38 citation statements)

References 38 publications

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“…Related to PD, various studies show the importance of those techniques for identifying PD using electroencephalography (EEG) [ 36 ] or for diagnosing and assessing PD using inertial sensors or video signals [ 37 ]. There are other diseases being assessed that utilize the same approach; for example, identifying atrial fibrillation using an electrocardiogram (ECG) and applying ML techniques to identify potential alterations [ 38 ], or diagnosing Alzheimer’s disease using the ML algorithms by processing sensor movement data from patients [ 39 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges

Barrachina

Maitín

Ávila

et al. 2021

Sensors

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Monitoring of motor symptom fluctuations in Parkinson’s disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation’s occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56–96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.

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Section: Discussionmentioning

confidence: 99%

Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges

Barrachina

Maitín

Ávila

et al. 2021

Sensors

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“…These sensors are expected to be helpful to make patient's life convenient. All these sensors are IoT-enabled to be always connected to the world for sensing the patients' activities and movements at all times [ 40 ]. Hydration Sensor.…”

Section: Methodology Used For Iomt-enabled Postdiagnosis Dementia Carementioning

confidence: 99%

A Perspective Roadmap for IoMT-Based Early Detection and Care of the Neural Disorder, Dementia

Juneja¹,

Dhiman²,

Kautish³

et al. 2021

Journal of Healthcare Engineering

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The Internet of Medical Things (IoMT) has emerged as one of the most important key applications of IoT. IoMT makes the diagnosis and care more convenient and reliable with proven results. The paper presents the technology, open issues, and challenges of IoMT-based systems. It explores the various types of sensors and smart equipment based on IoMT and used for diagnosis and patient care. A comprehensive survey of early detection and postdetection care of the neural disorder dementia is conducted. The paper also presents a postdiagnosis dementia care model named “Demencare.” This model incorporates eight sensors capable of tracking the daily routine of dementia patient. The patients can be monitored locally by an edge computing device kept at their premises. The medical experts may also monitor the patients’ status for any deviation from normal behavior. IoMT enables better postdiagnosis care for neural disorders, like dementia and Alzheimer’s. The patient’s behavior and vital parameters are always available despite the remote location of the patients. The data of the patients may be classified, and new insights may be obtained to tackle patients in a better manner.

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“…Deep Neural Networks does have a good application in medicine and smart healthcare. It has good adaptability in the analysis of multimodal healthcare data (image, text, speech, structured data) [ [58] ecosystem, which is nearly 10-20% more accurate than existing machine learning algorithms [22]. Hossain proposed a cloud-based patent and health monitoring model in the cyber physical environment, which has high efficiency and accuracy [23]; Deep Convolutional Neural Network (DCNN) and IoT are used in oral cancer image classification.…”

Section: A the Application Of Deep Learning And The Iot In Healthcarmentioning

confidence: 99%

Research on Disease Prediction Based on Improved DeepFM and IoMT

Amin

Alhussein

et al. 2021

IEEE Access

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In recent years, with the increase of computer computing power, Deep Learning has begun to be favored. Its learning of non-linear feature combinations has played a role that traditional machine learning cannot reach in almost every field. The application of Deep Learning has also driven the advancement of Factorization Machine (FM) in the field of recommendation systems, because Deep Learning and FM can learn high-order and low-order features combinations respectively, and FM's hidden vector system enables it to learn information from sparse data. The integration of them has attracted the attention of many scholars. They have researched many classic models such as Factorization-supported Neural Network (FNN), Product-based Neural Networks (PNN), Inner PNN (IPNN), Wide&Deep, Deep&Cross, DeepFM, etc. for the Click-Through-Rate (CTR) problem, and their performance is getting better and better. This kind of model is also suitable for agriculture, meteorology, disease prediction and other fields due to the above advantages. Based on the DeepFM model, we predicts the incidence of hepatitis in each sample in the structured disease prediction data of the 2020 Artificial Intelligence Challenge Preliminary Competition, and make minor improvements and parameter adjustments to DeepFM. Compared with other models, the improved DeepFM has excellent performance in AUC. This research can be applied to electronic medical records to reduce the workload of doctors and make doctors focus on the samples with higher predicted incidence rates. For some changing data, such as blood pressure, height, weight, cholesterol, etc., we can introduce the Internet of Medical Things (IoMT). IoMT's sensors can be used to conduct transmission to ensure that the disease can be predicted in time, just in case. After joining IoMT, a healthcare system is formed, which is superior in forecasting and time performance.

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DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Cited by 50 publications

References 38 publications

Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges

Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges

A Perspective Roadmap for IoMT-Based Early Detection and Care of the Neural Disorder, Dementia

Research on Disease Prediction Based on Improved DeepFM and IoMT

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