A Machine Learning Framework for Edge Computing to Improve Prediction Accuracy in Mobile Health Monitoring

Ram, Sigdel Shree; Apduhan, Bernady O.; Shiratori, Norio

doi:10.1007/978-3-030-24302-9_30

Cited by 16 publications

(17 citation statements)

References 4 publications

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“…Similar studies [69], [11] have proposed activity recognition using fog-based frameworks. In [11], twelve human activities were detected by employing wearable body sensors.…”

Section: A Physiological Health Data Analysismentioning

confidence: 81%

“…In [11], twelve human activities were detected by employing wearable body sensors. These studies used an LSTM based RNN model that was implemented on the fog nodes of local servers, whereas [69] proposed other types of movement monitoring sensors and used SVM and random forests for activity classification. Edge-based ML models (Edge ML) have been explored in recent studies, and involved the analysis of physiological health data using wearable sensors.…”

Section: A Physiological Health Data Analysismentioning

confidence: 99%

“…ML has also been used in Assistive Systems to rehabilitate patients after accidents. ML has been very popular in diagnosis and prediction of cardiac arrest in heart patients, using IoT based smart sensors [69]. In heart patients the ECG signal is monitored continuously and after noise filtering it is sent to ML algorithms for feature extraction [69].…”

Section: Machine Learning and Iot In Healthcare Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Edge Intelligence and Internet of Things in Healthcare: A Survey

2021

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“…Similar studies [69], [11] have proposed activity recognition using fog-based frameworks. In [11], twelve human activities were detected by employing wearable body sensors.…”

Section: A Physiological Health Data Analysismentioning

confidence: 81%

Section: A Physiological Health Data Analysismentioning

confidence: 99%

Section: Machine Learning and Iot In Healthcare Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Edge Intelligence and Internet of Things in Healthcare: A Survey

2021

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“…This is actually a cooperative fog to cloud architecture, where data preprocessing, indoor localization and activity recognition algorithms are performed by means of a home gateway while a private cloud is used for storing data to be accessed remotely. On the same line, fog architectures for activity recognition are presented in Uddin [50] and Ram et al [44] . In particular, Uddin [50] present a solution for recognizing twelve different human activities using wearable sensors and a LSTM recurrent neural network running on local fog server with GPU acceleration, while [44] employ additional sensors for movement tracking and investigate the use of support vector machines (SVM) and random forest (RF) classifiers for activity prediction.…”

Section: Analysis Of the Physiological Parametersmentioning

confidence: 98%

Trends in IoT based solutions for health care: Moving AI to the edge

Greco

Percannella

Ritrovato

et al. 2020

Pattern Recognition Letters

254

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In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health.

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“…Uddin [13] projected a solution to examine various human activities with the help of wearable sensors as well as Long Short-term Memory-Recurrent Neural Network (LSTM-RNN) which were implemented on local fog server and GPU acceleration. In the study conducted earlier [14], additional sensors were employed for movement tracking and to examine the application of Support Vector Machines (SVM) and Random Forest (RF) classification method for movement forecast. Some of the recently developed models for conducting physiological data analysis in portable sensors simulate the analysis of edge ML approaches.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

et al. 2021

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The recent advancements in Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI) transformed the conventional healthcare system into smart healthcare. By incorporating key technologies such as IoT and AI, medical services can be improved. The convergence of IoT and AI offers different opportunities in healthcare sector. In this view, the current research article presents a new AI and IoT convergence-based disease diagnosis model for smart healthcare system. The major goal of this article is to design a disease diagnosis model for heart disease and diabetes using AI and IoT convergence techniques. The presented model encompasses different stages namely, data acquisition, preprocessing, classification, and parameter tuning. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. The proposed method uses Crow Search Optimization algorithm-based Cascaded Long Short Term Memory (CSO-CLSTM) model for disease diagnosis. In order to achieve better classification of the medical data, CSO is applied to tune both 'weights' and 'bias' parameters of CLSTM model. Besides, isolation Forest (iForest) technique is employed in this research work to remove the outliers. The application of CSO helps in considerable improvement in the diagnostic outcomes of CLSTM model. The performance of CSO-LSTM model was validated using healthcare data. During the experimentation, the presented CSO-LSTM model accomplished the maximum accuracies of 96.16% and 97.26% in diagnosing heart disease and diabetes respectively. Therefore, the proposed CSO-LSTM model can be employed as an appropriate disease diagnosis tool for smart healthcare systems.

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A Machine Learning Framework for Edge Computing to Improve Prediction Accuracy in Mobile Health Monitoring

Cited by 16 publications

References 4 publications

Edge Intelligence and Internet of Things in Healthcare: A Survey

Edge Intelligence and Internet of Things in Healthcare: A Survey

Trends in IoT based solutions for health care: Moving AI to the edge

Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems

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