An IoT System for Remote Monitoring of Patients at Home

Park, Keehyun; Park, Joonsuu; Lee, Jong Whi

doi:10.3390/app7030260

Cited by 46 publications

(28 citation statements)

References 24 publications

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“…The proposed system successfully categorizes present activities of patients as normal, suspicious, or dangerous, which are utilized to inform the healthcare staff about potential problems. Park et al proposed an IoT System for the remote monitoring of patients at home and utilized Personal Healthcare Devices (PHDs) that sense and calculate persons' biomedical signals [64]. The proposed system informs medical staffs when the patients encounter emergency situations in real-time.…”

Section: Managerial Implicationsmentioning

confidence: 99%

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

et al. 2018

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As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.

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Section: Managerial Implicationsmentioning

confidence: 99%

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

et al. 2018

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“…The history of catheterization for each patient is also uploaded to the cloud server and saved as encryption data. After logging in, the outpatient department (OPD) doctor writes down a prescription and gives advice for the succeeding examinations or treatments [27]. As for the Internet connections, the history of the voided volume is stored in the app and transmitted to the server via WiFi or 4G.…”

Section: Feedback On Bladder Healthmentioning

confidence: 99%

Development of an Assistant System of Clean Intermittent Catheterization for Neurogenic Bladder Dysfunction Patients

et al. 2019

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Clean intermittent catheterization (CIC) is one of the methods currently used to prevent overdistention of the bladder in patients with neurogenic bladder dysfunction (NBD). It is also helpful in preventing urinary tract infection and retaining the function of the bladder. Voiding diary and social behavior are necessary for the patients to maintain their physical and mental health, nevertheless there are currently no good assistant systems to help them achieve these goals. In this study, we propose a CIC assistant system with the functions of recording and tracking the voided volume and accessible lavatory. The aim of this study is to assess the effects from the 12 patients who joined the proposed system. Information collected from participants included their demographics, past medicals, injury characteristics, current and past bladder managements, and any NBD related complications. The results indicate that most patients felt relieved that they could know the voided volume and track their history easily. Moreover, the accessible lavatory function of the proposed system could reduce time consumption by 43.1% in finding the suitable lavatory, thus the desire and willingness of patients to travel increased from 25% to 75% after using the system. The proposed system could help doctors with clinical diagnoses, and help patients to understand more about the history of their catheterization volume and time period. This study provided essential information and design for future investigation.

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“…Therefore, this grouping scheme satisfies the optimal requirement from the local perspective. Globally, however, the optimal grouping combinations are not necessarily achieved [33,34].The pseudo-code of the network coding algorithm is given in Table 3 below. Finally, the destination nodes will judge if they have received k linearly independent packet combinations.…”

Section: Design Of Greedy Grouping Algorithmmentioning

confidence: 99%

An Efficient Network Coding-Based Fault-Tolerant Mechanism in WBAN for Smart Healthcare Monitoring Systems

et al. 2017

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Abstract:As a key technology in smart healthcare monitoring systems, wireless body area networks (WBANs) can pre-embed sensors and sinks on body surface or inside bodies for collecting different vital signs parameters, such as human Electrocardiograph (ECG), Electroencephalograph (EEG), Electromyogram (EMG), body temperature, blood pressure, blood sugar, blood oxygen, etc. Using real-time online healthcare, patients can be tracked and monitored in normal or emergency conditions at their homes, hospital rooms, and in Intensive Care Units (ICUs). In particular, the reliability and effectiveness of the packets transmission will be directly related to the timely rescue of critically ill patients with life-threatening injuries. However, traditional fault-tolerant schemes either have the deficiency of underutilised resources or react too slowly to failures. In future healthcare systems, the medical Internet of Things (IoT) for real-time monitoring can integrate sensor networks, cloud computing, and big data techniques to address these problems. It can collect and send patient's vital parameter signal and safety monitoring information to intelligent terminals and enhance transmission reliability and efficiency. Therefore, this paper presents a design in healthcare monitoring systems for a proactive reliable data transmission mechanism with resilience requirements in a many-to-one stream model. This Network Coding-based Fault-tolerant Mechanism (NCFM) first proposes a greedy grouping algorithm to divide the topology into small logical units; it then constructs a spanning tree based on random linear network coding to generate linearly independent coding combinations. Numerical results indicate that this transmission scheme works better than traditional methods in reducing the probability of packet loss, the resource redundant rate, and average delay, and can increase the effective throughput rate.

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An IoT System for Remote Monitoring of Patients at Home

Cited by 46 publications

References 24 publications

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Development of an Assistant System of Clean Intermittent Catheterization for Neurogenic Bladder Dysfunction Patients

An Efficient Network Coding-Based Fault-Tolerant Mechanism in WBAN for Smart Healthcare Monitoring Systems

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