An integrated telemonitoring framework for insomnia disorder using internet of healthcare things

Insomnia is a health disorder caused by a disturbance in the continuity of sleep which causes serious problems for sufferers in carrying out daily activities. The prevalence of chronic insomnia sufferers is increasing in urban city life due to lifestyle and socio-economic conditions in developing countries. Currently, the de facto method to assess sleep disorder is using the Polysomnography device. However, the Polysomnography device is expensive and cumbersome, with the lack availability of device in healthcare services. This results in access limitation of treatment for the patient due to the limited number of devices. Patients in distance from health service should travel which increase time for diagnosis and high cost. Therefore, this study aims to create a solution to increase patient access to insomnia treatment. A new proposed framework could overcome problems in monitoring and diagnosing insomnia disorder. Patients at distance could receive similar performance from specialist as if they come to the hospital. In addition, this research is also proposed a diagnostic device which is portable and cheaper than polysomnography devices. The proposed device can be an alternative to the current polysomnography with low cost. The analysis in this study involves to assess user experience. This study conducted two questionnaires where the first questionnaire is to find out the current treatment conditions, constraints, and needs from the healthcare side to provide effective and efficient treatment and diagnostic for insomnia disorder. The second questionnaire was carried to find out the acceptance and user experience on the proposed framework for telemonitoring systems and its proposed devices for the diagnosis of insomnia. The second questionnaire involved eleven medical officers consisting of doctors and nurses as well as seven patients where the medical officers corresponded to the first and second questionnaires, while the patients only corresponded to the second questionnaire. The results showed that the level of acceptance from both the medical staff and the patients agreed that the telemonitoring system created by the researcher helped the treatment and provided equal access to patients for treatment and diagnosis of insomnia. The results also shows that improvement requirement on the proposed framework of the insomnia telemonitoring system by adding a platform for conducting patient consultations and a learning platform for medical staff remotely. The findings of this study indicate that the telemonitoring framework that has been studied in this study has a positive impact on all parties, both medical and patient, such that the same or even more affordable cost is the biggest concern aspect. Most patients agree on the ease of use of the device

Development of a Remote Straw Mushroom Cultivation System Using IoT Technologies

Indonesia's tropical climate creates vast potential for straw mushroom cultivation. However, crop failures are frequent during the rainy season due to lower temperatures. To address this challenge, this paper presents an innovative, IoT-based system designed to remotely control and monitor temperature and humidity in mushroom cultivation sites, thereby minimizing crop failure and optimizing production. The proposed system employs a DHT11 sensor to measure temperature and humidity levels accurately. A DS3231 module is incorporated to schedule automatic watering procedures, ensuring adequate hydration for the mushrooms without manual intervention. For real-time monitoring, an ESP32-Cam is used to capture images of the mushroom cultivation site. The core of this system is a NodeMCU microcontroller, which processes environmental data and automatically adjusts the cultivation conditions. The system triggers a heater if the temperature falls below 30°C, or an exhaust fan if it exceeds 35°C. Similarly, a humidifier activates if humidity falls below 80%, and an exhaust fan turns on when humidity exceeds 90%. To provide users with instant updates, the system integrates with the Blynk application, sending notifications when these specified conditions are met. This feature allows for prompt intervention when necessary, facilitating optimal growth conditions at all times. During testing, the proposed system demonstrated its effectiveness, enabling successful straw mushroom cultivation within nine days. Furthermore, it achieved this with modest power consumption, using a total of 661.608Wh. This system offers a promising solution to improve straw mushroom farming in regions with similar climates to Indonesia

Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection. However, artificial neural network required multitude of parameter setting in order to find the optimum parameter setting that produce the best performance. This paper proposed the parameter tuning framework for artificial neural network. Statlog heart disease dataset and Cleveland heart disease dataset is used to evaluate the performance of the proposed framework. The results show that the proposed framework able to produce high classification accuracy where the overall classification accuracy for Cleveland dataset is 90.9% and 90% for Statlog dataset

Development of Embedded System for Centralized Insomnia System

Insomnia is a common health problem in medical field as well as in psychiatry. The measurement of those factors could be collected by using polysomnography as one of the current standards. However, due to the routine of clinical assessment, the polysomnography is impractical and limited to be used in certain place. The rapid progress of electronic sensors to support IoT in health telemonitoring should provide the real time diagnosis of patient at home too. In this research, the development of centralized insomnia system for recording and analysis of patient with chronic-insomnia data is proposed. The system is composed from multi body sensors that connected to main IOT server. The test has been done for 5 patients and the result has been successfully retrieved in real time

Insomnia analysis based on internet of things using electrocardiography and electromyography

Insomnia is a disorder to start, maintain, and wake up from sleep, has many sufferers in the world. For patients in remote locations who suffer from insomnia, which requires testing, the gold standard performed requires patients to take the time and travel to the health care center. By making alternatives to remote sleep insomnia testing using electrocardiography and electromyography connected to the internet of things can solve the problem of patients' access to treatment. Delivery of patient data to the server is done to make observations from the visualization of patient data in real-time. Furthermore, using artificial neural networks was used to classify EMG, ECG, and combine patient data to determine patients who have Insomnia get resulted in patient classification errors around 0.2% to 2.7%

Development of a Remote Straw Mushroom Cultivation System Using IoT Technologies

Indonesia's tropical climate offers extensive potential for straw mushroom cultivation, an important topic given frequent crop failures during the rainy season due to reduced temperatures. Addressing this issue, this paper presents an innovative Internet of Things (IoT)-based system designed to remotely control and monitor temperature and humidity in mushroom cultivation sites, a tool that can significantly minimize crop failure and optimize production. Our proposed system employs a DHT11 sensor, responsible for accurately measuring temperature and humidity levels. To ensure the mushrooms receive adequate hydration without human intervention, a DS3231 module is incorporated for automatic watering scheduling. For real-time monitoring, we use an ESP32-Cam, a specific type of camera module, to capture images of the mushroom cultivation site. The heart of this system is a NodeMCU microcontroller, which processes environmental data and adjusts the cultivation conditions automatically. The system counteracts non-optimal conditions by triggering a heater or fan for temperature control, and a humidifier or exhaust for humidity control. It syncs with the Blynk app, providing updates for prompt response. The system was tested across multiple cultivation sites, showing improved crop success and low energy use, 661.608Wh. Despite its advantages, it acknowledges potential drawbacks, such as implementation costs, compatibility issues, and connectivity. Relevant performance indicators like crop yield and profitability are also evaluated. The contributions of this research are twofold. Firstly, it provides a robust, scalable solution for optimizing straw mushroom cultivation, particularly in regions with climates like Indonesia. Secondly, it sets a new benchmark for energy-efficient, automated mushroom farming, offering substantial benefits to farmers and the overall agricultural industry while further emphasizing its potential impact

Performance Analysis of RPL Protocols in LLN Network Using Friedman’s Test

International audienceThis paper provides a comparison study of the quality services of RPL protocols in low-power and lossy networks (LLN). We evaluate and compare our proposed protocol which is an extension of RPL based on Operator Calculus (OC), called RPL-OC, with the standard and other RPL variants. OC based approach is applied to extract the feasible end-to-end paths while assigning a rank to each one. The goal is to provide a tuple that containing the most efficient paths in end-to-end manner by considering more network metrics instead of one. Further, to address some significant issues of the performance analysis, a statistical test has been performed in order to determine whether the proposed protocol outperforms others or not by using Friedman test. The results show that there is a strong indication that four different protocols were analyzed and compared. It reveals that the proposed scheme outperforms others, especially in terms of end-to-end delay and energy consumption which allow ensuring quality of services requirements for Internet of Things (IoT) or smart city applications

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background: Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods: The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results: A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion: Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)