A Smart Healthcare Monitoring System Using Smartphone Interface

Akshat,; Gaurav, .; Zahid,; Bhupendra, -; Aditi, Aditi; Kumar, Sachin; Maneesha,; Pandey, Praveen

doi:10.1109/icdcsyst.2018.8605142

Cited by 21 publications

(4 citation statements)

References 3 publications

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“…e following are the detail answer of each research question. A healthcare monitoring system using random forest and internet of ings (IoT) 2 [11] Smart Healthcare: An Approach for Ubiquitous Healthcare Management Using IoT 3 [12] Smart ambulance system using concept of big data and internet of things 4 [13] Internet of things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare 5 [14] A hybrid framework for multimedia data processing in IoT-healthcare using blockchain technology 6 [15] An approach to develop the smart health using Internet of ings and authentication based on biometric technology 7 [16] Context-aware anonymous authentication protocols in the internet of things dedicated to e-health applications 8 [17] Edge computing for Internet of ings: A survey, e-healthcare case study and future direction 9 [18] Edge-of-things computing framework for cost-effective provisioning of healthcare data [19] Enabling technologies for fog computing in healthcare IoT systems [20] Harnessing the power of Internet of ings based connectivity to improve healthcare [21] Internet of ings (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions [22] Internet of ings: A survey of enabling technologies in healthcare and its applications [5] Mobile cloud computing based stroke healthcare system [23] Mobile edge computing based QoS optimization in medical healthcare applications [24] Paradigm of IoT big data analytics in the healthcare industry: A review of scientific literature and mapping of research trends [25] Policy-based access control for constrained healthcare resources [26] Smart integrated IoT healthcare system for cancer care [27] Enabling mass surveillance: data aggregation in the age of big data and the Internet of ings [28] Applying a mindfulness-based reliability strategy to the Internet of ings in healthcare-A business model in the Vietnamese market [29] A Smart Healthcare Monitoring System Using Smartphone Interface [30] A study on medical Internet of ings and Big Data in personalized healthcare system [31] An IoT architecture for preventive maintenance of medical devices in healthcare organizations [32] An IOT Based Smart Mobile Health Monitoring System [33] Context-aware pseudon...…”

Section: Overview Of Selected Studies and Resultsmentioning

confidence: 99%

Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Literature Review

Nazir

Ali

Ullah

et al. 2019

Wireless Communications and Mobile Computing

147

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The impact of Internet of Things has been revolutionized in all fields of life, but its impact on the healthcare system has been significant due to its cutting edge transition. The role of Internet of Things becomes more dominant when it is supported by the features of mobile computing. The mobile computing extends the functionality of IoT in healthcare environment by bringing a massive support in the form of mobile health (m-health). In this research, a systematic literature review protocol is proposed to study how mobile computing assists IoT applications in healthcare, contributes to the current and future research work of IoT in the healthcare system, brings privacy and security in health IoT devices, and affects the IoT in the healthcare system. Furthermore, the intentions of the paper are to study the impacts of mobile computing on IoT in healthcare environment or smart hospitals in light of our systematic literature review protocol. The proposed study reports the papers that were included based on filtering process by title, abstract, and contents, and a total of 116 primary studies were included to support the proposed research. These papers were then analysed for research questions defined for the proposed study.

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Section: Overview Of Selected Studies and Resultsmentioning

confidence: 99%

Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Literature Review

Nazir

Ali

Ullah

et al. 2019

Wireless Communications and Mobile Computing

147

View full text Add to dashboard Cite

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“…Environmental risks are taken from the concept of virtual zones and provide updated information elsewhere. People with chronic diseases should monitor their vital signs regularly and repeatedly [15,16]. Essential measurement of vital signs and physiological functions will be useful.…”

Section: Related Workmentioning

confidence: 99%

Sensors Based Optimized Closed Loop Control Algorithm to Minimize Hypoglycemia/Hyperglycemia using 4-Variate Time Series Data

G.,

Sundarapandi,

et al. 2024

JNMES

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The goal of this work is to create a closed-loop control system that combines continuous glucose, carbohydrates, and physiological variable readings to regulate glucose levels to treat hyperglycemia and prevent hypoglycemia, as well as a hypoglycemia early alarm module. We use an Extended Kalman filter (EKF) to estimate time-series coefficients of type 1 diabetic patient's glucose levels using a 4-variate time series data such as glucose level, insulin dose, physical activities, and food consumption. An adaptive Kalman Filter algorithm is best suitable for sensor fusion and also for real-time data, which uses the series of measurement data over the period to tend to predict the unknown variable. We proposed to provide the mealtime bolus as a prolonged bolus, with a slice of the insulin dosage delivered before food and the remainder delivered after food. Our research entails the use of a glucose monitor (CGM), physical activity monitor (accelerometer sensor), carbohydrates monitor (biosensor), an automatic insulin infusion controller that calculates the quantity of insulin to be injected, and an insulin infusion drive. Low-Power and Lossy Routing (LPLR) with 6LoWPAN are proposed for efficient routing and private networks. Our model is evaluated using the UVa/Padova simulator for 25 patients. We conducted our experiments with 25 virtual patients with a mix of all age categories. The simulator's default sample time is one minute, but we have set the 5-minute sampling time. Overall, the proposed models are good at predicting hypoglycaemic (<70 mg/dl), normal glycaemic (>70 and 180 mg/dl), and hyperglycaemic (180 mg/dl) blood sugar levels.

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“…Saat merekam dengan smartphone, pasang stetoskop yang dimodifikasi ke jack audio. Jika port jack audio tidak berfungsi, gunakan konverter ke USB dan hubungkan ke smartphone (Akshat, 2018).…”

Section: Sistem Monitoring Kesehatan Usus Berbasis Embedded Systemunclassified

Sistem Identifikasi Kesehatan Pencernaan Berdasar Suara Usus Menggunakan Embedded System

Fabiana,

Prasetio,

Setiawan

et al. 2023

JTIIK

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Ileus adalah salah satu penyakit usus yang disebabkan oleh tersumbatnya lumen usus akibat berhentinya gerak peristaltik di dalam usus. Jika tidak ditangani dengan cepat, Ileus bisa menyebabkan usus berlubang. Namun, penderita Ileus seringkali tidak menyadari terjadinya penyakit ini di dalam tubuhnya, sehingga sebaiknya memeriksakan kesehatan pencernaan setiap hari sebagai pencegahan. Oleh karena itu, diperlukan stetoskop digital untuk memberikan jawaban dari hasil auskultasi. Penelitian ini menggunakan stetoskop yang dimodifikasi dengan soundcard yang terhubung dengan raspberry pi dan ditampilkan melalui aplikasi VNC Viewer. Sedangkan Convolutional Neural Network dengan model tensor flow digunakan sebagai metode klasifikasi dan MFCC teknik ekstraksi fiturnya. Sistem merekam dan mendeteksi kehadiran suara perut dengan auskultasi selama 10 detik, dan jika terdeteksi suara perut lebih dari 1 maka dapat disimpulkan pencernaan itu sehat, begitu juga sebaliknya. Dalam penelitian ini, uji klasifikasi untuk yang lain diberi label ‘NIHIL’ suara perut, dan hasil auskultasi adalah 5,85 detik untuk uji klasifikasi. Akurasi yang diperoleh untuk klasifikasi TERDETEKSI' adalah 90%, dan untuk ‘NIHIL’ adalah 100%. AbstractIleus is one intestinal disease caused by the blockage of the bowel lumen due to the cessation of peristalsis within the intestine. If not handled quickly, Ileus could lead to a perforated bowel. However, the sufferer of Ileus often does not realize the occurrence of this disease within his body, so it would be better to check up on the digestive health every day as a preventive. Therefore, a digital stethoscope is needed to provide answers from auscultation results. This study used a modified stethoscope with a soundcard connected to the raspberry pi and displayed through the VNC Viewer application. Meanwhile, Convolutional Neural Network with tensor follow model is used as the classification method and MFCC Technique as the feature extraction. The system records and detects the bowel noise with auscultation for 10 seconds, and if the bowel noise is detected more than1 time, it means normal condition or health, and vice versa. In this study, the classification test for another was labeled with ‘NIHIL,' and the auscultation result was 5.85 seconds for the classification test. The accuracy obtained for the classification of 'digestion-sound' is 90%, and for the 'no digestion sound' is 100%.

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A Smart Healthcare Monitoring System Using Smartphone Interface

Cited by 21 publications

References 3 publications

Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Literature Review

Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Literature Review

Sensors Based Optimized Closed Loop Control Algorithm to Minimize Hypoglycemia/Hyperglycemia using 4-Variate Time Series Data

Sistem Identifikasi Kesehatan Pencernaan Berdasar Suara Usus Menggunakan Embedded System

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