IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review
2. IoT-Based Healthcare Systems and Their Applications
- Remote healthcare: Wireless IoT-driven solutions bring healthcare to patients rather than the patient to healthcare. Data are collected securely through IoT-based sensors, and the data are analyzed by a small algorithm before being shared with health professionals for appropriate recommendations.
- Real-time monitoring: IoT-driven non-invasive-monitoring sensors collect comprehensive psychological information. Gateways and cloud-based analysis manage the storage of data.
- Preventive care: IoT healthcare systems use sensor data, which help with the early detection of emergencies and alerts family members. Machine learning for health-trend tracking and early anomaly detection is achieved through the IoT approach .
2.1. The Significance of IoT-Based Healthcare-Monitoring Systems
2.2. Benefits of Using IoT in Healthcare
- Reduced cost of care.
- Human errors are reduced.
- Elimination of the limitations of distance.
- Reduced amounts of paperwork and record keeping.
- Chronic diseases are detected early.
- Improvements in medication management.
- The need for prompt medical care.
- Better treatment outcomes.
3. Review of Recent Related Studies
|Authors with Reference||Aims and Contributions||Methodology||Hardware/Software |
|Gera et al. ||A patient health-monitoring system that is built on IoT technology and is connected to the Cloud Talk platform.||Used method known as software development life cycle (SDLC)||LM35, SEN-11574, MAX30102, and BMP 180.||Improves decision-making abilities and streamlines the normal flow of the healthcare system||Temperature, SpO2 level, BP, and pulse rate||IEE 802.11||Minimal contribution to the administration of medical care for patients|
|Wu, Wu ||Developed a small wearable sensor patch that can assess a variety of physiological signals.||Uses a smartphone as the mobile gateway, Raspberry Pi 3 as a fixed gateway, and a BLE module for transmission parameters.||AD8232, PPG, and Si7051 sensors, RFD77101 and Raspberry Pi 3.||ECG, HR, BT, and BP.||MQTT||Range and bandwidth limitations.|
|Islam, Rahaman ||Proposed a real-time IoT system to monitor patients’ vital signs and the room’s environmental conditions.||Data from sensors are gathered, processed, and uploaded to the cloud using an ESP32.||LM35, Heartbeat Sensor Module, DHT11, MQ-9, MQ-135, and ESP32.||In cases of infectious disease, the system is helpful.||BT and HR, CO, CO2, and humidity.||HTTP|
|Al-Sheikh and Ameen ||Designed an IoT healthcare-monitoring system that uses a mobile phone.||The system uses Arduino Uno to collect and process sensors’ data, followed by Wi-Fi transmission to the cloud.||Max30102, AD8232, LM35, NodeMCU, and Arduino.||HR, SpO2, ECG, and BT||IEEE 802.11|
|Hamim, Paul ||Developed a prototype of IoT-based remote health-monitoring system.||The system collects and processes sensor data using Arduino UNO and sends it to the cloud using Raspberry Pi 3.||LM35, HR Sensor Module, GSR sensor, Arduino, and Raspberry Pi 3.||HR, BT, and GSR||IEEE 802.11||System uses two microcontrollers that make it quite big.|
|Swaroop, Chandu ||Enhances healthcare delivery by communicating multiplexed data over three modes—BLE, GSM, and Wi-Fi.||Monitoring three parameters and sending data using three modes.||DS18B20, Sunrom BP/ HR monitor, Raspberry Pi 3, BLE adaptor, and USB GSM module.||HR, BT, and BP.||MQTT, BLE CSR Mesh||Accuracy depends on the sensors.|
|Gupta, Parikh ||Designed a real-time IoT monitoring system to track and evaluate the health of obese adults. Can store the data of multiple patients.||The MCU includes a built-in keyboard, LCD, and all the linked sensors. The keypad gives the user access to the device’s menus and the LCD display. The data are gathered by the ESP8266 and uploaded to the cloud.||MAX30100, LM35, wrist BP and pulse rate monitor, Atmega 328, keypad, LCD, and ESP8266 Wi-Fi Module.||BP, BT, pulse rate, and SpO2.||IEEE 802.11|
|Alamsyah, Ikhlayel ||Built an IoT-based system to monitor patients’ vital signs. Helps clinicians to make diagnoses.||This system uses Raspberry Pi for processing and communicating with the Internet using Wi-Fi technology.||MCP3008, HRM-2511E, DS18b20, MPX5050DP, and LCD.||Medical staff can access patients’ data through an Android device.||HR, BP, and BT||IEEE 802.11||Wi-Fi technology is not preferred for long-range application.|
|Sangeethalakshmi et al. ||Devised a real-time IoT-based system to track the condition of patients and save lives.||Detects vital parameters and sends them to ESP32 for processing and transferring to the cloud using Wi-Fi module.||LM35, AD8232, MAX30100, BP sensor, and ESP32.||Temperature, HR, ECG, BP and SpO2.||Wi-Fi/802.11||System needs to be evaluated, tested, and reorganized.|
|Sahu, Atulkar ||Created an IoT-enabled vital-sign-monitoring system.||Small electrical sensors are fitted to different bodily parts. Body sensor network transmits vital indicators to a controller via wireless or wired means (BSN).||ECG electrodes, pulse Oximeter, NIBP, BT sensors, STM32F103xC, CY8C58LP, and BLE 4.0 module.||System has an Android application and shows high accuracy measurements.||HR, SpO2, temperature, BP, and ECG.||Wi-Fi/802.11|
|Not suitable for long-range communication.|
|A. D. Acharya and S. N. Patil ||The patient’s body has sensors attached. These send body data to the MCU; then, they send the data to the cloud via a Wi-Fi module.||AD8232, LM35, MPX10, Arduino, and Raspberry Pi Module.||ECG, temperature, and BP.||IEEE 802.11||Wi-Fi technology is not preferred for long-range application.|
|Jennifer S. Raj ||Innovative Big Data-processing platform for IoT-based healthcare-monitoring system.||Data processing is divided into three stages: collection and aggregation, classification and analysis of collected data, and decision-making.||In comparison to the traditional model, it is efficient in the process of handling data and extracting information.||Data management, storage, f-measure, sensitivity, and specificity||Not provided||Data-processing time is not entertained.|
|Kishor and Chakraborty ||An approach to medical care that is underpinned by fog computing and makes use of AI and IoT||Three phases are involved. First, data are collected; then, they are pre-processed and computed; and lastly, the results are made visible to doctors or end-users and stored in the cloud.||This model assists medical professionals in making accurate and timely diagnoses of the disease.||Heart disease, diabetes, breast cancer, hepatitis, liver disorder, dermatology, surgery data, and thyroid data.||Not provided||Predicts only the common diseases|
|Souri et al. ||A student healthcare-monitoring system based on the IoT.||This methodology has three levels: finding the relevant data, collecting the data, and pre-processing the data.||Utilizes innovative medical technologies and identifies changes.||Biological and behavioral changes.||Not provided|
|Kaur et al. ||Enhancing the interaction between patients and medical professionals||Eight datasets on different diseases were used to test the proposed work.||Five machine learning techniques.||Provides automatic recommendations.||Accuracy and area under the curve||Not provided||The performance comparison displayed here only includes accuracy and area under curve (AUC).|
|SoonHyeong et al. ||Enhanced reliability and security through the implementation of blockchain technology.||This study used blockchain-based IoT. Several sensors were used to assess ECG data.||Integrated sensor module||BP, HR, temperature, weight, and ECG||BLE||Stored data/information can be transferred through smartphone only.|
|Piyush et al. ||Offers a mechanism for improving the quality of life of Alzheimer’s patients, and also benefits the people who care for them.||The study utilized IoT-based sensor data to determine various patient body parameters. All these sensors, attached to the MCU, are then transferred to the cloud.||LM35, pulse sensor, Gyroscope MPU6050, Atmega328 microcontroller, and ESP8266.||Dynamic estimation.||BT, BP, striding action, and speed.||IEEE 802.11||Cannot predict the condition of the patient before the situation becomes worse.|
|Hashim et al. ||Developed an IoT-based healthcare-monitoring system with multiple sensors and a smart security system.||Multiple sensors are connected to Arduino, and the collected data are presented on an LCD. The Wi-Fi module transmits data to the cloud.||DHT11, pulse sensor, mlx 90164, Arduino, LCD, and ESP8266 Wi-Fi module.||HR, BT, room temperature, and humidity.||IEEE 802.11||The size of the prototype needs to be reduced and enhanced.|
|Mostafa et al. ||Designed an IoT that can monitor patients’ readings continuously; keeps the data on display in front of the patient and on the screen of the doctor’s mobile device.||Three sensors are read by MCU with availability to represent the data locally and remotely.||Max30100, DS18B20, IR sensor, NodeMCU, and LCD.||HR, SpO2, and temperature||Wi-Fi/802.11||The prototype’s size should be minimized.|
|Jenifer et al. ||Designed an IoT based on electronic sensors to monitor patient healthcare remotely.||Sensors collect data on various physical factors and upload them to the cloud database over Wi-Fi.||LM35, Arduino Uno, SIM300, GPS shield.||Automatic emergency alert message and location can be sent.||HR, temperature, BP, and SpO2 level||IEEE 802.11|
|Dhruba et al. ||Developed a real-time sleep apnea-monitoring system based on the IoT.||Takes readings of sensors and measures several sleep indices, and alerts users via a mobile application when anything unusual occurs.||Max 30102, pulse sensor, GSR sensor, AD8232 and sound sensor, Arduino Uno, and Bluetooth module.||GSR, ECG, HR, sound, and SpO2.||BLE||During sleep, the worn device can be detached and feel uncomfortable to the patient.|
|Tiwari et al. ||Designed a system for remote monitoring of healthcare based on IoT.||Performs ongoing observation of a patient’s vital signs and detects the presence of abnormalities.||LM35, MAX30100, AD8232 and IR sensors, NodeMCU, and Arduino IDE.||Simple to operate and affordable due to its high level of cost effectiveness.||HR, temperature, and ECG.||MQTT, HTTP|
|Vaneeta et al. ||Conceived and built an intelligent health-monitoring system based on the IoT.||Consists of three primary steps: data collection, data processing, data storage, and the display of patients’ parameters locally and remotely.||MLX90614 and MAX30100 sensors, BP serial port, LCD, and Raspberry Pi.||This system will send an alert to the attending doctor or physician if there have been any deviations from the normal values of the patient’s health.||BP, HR, SpO2, and temperature.||IEEE 802.11||Need to increase the security of patients’ data and decrease the data-transfer delay.|
|Khan et al. ||Established a mechanism for measuring multiple health indicators quickly.||Sensors capture information on various physical factors and upload them to the cloud using the Bluetooth module.||LM35, MAX30100, Arduino UNO, Bluetooth module, and LCD.||Data can be monitored using mobile app.||BT, HR, and SpO2.||BLE||The size of prototype needs to be enhanced.|
- The IoT has the potential to be integrated with a wide variety of devices, which is not possible with most of the systems that are currently in use.
- There is the possibility that the data that are stored will not be protected.
- Complex systems have many disconnects between the various people, stages, and procedures.
- An investigation into the circumstances surrounding an accident will typically reveal the existence of several gaps, but gaps themselves are rarely the cause of accidents.
- The ability to understand and reinforce the normal ability of practitioners in order to bridge gaps contributes to an increase in overall safety.
- The conventional viewpoint, which maintains that systems ought to be shielded from the unreliable influence of humans, is challenged by this point of view.
- We have a limited understanding of how professionals pinpoint newly formed gaps and devise solutions to close them when systems undergo transformation.
4. Internet of Wearable Things
4.1. Wireless Network Technologies for IoT Healthcare
4.2. Wearable Sensors in Healthcare-Monitoring Systems
4.2.1. Use Cases of Health-Monitoring Sensors
- Heart-rate detection/Cardiac monitoring systems/Stroke
- Body-temperature measuring
- Activity recognition
- Blood-glucose monitoring and hemoglobin concentration
- Respiration-rate detection and monitoring
- Sleep monitoring
- Alzheimer’s disease monitoring and Anemia detection
- Molecular diagnostics and Clinical diagnosis
- Blood-oxygen-saturation detection
4.2.2. Classification of Health-Monitoring Sensors
- Monitoring vital signs in hospitals.
- Aging in place and in motion.
- Assistance with motor and sensory impairments.
- Large-scale medical and behavioral research in the field.
4.2.3. Performance Evaluation of IoT Sensors
5. Security and Protocols for IoT Healthcare-Monitoring Systems
6. IoT Healthcare Challenges and Open Issues
6.1. Security-Based: Security and Privacy
6.2. QoS-Based: Performance, Fuctional Stability and Reliability, and Cost
6.3. Computational Intelligence-Based
7. Suggestions and Recommendations
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Abdulmalek, S.; Nasir, A.; Jabbar, W.A.; Almuhaya, M.A.M.; Bairagi, A.K.; Khan, M.A.-M.; Kee, S.-H. IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review. Healthcare 2022, 10, 1993. https://doi.org/10.3390/healthcare10101993
Abdulmalek S, Nasir A, Jabbar WA, Almuhaya MAM, Bairagi AK, Khan MA-M, Kee S-H. IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review. Healthcare. 2022; 10(10):1993. https://doi.org/10.3390/healthcare10101993Chicago/Turabian Style
Abdulmalek, Suliman, Abdul Nasir, Waheb A. Jabbar, Mukarram A. M. Almuhaya, Anupam Kumar Bairagi, Md. Al-Masrur Khan, and Seong-Hoon Kee. 2022. "IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review" Healthcare 10, no. 10: 1993. https://doi.org/10.3390/healthcare10101993