# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
#### 3.1.2 Real-Time Continuous Monitoring
If it detects the person is in pain, the system sends a notification to related entities, such as hospitals, health centers, general practitioners, and caregivers, so that they can be directed to the patient for immediate assistance. [PERSON] [23] presented a proposal for a miniature electrochemical \"Dongle\" (USB flash device comprising a reader and a sensor). This system allows diabetic patients' monitoring through the glucose and uric acid measurement. It includes a smartphone as an electrochemical analyzer, which powers the \"Dongle\" with an OTG cable; and a medical service platform for consulting, monitoring, and storing biochemical parameters, so that the doctor can monitor the patient's health status. The intelligent health monitoring system presented by [PERSON] et al. [27] was used to monitor a patient's vital signs and venous transfusion. The doctor in charge of performing the procedure was miles away from the individual. In addition, the system notifies the nursing service once the transfusion is completed for the removal of the needles. In case of emergency, it has a button to communicate with the nursing department. The system has body sensors and an intravenous injector. Autilife is a proposal submitted by [PERSON] et al. [28] to monitor children in autism centers. Emergency situations such as epilepsy, heart attacks, strokes, hysteria, and anxiety are situations that can occur in an autism center. The system focuses on improving their life quality, through the constant monitoring of physiological parameters such as blood pressure, body temperature, heart rate, speech signals, and body movements. The system includes WBSN in various accessories, such as a watch, belt, shoes, shirt and glasses, for the collection of parameters that are sent to a database through the cellular network. The SVM is used for classification of collected data and signals. The classification contains two classes: normal-health condition and endangered-health condition. If one's health is in danger, the system sends alarms to a nearby ambulance or hospital and to the autism center caregiver. The intelligent patient-monitoring system proposed by [PERSON] et al. [32] allows one to detect postsurgical falls and body movements, such as fast and slow walking, squatting, lying down, and sitting. The system's objective is to protect the patients' health by preventing falls that could lead to injuries and even death. The patients who benefit most from the system are the elderly, due to their lack of mobility and balance. With this resource, doctors can monitor patients 24 h a day. ML algorithms such as SVM, Naive Bayes, and decision tree were used for classification of six body movements, including falling. For training, the system uses repetitive body movement signal information from eight different individuals of different ages, through the use of low-cost body sensors. The results showed that SVM performed better in classifying body movements. [PERSON] et al. [33] proposed a telemonitoring and teleassistance system for monitoring patients suffering from diabetes. Body weight and blood glucose measurements can be taken through a glucose test kit that incorporates an NFC (near-field communication) glucometer, a lancing device, and a scale. By using a mobile app, health parameters can be recorded and monitored at any time. The mobile app can interact directly with the scale via a Bluetooth connection and with the glucometer via an NFC connection. The data parameters acquired on the mobile phone through the app are sent to a cloud platform via the cellular network. Medical staff can access the platform and monitor the patient's parameters in real time. Focused on people with diabetes, [PERSON] et al. [35] proposed a system that tracks the user's glucose level. To capture the measurements, an smart device (Dexcom G6) is used, which transmits the data to a smartphone via Bluetooth, and stores the values and generates an alert in case the measured values are not adequate. The home telemedicine system presented by [PERSON] et al.