We all know that the fifth generation of mobile communication technology (5G) is advancing rapidly, providing at least ten times the peak rate of 4G, millisecond-level transmission delay, and hundreds of billions of connection capabilities with a brand new network architecture, opening a new era of extensive interconnection of all things and deep human-machine interaction.

The application of 5G technology will break the limitations of space, optimizing people’s lives in multiple scenarios such as medical care and smart home medical care. Among them, the data interconnection of mobile medical devices—supporting real-time transmission of large amounts of human health data—assists medical institutions in achieving uninterrupted body monitoring for non-hospitalized wearers. At the same time, it can also achieve unified data transmission for all hospital equipment, such as medical monitors, portable monitors, etc., through medical platforms.

Remote surgical teaching—by live streaming surgical images and medical images remotely, combined with AR, helps grassroots doctors achieve off-site internship in surgical procedures.

Super ambulance—transmitting ultra-high-definition video and smart medical device data, assisting in-hospital doctors to grasp the condition of patients on the ambulance in advance.

Advanced remote consultation—combined with transmitted high-definition video and force sensing and feedback devices, providing doctors with a more realistic understanding of the patient’s condition and offering advanced consultations for patients.

Remote-controlled surgery—doctors perform remote surgery through robots based on real-time information transmitted over the 5G network, combined with VR and tactile perception systems.

Under the 5G network, diagnosis and treatment will break the geographical limitations, health management and preliminary diagnosis will become home-based, and doctors and patients will achieve more efficient allocation and connection, transforming traditional hospitals into health management centers. This article is primarily written by the author LineLian on real-time monitoring of users’ vital signs and environmental information through smart devices worn at home, using wireless transmission to send monitoring data to servers for real-time processing and feedback. At the same time, users, family members, and medical staff can also grasp the user’s vital signs and environmental status in real-time through a mobile APP, reducing the incidence of sudden illnesses among users (the elderly, children, and special populations), facilitating prevention and timely treatment.

The hardware system uses Arduino Mega2560 [The Mega2560 is a core circuit board with USB interface, featuring 54 digital input/output channels, suitable for designs requiring a large number of IO interfaces. It can be powered in three ways and can automatically select the power supply method.] and Arduino Uno [The Arduino Uno development board is based on the ATmega328 MCU controller and has 14 digital input/output pins (6 of which can be used for PWM output), 6 analog inputs, a 16MHz ceramic resonator, a USB interface, a power socket, an ICSP connector, and a reset button. It uses the Atmega16U2 chip for USB-to-serial data conversion.] as core controllers, with multiple sensors working together to monitor users’ vital sign data in real-time. Among them, the MDL0019 wristband heart rate measurement module, combined with the Pulse Sensor pulse sensor worn on fingertips, earlobes, and other parts, can improve accuracy and reduce errors; the GSR skin current sensor monitors users’ emotional fluctuations; the MLX90615 infrared temperature sensor monitors surface temperature; and the AD8232 single-lead ECG module monitors patients’ ECG data and generates ECG charts. Meanwhile, the ESP8266 Wi-Fi module is used to transmit data to the server for intelligent analysis.

Additionally, considering that the physical condition of certain patients and special users can be greatly affected by environmental conditions, the system also incorporates an environmental monitoring module. It consists of a PM2.5 dust sensor, GY-68 BMP180 BOSCH temperature and pressure module, and light-sensitive sensor to form an external environment detection system. Furthermore, the system is equipped with emergency functions; when abnormal external environment or user vital sign data is detected in real-time, the server will send emergency alerts to the user’s family and medical staff, thus providing timely medical assistance to patients. The overall hardware system composition is shown in the figure below:

The IoT forwarding server and core analysis server

(1) Based on Tomcat and Django framework, MySQL database, and SQList to build the IoT cloud forwarding server;

(2) Using the Vert.x framework. The Vert.x framework is based on events and asynchronous operations, relying on the fully asynchronous Java server Netty, and has expanded many other features, lightweight, high-performance, and supports multi-language development;

(3) Data transmission uses AES encryption, Base64 encoding is simple, can be parallel computed, and does not transmit errors; it is not easily attacked (error transmission) and is suitable for long messages, conforming to SSL, IPSec standards; packets are converted to stream mode, allowing encryption of data smaller than packets.

Artificial intelligence, optoelectronic devices, high-end imaging molecular probes, and core components of endoscopes, as well as high-end active-related hot topics are in the “Technical Forum L: Core Components and Technology Forum for High-End Active Medical Equipment.” As a supporting forum for the 2021 Medtec China Exhibition, the forum was born to focus on high-end medical device design and service areas, with heavyweight guests speaking on-site! We look forward to your participation 👍

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(1) Using RxAndroid architecture allows for easy return of Android UI main thread objects compared to traditional Android frameworks;

(2) Using independently designed chart controls—FlaskChart can draw line charts, curve charts, and bar charts, allowing for color changes in lines and stretching of drawn charts, as well as the display of multiple charts together.

(1) View real-time vital sign data and external environmental data of users;

(2) Real-time viewing of users’ ECG, historical data via the internet, providing disease analysis for users, recording medication details, adding medication reminder functions, and providing exclusive medical advice for patients based on historical data analysis;

(3) When monitoring data abnormalities occur, the APP automatically alerts medical staff and family members. The mobile APP homepage requirement demonstration DEMO is shown below.

To ensure the stability of user monitoring data, a MySQL database was added on top of the high-performance IoT cloud forwarding server for easy management and storage of monitoring data. A web-based visualization management system was built using the bootstrap framework combined with ECharts visualization framework. Through visualization display, various patient data can be more intuitively presented, facilitating the management of patients’ conditions by medical staff and family members. The backend of the web visualization management system is shown in the demonstration image below.

The product gradually adopts various machine learning predictive classification algorithms to analyze the interaction between monitoring data and emergency department demand through mathematical models. In terms of method selection, we considered various algorithms such as decision trees, logistic regression, Adaboost, etc., comparing the actual results of test data with model predictions to evaluate the predictive accuracy of classifiers.

The method steps diagram is shown below:

The design of AI+ home healthcare products based on future 5G and current 4G IoT aims to reasonably integrate medical resources, provide personalized services to multiple groups, and relieve pressure on the social public medical security system. The main considerations are as follows:

(1) Analyze product usability and ease of use from the perspective of personalized product design; for example, the convenience of elderly users operating IoT devices.

(2) Analyze user needs and experience from the perspective of interaction design; since the product itself involves multiple ends, it is necessary to consider ease of use and friendly interaction across multiple devices.

(3) Propose to construct an intelligent medical system on a cloud computing platform from the perspective of system design, making the product, people, and system work together, and ensuring strong operability, compatibility, extensibility, and practical applicability.

Achieving the above product design may not be difficult for most product managers on the internet, but the challenge lies in seeing product design from 0 to 1.

Summarizing how to collect data from IoT and smart hardware devices, improving hardware performance stability and reducing costs. Discovering more considerate new functions for users.

Summarizing data mining to create more effective algorithm models for early prediction of patient illnesses and preventive treatment.

Summarizing how AI can empower home healthcare products, leading to AI-driven health and AI empowering more industries.

According to social science statistics, the current aging population in China is growing at a rate of 3.4% per year. By 2020, the elderly population in China will reach 248 million, and the aging level will reach 17.17%. The number of empty-nest elderly people is increasing; on the other hand, the lack of effective care for children has led to growing safety concerns. This is a pressing need, and we look forward to more products entering the market.

Image source: LineLian Smart Products