Wearable Technology and Visual Reality Application for Healthcare Systems

This study developed a virtual reality interactive game with smart wireless wearable technology for healthcare of elderly users. The proposed wearable system uses its intelligent and wireless features to collect electromyography signals and upload them to a cloud database for further analysis. The electromyography signals are then analyzed for the users’ muscle fatigue, health, strength, and other physiological conditions. The average slope maximum So and Chan (ASM S & C) algorithm is integrated in the proposed system to effectively detect the quantity of electromyography peaks, and the accuracy is as high as 95%. The proposed system can promote the health conditions of elderly users, and motivate them to acquire new knowledge of science and technology.

Intelligent Robotic Palletizer System

In the global wave of automation, logistics and manufacturing are indispensable and important industries. Among them, the related automatic warehousing system is even more urgently needed. There are quite a few cases of using robotic arms in the current industry cargo stacking operations. Traditional operations require engineers to plan the stacking path for the robotic arm. If the size of the object changes, it will take extra time to re-plan the work path. Therefore, in recent years, quite a lot of automatic palletizing software has been developed; however, none of it has a detection mechanism for stacking correctness and personnel safety. As a result, in this research, an intelligent robotic palletizer system is developed based on a self-developed symmetrical algorithm to stack the goods in a staggered arrangement to ensure the overall structure. Innovatively, it is proposed to check the arrangement status and warnings during the visual stack inspection to ensure the correctness of the stacking process. Besides, an AI algorithm is imported to ensure that personnel cannot enter the set dangerous area during the work of the robotic arm to improve safety during stacking. In addition to uploading the relevant data to the cloud database in real time, the stacking process combined database and vision system also provide users with real-time monitoring of system information.

Literature Review on Cloud Database

Cloud computing have high interest from companies .since ,its inception With its services delivery model, cloud computing add technical and strategic business value to companies. This paper presents a systematic literature review to explore the current key issues related to cloud computing adoption.

An Efficient Cloud Framework for Multi-Robot System Management

Efficient knowledge sharing, computation load minimization, and collision-free movement are very important issues in the field of multi-robot automation. Several cloud robot architectures have been investigated to fulfill these requirements. However, the performance of the cloud-robot architectures created to date are suboptimal due to the lack of efficient data management for multi-robotic systems. With this point in mind, this paper proposes an efficient cloud multi-robot framework with cloud database model for mobile robot applications to facilitate multi-robot management, communication, and resource sharing. In this proposed architecture, the cloud framework is comprised with cloud data analysis, cloud database management, and cloud service management. The data analysis serves different data processing and decision-making tasks for generating the next robot action based on robot sensors’ data with the help of a data access components layer. A multistage cloud database model distributes, stores, and accesses different categories of data related to robot sensors and environments. And cloud service facilitates multi-robot management, communication, and resource sharing in the cloud framework. Additionally, as a use case, a cloud-based convolutional neural network (CNN) model is introduced for learning and recognizing robot application data. The obtained results of our tests indicate that the proposed cloud-robot architecture provides efficient computation power, communications, and knowledge sharing for managing multi-mobile robot systems.

APPLICATION OF CULTURAL HERITAGE HEALTH CHECK IN THE IMMOVABLE CULTURAL HERITAGE CONSERVATION PRACTICE IN BEIJING

Taking the immovable heritage in Daxing, Changping and Fangshan Districts in Beijing as case studies, starting from direct needs of the basic level, this paper aims to explore and establish a rapid, effective, promotional and regionalized risk screening and preventive conservation methodology for immovable cultural heritage by establishing work indicators and “cultural heritage health check”. Digital technologies and cloud database are used as recording and analysis tools.

Development of a Sensor-Based Heartbeat and Body Temperature Monitoring System for Remote Chronic Patients

The growing number of chronic diseases have stretched the healthcare sector. Globally, more than 36 million deaths per year are attributed to chronic disease complications. This has increased the demand for telemedicine in managing chronic patients as they must be on continuous monitoring for a long time. The involvement of wireless sensor networks and cloud computing technology in the health sector is increasing due to the potential it possesses in remote sensing and monitoring applications. This paper presents a developed system prototype for monitoring the heartbeat rate and body temperature of chronic patients using sensors. The monitored data are sent to a cloud database in real-time via an internet connection using the ESP8266 wireless module. The approach involves connecting a heart pulse sensor, an MLX90614 contactless temperature sensor, and the ESP8266 module to the Arduino development board. The goal of this work is to create a system that interfaces chronic patients and medical personnel in an attempt to avert the effects of insufficient health facilities, especially in rural Africa. The patient’s data in the cloud database can also be retrieved by medical personnel anytime in order to track the patient’s conditions and to advise the patient accordingly. The sensed heartbeat and body temperature readings were processed, sent, and recorded in the cloud database effectively.

Feature Matching Synchronized Reasoning from Energy-Based Memory Network for Intelligent Data Management in Cloud Computing Data Center

A cloud data center for software-as-a-service (SaaS) was built for the purpose of stably managing these server computers in one place in order to provide an uninterrupted service, not only for a stable power supply and security but also for the efficient data management. To manage such a data center efficiently, it is important to build a cloud database with structured storage above all else. In recent decades, many studies have focused on designing cloud data centers and most of the research has focused on communication traffic, routing, topological issues and communication technology. However, in order to build an efficient cloud database that can support user demand, the most sophisticated intelligent system, based on AI technology and considering user convenience, should be designed. From this viewpoint, adopting human brain functions, Energy-Based Memory Network was designed for a knowledge-based frame of an intelligent system. And its event-related synchronized data extraction mechanism was proposed. In particular, a Thinking Thread extraction phase was implemented for the reasoning process using qualia matching and a deep extraction method in a cloud database. The purpose of this approach is to design and implement an intelligent cloud database that has an efficient structure and mechanism for supporting user demand and providing accurate, prompt services. In experiments, the working phase of the functions was simulated with data and analyzed. As a result, it was confirmed that the proposed system works well and intelligently for the design purpose.

Residential elbow joint rehabilitation system by smartphone with cloud database

BACKGROUND: Physical therapy treatment has gradually become important in hospitals. This paper focused on elbow joint rehabilitation, as this form of rehabilitation is used most often. Moreover, most elbow joint rehabilitation programs could be conducted at home without going to a medical institution, which will economize medical manpower. OBJECTIVE: How to judge the correct rehabilitation motion becomes an issue for elbow joint rehabilitation at home. Therefore, this study proposed a residential elbow joint rehabilitation system (REJRS) by smartphone with a cloud database to address these issues. METHODS: REJRS has the ability to judge the correct motions and times of rehabilitation in real time. When the rehabilitation motions are incorrect, the number of rehabilitation repetitions is insufficient, or a timed rehabilitation session is insufficient, the patient will receive a warning text and light alert by REJRS. Then, the data of rehabilitation sessions are uploaded to the cloud database immediately. RESULTS: Patients can query their rehabilitation data at all times. Moreover, medical staff can track the status of each patient’s rehabilitation at any time and any place by downloading the data from the cloud database via the Internet. In our experimental results, the rate for detecting the correct elbow joint rehabilitation motion was up to 90%. CONCLUSIONS: The results show that REJRS could be applied for residential elbow joint rehabilitation. In the future, REJRS will be verified by the Institutional Review Board (IRB) for application to clinical treatment.