Cloud-based Management Platform Research Articles

Analysis of youth sports physical health data based on cloud computing and gait awareness

Abstract Sub-health problems are becoming increasingly serious in today’s society, and some organizations are not paying enough attention to adolescent sports health data. For adolescent sports, health needs to be measured regularly and tested constantly so that the intake of diet and medication can be reasonably adjusted according to their biochemical indicators. The Smart Health Life Growth Cloud System can effectively manage residents’ health data digitally and informally, enabling users to manage their health data better and facilitating doctors to keep abreast of users’ health conditions, while also facilitating the government to conduct research and studies on the physical fitness of adolescents in the areas under its jurisdiction. The cloud-based management platform for student physical health management relies on the mobile internet as a practical service platform whose primary role is to provide young people with a convenient sporting life, focusing on practicality, service, and interactivity. We also collect sensor data to detect gait patterns (with or without leg contact) and filter them through an adaptive hybrid filter to differentiate between the two patterns. In turn, the Smart Health Life Growth Cloud system changes the traditional medical model and greatly improves the information and intelligence of the healthcare industry. Using the exercise individual health evaluation model in this article is controlled to be within 20%, thus concluding that the exercise individual health evaluation model proposed in this article can predict the exercise limit of an exercise individual more accurately.

Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection

The inspection of bridge structural cracks is essential to the structural safety evaluation and could provide reference for preventive maintenance. The traditional bridge structure inspection methods rely heavily on trained engineers with professional equipment. While such kind of way could provide reliable crack inspection data, the enormous amount of existing bridges waiting for inspection challenges the efficiency of these methods. Fortunately, the development of smartphones facilitates the possibility of making the pedestrian taking smartphones a mobile sensing node, which is able to collect crack information such as images and locations. At the same time, the booming deep learning methods could offer remarkable crack detection capacity to deal with the crack images automatically. Given this consideration, this paper established a crowd-sensing-based system for bridge structural crack detection. The system was composed of the cloud-based management platform and the mobile based application (APP) for crack information collection. The mobile-based APP was used by the volunteer pedestrians to collect the crack images as well as the locations, and the location accuracy was estimated to be around 5~10 m. Meanwhile, the cloud-based management platform was used for the management of the users and the collected crack information uploaded by all of the volunteers. A deep neural network was used to deal with the crack detection tasks and evaluate the quality of the collected images to see if they could be fitted for crack detection in bridge inspection works.

Artificial intelligence (AI) based system for the diagnosis and classification of scalp health: AI-ScalpGrader

Many people suffer from scalp disorders but common treatment devices have faults such as inaccuracy of results and inconvenience of use. This study proposes a deep learning-based intelligent scalp diagnosis and classification system, named artificial intelligence (AI)-ScalpGrader. The proposed system consists of a portable scalp imaging device (ASM-202), a mobile device app, a cloud-based AI training server, and a cloud-based management platform. The instrumentation diagnoses and classifies ten scalp symptoms (normal, drying, oily, sensitivity, atopy, seborrheic, trouble, dry dandruff, oily dandruff, and hair loss) based on seven dermatologist-based indices (microkeratin, sebaceous, erythema between hair follicles, follicular erythema/pustules, dandruff, and hair loss) by AI-based characterization of the symptoms and indices. EfficientNet, a convolutional neural network (CNN) learning model, is MBConvolution composed with depthwise convolution, squeeze excitation, and width scaling and was adopted to diagnose and classify scalp conditions through retraining of images in the system. The results and verification on the reliability of AI-based data show that the system is able to diagnose and classify these symptoms and severity of the indices with accuracy values from 87.3 to 91.3%. Therefore, the AI-ScalpGrader is a novel approach to diagnose and classify scalp status.

ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health

Many people suffer from scalp hair problems such as dandruff, folliculitis, hair loss, and oily hair due to poor daily habits, imbalanced nutritional intake, high stress, and toxic substances in their environment. To treat these scalp problems, dedicated services such as scalp hair physiotherapy have emerged in recent years. This article proposes a deep learning-based intelligent scalp inspection and diagnosis system, named ScalpEye, as an efficient inspection and diagnosis system for scalp hair physiotherapy as part of scalp healthcare. The proposed ScalpEye system consists of a portable scalp hair imaging microscope, a mobile device app, a cloud-based artificial intelligence (AI) training server, and a cloud-based management platform. The ScalpEye system can detect and diagnose four common scalp hair symptoms (dandruff, folliculitis, hair loss, and oily hair). In this study, we tested several popular object detection models and adopted a Faster R-CNN with the Inception ResNet_v2_Atrous model in the ScalpEye system for image recognition when inspecting and diagnosing scalp hair symptoms. The experimental results show that the ScalpEye system can diagnose four common scalp hair symptoms with an average precision (AP) ranging from 97.41% to 99.09%.

AnyCharge: An IoT-Based Wireless Charging Service for the Public

Mobile phones have become a necessity of modern life and are used for various tasks, including audio calls, text messaging, Web surfing, video streaming, gaming, and payments. Most of these tasks consume significant battery power, which makes public charging services increasingly important. Therefore, there are many public places and private stores have started to provide free charging services. However, there are several issues that restrict the popularity of public charging service. First, people cannot easily find charging spots. Second, there is no effective way to monitor charging status and manage the chargers. Third, a free charging service increases business expenses. To address these issues, we developed a Internet of Things (IoT)-based wireless charging service system, which is called AnyCharge. There are five major components of AnyCharge: 1) a Wi-Fi enabled wireless charger; 2) an IoT gateway; 3) a cloud-based management platform; 4) a secure Wi-Fi auto-connection algorithm; and 5) a mobile app. The charger is connected to an IoT gateway through Wi-Fi using our secure auto-connection algorithm, and the gateways are linked to the cloud server using message queue telemetry transport. The administrators can monitor and control chargers using the management platform. In addition, Android and iOS apps have been created to allow users to locate free chargers and find the shortest route to the nearest charging spot. We initiated a large-scale experimental deployment in Taiwan, Thailand, Singapore, and Japan. More than 200 chargers have been installed in different places, including restaurants, hospitals, telecom stores, and hotels. Statistics shows that there is a strong demand for public charging services.

A Deep Learning-Based Intelligent Medicine Recognition System for Chronic Patients

This paper proposes an intelligent medicine recognition system based on deep learning techniques, named ST-Med-Box. The proposed system can assist chronic patients in taking multiple medications correctly and avoiding in taking the wrong medications, which may cause drug interactions, and can provide other medication-related functionalities such as reminders to take medications on time, medication information, and chronic patient information management. The proposed system consists of an intelligent medicine recognition device, an app running on an Android-based mobile device, a deep learning training server, and a cloud-based management platform. Currently, eight different medicines can be recognized by the proposed system. The experimental results show that the recognition accuracy reaches 96.6%. Therefore, the proposed system can effectively reduce the problem of drug interactions caused by taking incorrect drugs, thereby reducing the cost of medical treatment and giving patients with chronic diseases a safe medication environment.

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergency-related announcements is approximately 7 s.

Design and Implementation of a Drowsiness-Fatigue-Detection System Based on Wearable Smart Glasses to Increase Road Safety

This paper proposes a drowsiness-fatigue-detection system based on wearable smart glasses to increase road safety. The proposed system is composed of a pair of wearable smart glasses, an in-vehicle infotainment telematics platform, an on-board diagnostics-II-based automotive diagnostic bridge, an active vehicle rear light alert mechanism, and a cloud-based management platform. A dedicated miniature bandpass infrared (IR) light sensor is also proposed and implemented for the low-cost, lightweight, wearable smart glasses, which can provide a higher signal-to-noise ratio than a general commercial IR light sensor, minimize the ambient environmental light image, and efficiently increase the accuracy of detection. The proposed system can detect the status of the vehicle driver with respect to drowsiness or fatigue conditions in real time. When drowsiness or fatigue is detected, the active vehicle real light alert mechanism will automatically be flickered to alert following vehicles. The related information will also be concurrently transmitted to a cloud-based management platform. As a result, the proposed system can lead to increased road safety.

Combination of micro and cloud-based system to ambulance

This paper presents the integrated system of patients vital monitoring in the ambulance. It provides a real time cloud-based management platform of the ambulance tracking function with Raspberry Pi 2B which integrates 3G dongle, webcam and GPS model by using the commercial SpaceLabs bedside monitor. Through this platform structure to monitor the instruments, emergency medical personnel can also monitor the patient’s condition. The results show the feasibility to transmit current information to server and save in database by developed home-made system. By means of the internet database management of the transfer patients, the hospital staff can facilitate distal supervision and management. The system in this study has been completed commercial prototype test.