SAR.IoT: Secured Augmented Reality for IoT Devices Management.

Currently, solutions based on the Internet of Things (IoT) concept are increasingly being adopted in several fields, namely, industry, agriculture, and home automation. The costs associated with this type of equipment is reasonably small, as IoT devices usually do not have output peripherals to display information about their status (e.g., a screen or a printer), although they may have informative LEDs, which is sometimes insufficient. For most IoT devices, the price of a minimalist display, to output and display the device's running status (i.e., what the device is doing), might cost much more than the actual IoT device. Occasionally, it might become necessary to visualize the IoT device output, making it necessary to find solutions to show the hardware output information in real time, without requiring extra equipment, only what the administrator usually has with them. In order to solve the above, a technological solution that allows for the visualization of IoT device information in actual time, using augmented reality and a simple smartphone, was developed and analyzed. In addition, the system created integrates a security layer, at the level of AR, to secure the shown data from unwanted eyes. The results of the tests carried out allowed us to validate the operation of the solution when accessing the information of the IoT devices, verify the operation of the security layer in AR, analyze the interaction between smartphones, the platform, and the devices, and check which AR markers are most optimized for this use case. This work results in a secure augmented reality solution, which can be used with a simple smartphone, to monitor/manage IoT devices in industrial, laboratory or research environments.

IndoorCare: Low-Cost Elderly Activity Monitoring System through Image Processing.

The Portuguese population is aging at an increasing rate, which introduces new problems, particularly in rural areas, where the population is small and widely spread throughout the territory. These people, mostly elderly, have low income and are often isolated and socially excluded. This work researches and proposes an affordable Ambient Assisted Living (AAL)-based solution to monitor the activities of elderly individuals, inside their homes, in a pervasive and non-intrusive way, while preserving their privacy. The solution uses a set of low-cost IoT sensor devices, computer vision algorithms and reasoning rules, to acquire data and recognize the activities performed by a subject inside a home. A conceptual architecture and a functional prototype were developed, the prototype being successfully tested in an environment similar to a real case scenario. The system and the underlying concept can be used as a building block for remote and distributed elderly care services, in which the elderly live autonomously in their homes, but have the attention of a caregiver when needed.

Web AR Solution for UAV Pilot Training and Usability Testing.

Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human-drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies.

Probe and Sensors Development for Level Measurement of Fats, Oils and Grease in Grease Boxes.

The wide spread of food outlets has become an environmental and sanitation infrastructure problem, due to Fats, Oils and Grease (FOG). A grease box is used at the industrials facilities to collect the FOG, in a specific time window, while its quality is good for recycling (e.g., biodiesel) and it is economically valuable. After this period, it will be disposed at a cost. For the proper management of the grease boxes, it is necessary to know the quantity of FOG inside the boxes, which is a major problem, as the boxes are sealed and permanently filled with water. The lack of homogeneity of the FOG renders it not detectable by current probes for level detection in liquids. In this article, the design, development and testing of a set of probes for FOG level measurement, based on the principles used in sensors for the detection of liquids inside containers, is described. The most suitable probe, based on the capacitance principle, together with the necessary hardware and software modules for data acquisition and transmission, was developed and tested. After the development phase, the probe was integrated on a metropolitan system for FOG collection and grease box management in partnership with a grease box management company.

Leg stiffness and muscle power vs manometer in sportswomen with symptoms of stress urinary incontinence.

BACKGROUND: Pelvic floor muscle training can cure or alleviate stress urinary incontinence. This study aimed to evaluate maximum voluntary contractions of the pelvic floor muscle in sportswomen and verify the association with leg stiffness and muscle power, both maximal and submaximal. METHODS: The sample consisted of 41 sportswomen between 18 and 42 years of age. Pelvic floor muscle strength was measured by the manometer. The sportswomen were instructed to perform 3 maximum voluntary contractions of the perineum, held for 3 seconds. Maximal and submaximal leg stiffness and muscle power were measured with a force platform, in two conditions: 1st condition was the sub-maximal, double leg hop test, which was performed allowing sportswomen to self-select their preferred frequency and 2nd condition was the maximal double leg hop test, which was performed asking athletes to maximize hop height and minimize contact time on the top of the force platform for 6 consecutive hops. FINDINGS: Maximal and submaximal leg stiffness values increase with increasing age, weight, height, and body mass index, showing positive and significant (p <0.05) or close correlations. There are strong positive correlations between maximal and submaximal leg stiffness (r = 0.759) and between maximal and submaximal muscle power. Maximum voluntary contractions values decrease with increasing leg stiffness: the correlation is significant with maximal leg stiffness. INTERPRETATION: Maximum voluntary contractions values decrease with increasing leg stiffness and increase with increasing muscle power values. If the training program aims to increase muscle power, it may also increase maximum voluntary contractions.