Ambient Assisted Working (AAW) is a discipline aiming to provide comfort and safety in the workplace through customization and technology. Workers’ comfort may be compromised in many labor situations, including those depending on environmental conditions, like extremely hot weather conduces to heat stress. Occupational heat stress (OHS) happens when a worker is in an uninterrupted physical activity and in a hot environment. OHS can produce strain on the body, which leads to discomfort and eventually to heat illness and even death. Related ISO standards contain methods to estimate OHS and to ensure the safety and health of workers, but they are subjective, impersonal, performed a posteriori and even invasive. This paper focuses on the design and development of real-time personalized monitoring for a more effective and objective estimation of OHS, taking into account the individual user profile, fusing data from environmental and unobtrusive body sensors. Formulas employed in this work were taken from different domains and joined in the method that we propose. It is based on calculations that enable continuous surveillance of physical activity performance in a comfortable and healthy manner. In this proposal, we found that OHS can be estimated by satisfying the following criteria: objective, personalized, in situ, in real time, just in time and in an unobtrusive way. This enables timely notice for workers to make decisions based on objective information to control OHS.

Software process standards (e.g. ISO/IEC 12207, ISO/IEC 15504) and models (e.g. CMMI) provide a set of best practices and guidelines for improving the quality of the software process and products resulting from that process. However, they do not prescribe a particular software development methodology (i.e. RUP, MSF), and thus software development teams face a compliance problem between the selected development methodology and a pursued particular standard or model. In this research, the particular issue of compliance of Agile Software Development Methodologies (SCRUM, XP, and UPEDU) and the new ISO/IEC 29110 standard is studied. Because the new standard is focused on the software process in very small software development companies or small software project teams in the range from 1 to 25 people, and the Agile Software Development Methodologies (ASDMs) are primarily for same targets, this study is important. The ISO/IEC 29110 standard contains two processes: Project Management and Software Implementation. This study is focused on the first process. The main findings indicate that the UPEDU and SCRUM methodologies present and high compliance level with the ISO/IEC 29110 Project Management process, while XP has a moderate level. Thus, software developer teams interested in achieving compliance with the ISO/IEC 29110 Project Management process can count with two ASDMs. However, a full compliance study (with both Project Management and Software Implementation) is still missing

Machine learning methods have been used to classify neurodegenerative diseases using gait data. Recent works with Huntington’s disease patients have reported results up to 88.2% of correct classification based on a probabilistic modeling approach for gait assessment. The aim of this work was to improve Huntington’s disease patients classification results while reducing the number of sensor devices to capture gait data and identifying the related gait features. The proposed method is based on general Assembles (Meta) Classifier algorithms approach, for the classification of Huntington’s disease affected gait versus Healthy Control subjects normal gait. The proposed methodology was tested on gait data recorded on Huntington’s disease patients and healthy control subjects using raw data from smart-phones movement sensors placed at both ankles. Best partial results of individual classifier algorithms are taken at each iteration of the meta-classifier, to predict the final result by averaging results and majority votes. Several instances of this combined approach were tested and validated. Obtained results confirm an improvement in accuracy, since 13 subjects out of a total of 14 were correctly classified. All 7 Huntington’s disease patients were correctly selected with Logitboost & RandomForest combination.

The most important thing immediately before, during and after a disaster occurs is the dissemination of information, a deployment of devices enabled by IoT (Internet of Things) could bring benefits in terms of giving to people information opportunely for making decisions in face of this disaster. In this paper, we present a sensor to measure water level in rivers, lakes, lagoons and streams. For such purpose and to prove our concept, we designed a pilot project through a micro-model that is constructed with a water level measurement sensor based on a simple open circuit that closes when in contact with water and experimentally tested into a water container under a controlled environment. This micro-model is performed on the basis of a programmable electronic board (Netduino Plus 2), an electronic circuit connected to electrical resistances that are located at a specific height, within a water container, when the water level rises and reaches the resistors, varies the impedance, this shows the actual water level and so on for different heights. The information from water level sensor is transmitted via WiFi to a laptop, then this information is also seen in smartphones, where users can see the water level in rivers. Finally, the micro-model is tested by experimental tests under a controlled environment and satisfactory results are obtained.

Knowing the perceived exertion of workers during their physical activities facilitates the decision-making of supervisors regarding the worker allocation in the appropriate job, actions to prevent accidents, and reassignment of tasks, among others. However, although wearable heart rate sensors represent an effective way to capture perceived exertion, ergonomic methods are generic and they do not consider the diffuse nature of the ranges that classify the efforts. Personalized monitoring is needed to enable a real and efficient classification of perceived individual efforts. In this paper, we propose a heart rate-based personalized method to assess perceived exertion; our method uses fuzzy logic as an option to manage imprecision and uncertainty in involved variables. We applied some experiments to cleaning staff and obtained results that highlight the importance of a custom method to classify perceived exertion of people doing physical work.

Social networks are useful tools for disseminating emergency warnings of danger to the population since it promises enormous success because most of the citizens have mobile phones with Internet and social networking applications. We can take advantage of designing a river level sensor to monitor in real time the water level of the river, in addition to storing historical data to forecast leveling or possible overflowing of rivers, it allows citizens to have an early warning system to be informed promptly and power make a decision before disaster strikes. The dissemination of information and deploying devices enabled by IoT (Internet of Things) could bring benefits in terms of giving to people information opportunely for making decisions in the face of this disaster. In this paper, we present a sensor to measure water level in rivers. We designed a pilot project through a water level measurement sensor based on an ultrasonic sensor, this sensor detects objects (water) by emitting a short ultrasonic burst and then listening for the echo, it calculates the distance between the sensor and the body of water. All data collected from the water level sensor are transmitted to a server. After social networks users such as Facebook can know the water level in rivers.