SENHOD: Scarce-Resources Wireless Sensor Network for Healthcare in Oil Derricks

We present our experience with designing, developing, and deploying of a Scarce-resource Wireless Sensor Network (SWSN) for monitoring temperature and humidity high above oil derricks (drilling tower) in PEMEX (Parastatal Mexican Petroleum Company) drilling facilities. SENHOD system (scarce-resources wireless SEnsor Network for Healthcare in Oil Derricks) represents an information tool to reduce derrickmens’ health risk due to high temperature and humidity exposure during a working day. For our deployment we meet the design requirements, in accordance to the scenario and its necessities. SENHOD has suitable operation characteristics configurable by users, such as: operation modes, physical parameter selection, sensing rate, and awake-sleep nodes

Analysis of some Mobile Applications for Cycling

This article analyzes some available bike mobile applications as an alternative to bike computers, as known as cycle computers or speedometers or speed sensors. We have stored a lot of datasets recorded from different mountain bike routes; in this study, we analyzed two routes only. Most mobile cycling applications estimate fields such as speed, heading, slope, distance, VMG (Velocity Made Good) and pace (cadence). However, it is necessary to calculate the relationship between cadence and power in pedaling so that cyclists know the appropriate moment to apply force to their legs to improve the torque. We studied four cycling apps and one bike computer. The contribution of this paper lies in the fact that it reports and compares measurements of cycling workouts using four mobile applications for cycling, at the same time these measurements are compared against a speedometer; the differences in distance and speed between the mobile apps used in this study are slightly notorious. We also show comparative tables and graphs, and performance evaluation of biking routes in two different bike routes

Attention-Inspired Artificial Neural Networks for Speech Processing: A Systematic Review

Artificial Neural Networks (ANNs) were created inspired by the neural networks in the human brain and have been widely applied in speech processing. The application areas of ANN include: Speech recognition, speech emotion recognition, language identification, speech enhancement, and speech separation, amongst others. Likewise, given that speech processing performed by humans involves complex cognitive processes known as auditory attention, there has been a growing amount of papers proposing ANNs supported by deep learning algorithms in conjunction with some mechanism to achieve symmetry with the human attention process. However, while these ANN approaches include attention, there is no categorization of attention integrated into the deep learning algorithms and their relation with human auditory attention. Therefore, we consider it necessary to have a review of the different ANN approaches inspired in attention to show both academic and industry experts the available models for a wide variety of applications. Based on the PRISMA methodology, we present a systematic review of the literature published since 2000, in which deep learning algorithms are applied to diverse problems related to speech processing. In this paper 133 research works are selected and the following aspects are described: (i) Most relevant features, (ii) ways in which attention has been implemented, (iii) their hypothetical relationship with human attention, and (iv) the evaluation metrics used. Additionally, the four publications most related with human attention were analyzed and their strengths and weaknesses were determined

Fuzzy System to Assess Dangerous Driving: A Multidisciplinary Approach

Dangerous driving can cause accidents, injuries and loss of life. An efficient assessment helps to identify the absence or degree of dangerous driving to take the appropriate decisions while driving. Previous studies assess dangerous driving through two approaches: (i) using electronic devices or sensors that provide objective variables (acceleration, turns and speed), and (ii) analyzing responses to questionnaires from behavioral science that provide subjective variables (driving thoughts, opinions and perceptions from the driver). However, we believe that a holistic and more realistic assessment requires a combination of both types of variables. Therefore, we propose a three-phase fuzzy system with a multidisciplinary (computer science and behavioral sciences) approach that draws on the strengths of sensors embedded in smartphones and questionnaires to evaluate driver behavior and social desirability. Our proposal combines objective and subjective variables while mitigating the weaknesses of the disciplines used (sensor reading errors and lack of honesty from respondents, respectively). The methods used are of proven reliability in each discipline, and their outputs feed a combined fuzzy system used to handle the vagueness of the input variables, obtaining a personalized result for each driver. The results obtained using the proposed system in a real scenario were efficient at 84.21%, and were validated with mobility experts’ opinions. The presented fuzzy system can support intelligent transportation systems, driving safety, or personnel selection

Dynamic Fuzzy Model to Detect Verbal Violence in Real Time

The crime rates in Mexico have been increasing in recent years, every day there are news on social media and in the news where assaults and verbal aggressions by criminals can be seen. Public transportation units suffer from violence that authorities have not been able to reduce, despite their efforts. That is why we have developed a fuzzy logic model that can adapt to almost any scenario thanks to the dynamism that we have implemented in each one of its stages. We have obtained promising results that we believe will be of great help to the authorities in the police headquarters to detect in real time the exact moment in which a verbal aggression typical of a violent assault is happening. This is a tool to help the authorities, not a substitution; making use of the latest technologies available to us

A Fuzzy Logic-Based Personalized Method to Classify Perceived Exertion in Workplaces Using a Wearable Heart Rate Sensor

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