L.G. Jaimes

Florida Polytechnic University · Computer Science

This paper examines just-in-time adaptive interventions (JITAIs) for stress, a pervasive and affective computing application with significant implications for long-term health and quality of life. We discuss fundamental components needed to enabling JITAIs based for one kind of affect data stress. Chronic stress has significant long-term behavioral...

Advances in pervasive computing, machine learning, and human activity recognition are changing preventive health care. Emerging paradigms, such as Mobile Cyber-Physical System (MCPS) and Just-in-time interventions (JITI), allow patients to take health monitoring, diagnosis, therapy and treatments beyond traditional medical settings. These paradigms...

Advancements in ubiquitous computing are rapidly changing preventative health care these quick changes allow not only to track in real time the heath of an individual, but also to react to any anomalies that may indicate the need of help. This new health care paradigm (i.e., Just-in-time interventions) allows to support treatments and deliver help...

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with...

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with...

This paper presents an incentive mechanism for vehicular crowdsensing (VCS). Here, a platform selects a set of spots or Places of sensing Interest (PsI) and outsources the collection of data from these places. In particular, the platform is interested in collecting data from most of the PsIs (spatial coverage) at regular and well-spread time interv...

Advances in sensor technology and machine learning as well as the widespread use of smartphones are shifting the focus of healthcare. Emerging paradigms such as cyber-physical systems (CPSs) make possible the transition from reactive to preventive care. CPSs can be implemented to achieve effective mobile health solutions and to provide sophisticate...

According to the world report in 2019 about vision health presented by the World Health Organization (WHO), there are 2.2 billion people in the world with some kind of visual impairment. Furthermore, in a recent National Health Interview Survey report, 25.5 million adult Americans 18 and older reported experiencing vision loss. Of these 25.5 millio...

The rapid adoption of the vehicles and their on-board sensors as a primary means of transportation make them natural candidates for the outsourcing of data collection. However, vehicles mobility patterns tend to cluster into specific regions such as highways and popular roads, that makes their utilization difficult for data collection in isolated r...

In this paper, we present an incentive mechanism for Vehicular Crowdsensing in the context of autonomous vehicles (AVs). In particular, we propose a solution to the problem of sensing coverage of regions located out of the AVs’ planned trajectories. We tackle this problem by dynamically modifying the AVs’ trajectories and collecting sensing samples...

Currently, multi-agent mobile robotic testbeds are expensive, inaccessible, and restricted characteristics. Besides, the remotely accessible testbeds have limitations for specific tasks and limited time of implementation. For these reasons, we designed, produced, and implemented a testbed with excellent qualities for everybody. The ARGroHBotS is an...

In this paper, we present an incentive mechanism for Unmanned Aerial vehicles (UAVs) crowdsensing. In particular, we propose a solution to the problem of sensing coverage of lower regions of the atmosphere where a set of UAVs transverse it as part of their daily activities. We propose a UAV sensing market where data collection is an additional by-p...

Over past few decades, various ways have been conducted through side channel attacks to steal information for a computer system. Unlike conventional hardware-based methods, i.e. power-based side channel, side channel on micro-architecture does not require any physical access to the devices under interest. Instead, only compromised programs need to...

Monitoring environmental variables in lower layers of the atmosphere is an important activity to measure changes that result from natural events and human interventions. Volcano eruptions, commercial aviation, and the massive spread of pesticides using light aircraft are just some examples of low layer atmosphere polluters. Twice a day, every day o...

Crowdsensing refers to an approach for collecting of data from a large number of smart devices and sensors carried by many individuals and has been employed for numerous applications, which include pollution monitoring, traffic monitoring and noise sensing. It is an important mechanism for building applications in the smart environments enabled by...

A recent study showed that around 77% of the US population experiences regularly physiological symptoms of stress such as sleep deprivation, fatigue, and migraines and 73% psychological symptoms such anxiety, angry, and lack of focus. Thus, psychological stress remains an important issue of public health. In this paper, we propose a dynamical syste...

This paper proposes an algorithm based on a fuzzy logic approach, capable to guide a robot swarm with the aim to keep a leader-follower formation without colliding with other swarm agents. The fuzzy system is programmed and evaluated originally in Matlab, where several experiments were performed. The results depicted a robot swarm showing some bio-...

In this article, we describe and evaluate a crowdsensing approach that entails local cooperation between crowdsensing participants in smart environments, utilizing an underlying fog computing-enabled Internet of Things. A fog computing-based Internet-of-Things architecture involves a layer of computing nodes residing closer to the sensing devices,...

Spike neuron networks are a closer approximation to biological neurons. Integrate and fire (I&F) is one of the most popular models of spike neurons. This paper proposes a simplified model of the I&F neuron with the purpose of implementing it in an FPGA. The simplified model was developed in FPGA preserving the most important features of the model....

According to the American Psychological Association, 49% of the U.S. population suffers from chronic, daily stress. Chronic stress also has significant long-term behavioral and physical health consequences, including an increased risk of cardiovascular disease, cancer, anxiety and depression. In this work, we examine how smartphones and mobile sens...

Crowdsensing in the case of health-related information can enable a new mode of health data collection, able to collect more detailed and real-world health and physiological data than possible via traditional means. Various incentivization schemes for crowdsensing in general have been proposed, but there are characteristics specific to the health s...

Internet Security is a growing issue within the modern world. One of the weakest links in computer security is the use and misuse of passwords. As knowledge and technology becomes more widely spread, the methods of hacking passwords become increasingly easier and more accessible. With so much data being protected by passwords, it is important that...

Recent studies have modeled the incentive mechanism as a complete information game where the contributors have common knowledge. However, that assumption is not realistic in real world scenarios. In this paper, we present an incentive mechanism for CS in sealed markets in which participants have incomplete information on other participants’ behavio...

In this paper, we design an incentive mechanism for data collection in smart cities. We propose an incentive mechanism for crowdsensing with multiple crowdsourcers. We model the incentive mechanism as a non-cooperative game. We consider two different pricing mechanisms when the crowdsourcers fixed the rewards in advance, and when the crowdsourcers...

According with the World Health Organization, Falls are the second leading cause of accidental or unintentional injury deaths worldwide. Adults older than 65 suffer the greatest number of fatal falls. Therefore, the quality of life of older people can be improved by using automatic fall detection systems. This paper presents a fall detection system...

Work-related stress is normal and at low levels it can actually increase productivity. However, the accumulation of stress may have significant long-term behavioral and physical health consequences such as sleep deprivation, and anxiety disorder. According to the American Psychological Association, around 49% of the U.S. population suffers from chr...

Floods are a constant threat throughout the year to the United Stated and its territories like Puerto Rico. Although there are various methods of alerts available; such as the Emergency Broadcast System or sirens, none of these can alert a user remotely in an efficient and timely manner. The design goal of this project is to provide a real-time sys...

Crowdsensing (CS) is a new data collection paradigm based on the willingness of people to utilize their mobile devices to sense and transmit data of interest. Given the large amount of cellular users, mobile sensor networks will be able to collect enough data to address large-scale societal problems in a fast, easy, and cost-effective manner. One i...

We present A-PIE, a hybrid privacy-preserving mechanism for Participatory Sensing Systems that provides a high level of privacy protection as well as a high quality of information while minimizing the energy consumption. A-PIE takes into consideration the variability of the variable of interest to identify clusters, and divides the target area in c...

Crowd Sensing (CS) is a sensing paradigm that takes advantage from the increasing use of mobile smart devices and their capabilities for sensing and computation. In this paper, we present an incentive mechanism for encourage user participation in schemes of sensing that requires consecutive and regular sampling. The proposed mechanism uses a recurr...

Crowd sensing (CS) is an approach to collecting many samples of a phenomena of interest by distributing the sampling across a large number of individuals. While any one individual may not provide sufficient samples, aggregating samples across many individuals provides high-quality, high-coverage measurements of the phenomena. Thus, for participator...

Crowd sensing (CS) is an approach that consists of collecting many samples of a phenomena of interest by distributing the sampling process across a large number of individuals. In this work, we address the effect of cooperation among individuals by modeling a recurrent CS task as a repeated game. In this game, participants are the players of the co...

Crowd sensing (CS) is a new sensing paradigm that takes advantage of the availability of mobile devices almost in every place. In this type of system, the mobile phones users are asked to use their resources such as data plan, energy and time, in order to collect and transmit data to a central infrastructure. Since participants usually do not recei...

Chronic stress has significant long-term behavioral and physical health consequences, including an increased risk of cardiovascular disease, cancer, anxiety and depression. This paper conducts post-hoc experiments and simulations to demonstrate feasibility of both real-time stress forecasting and stress intervention adaptation and optimization. Usi...

Crowd sensing is an approach to collect many samples of a phenomena of interest by distributing the sampling across a large number of individuals. While any one individual may not provide sufficient samples, aggregating samples across many individuals may provide high-quality and high-coverage measurements of a phenomena. In this work, we propose a...

We present a mobile avatar system designed to provide a constant user-avatar interface for health behavior change therapy. The presented Android application replaces the user's phone background with an animated avatar. The avatar's level of physical activity is made to match the physical activity level of the user. This activity level is inferred u...

Personal Informatics (PI) systems help individuals collect and reflect on personal physiological, behavioral and/or contextual data. Typically, these systems offer users interactive visualizations that allow meaningful exploration of the data. Through this exploration, PI systems have great potential to facilitate self-reflection and encourage beha...

Participatory sensing (PS) systems rely on the willingness of mobile users to participate in the collection and reporting of data using a variety of sensors either embedded or integrated in their cellular phones. However, this new data collection paradigm has not been very successful yet mainly because of the lack of incentives for participation. A...

Several clustering algorithms include one or more parameters to be fixed before its application. This is also the case of fuzzy c-means, one of the most well-known fuzzy clustering algorithms, where two parameters c and m are required. c corresponds to the number of clusters and m to the fuzziness of the solutions. The selection of these parameters...