Del-Valle-Soto, Carolina
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Preferred name
Del-Valle-Soto, Carolina
Official Name
del Valle Soto, Carolina
Alternative Name
cvalle
Main Affiliation
ORCID
Scopus Author ID
56257456700
Researcher ID
T-5779-2017

Research Output
Now showing 1 - 10 of 78
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Preface

2021 , Del-Valle-Soto, Carolina

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A Comprehensive Review of Behavior Change Techniques in Wearables and IoT: Implications for Health and Well-Being

2024 , Del-Valle-Soto, Carolina , López-Pimentel, Juan Carlos , Vázquez Castillo, Javier , Nolazco Flores, Juan Arturo , Velázquez, Ramiro , José Varela-Aldás , Visconti, Paolo

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals’ overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit.

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A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

2021 , Del-Valle-Soto, Carolina , Carlos Mex-Perera , Juan Arturo Nolazco-Flores , Rodríguez Vázquez, Alma Nayeli , Rosas-caro, Julio , Alberto F. Martínez-Herrera

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

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Remotely Vital Signs Capturer for Older Adults Applied in Residential Zones

2022 , Del-Valle-Soto, Carolina , Valdivia, Leonardo , Velázquez, Ramiro , Paolo Visconti

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Implications for Serious Game Design: Quantification of Cognitive Stimulation in Virtual Reality Puzzle Games through MSC and SpEn EEG Analysis

2024 , Gomez Romero Borquez, Jesus Alberto , Del-Valle-Soto, Carolina , Del-Puerto-Flores, J. Alberto , Castillo-Soria, Francisco R. , Maciel-Barboza F.M.

This paper investigates the cognitive stimulation experienced by players engaging in virtual reality (VR) puzzle games through the analysis of electroencephalography (EEG) data. The study employs magnitude-square coherence (MSC) and spectral entropy (SpEn) metrics to quantify neural activity patterns associated with problem-solving processes during gameplay. Results reveal unique coherence and entropy profiles across different VR gaming tasks, with Tetris gameplay eliciting heightened coherence and entropy values compared to other games. Specifically, Tetris demonstrates increased coherence between frontal and temporal brain regions, indicative of enhanced visuospatial processing and decision making. These findings underscore the importance of considering both spectral coherence and entropy when assessing the cognitive effects of video game tasks on brain activity. Insights from this study may inform the design of serious VR games aimed at promoting cognitive development and problem-solving skills in players.

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Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science

2024 , Arce Rodríguez, Mariana , Pérez-Díaz, Heráclito , Del-Valle-Soto, Carolina , Ramon A. Briseño

Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.

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Energy-Efficient Analysis in Wireless Sensor Networks Applied to Routing Techniques for Internet of Things

2019 , Del-Valle-Soto, Carolina , Durán-Aguilar, Gabriela , Cortes-Chavez, Fabiola , Rossa Sierra, Alberto

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Mapping EEG Alpha Activity: Assessing Concentration Levels during Player Experience in Virtual Reality Video Games

2023 , Gomez Romero Borquez, Jesus Alberto , Del-Puerto-Flores, J. Alberto , Del-Valle-Soto, Carolina

This work presents a study in which the cognitive concentration levels of participants were evaluated using electroencephalogram (EEG) measures while they were playing three different categories of virtual reality (VR) video games: Challenging Puzzlers, Casual Games, and Exergames. Thirty-one voluntary participants between the ages of 17 and 35 were recruited. EEG data were processed to analyze the brain’s electrical activity in the alpha band. The values of power spectral density (PSD) and individual alpha frequency (IAF) of each participant were compared to detect changes that could indicate a state of concentration. Additionally, frontal alpha asymmetry (FAA) between the left and right hemispheres of the brain was compared. The results showed that the Exergame category of video games elicited higher average cognitive concentration in players, as indicated by the IAF and FAA values. These findings contribute to understanding the cognitive effects of VR video games and their implications for designing and developing VR experiences to enhance cognitive abilities.

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Performance Evaluation of Active and Passive Haptic Feedback in Shape Perception

2019 , Velázquez, Ramiro , Edwige Pissaloux , Del-Valle-Soto, Carolina , Masayuki Arai , Valdivia, Leonardo , Del-Puerto-Flores, J. Alberto , Gutierrez, Carlos A.

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Novel Detection Methods for Securing Wireless Sensor Network Performance under Intrusion Jamming

2019 , Del-Valle-Soto, Carolina , Valdivia, Leonardo , Rosas-caro, Julio

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