Del-Valle-Soto, Carolina
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Del-Valle-Soto, Carolina
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del Valle Soto, Carolina
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cvalle
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Scopus Author ID
56257456700
Researcher ID
T-5779-2017

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Energy Harvesting Technologies and Devices from Vehicular Transit and Natural Sources on Roads for a Sustainable Transport: State-of-the-Art Analysis and Commercial Solutions

2023 , Roberto De Fazio , Mariangela De Giorgi , Donato Cafagna , Del-Valle-Soto, Carolina , Paolo Visconti

The roads we travel daily are exposed to several energy sources (mechanical load, solar radiation, heat, air movement, etc.), which can be exploited to make common systems and apparatus for roadways (i.e., lighting, video surveillance, and traffic monitoring systems) energetically autonomous. For decades, research groups have developed many technologies able to scavenge energy from the said sources related to roadways: electromagnetism, piezoelectric and triboelectric harvesters for the cars’ stress and vibrations, photovoltaic modules for sunlight, thermoelectric solutions and pyroelectric materials for heat and wind turbines optimized for low-speed winds, such as the ones produced by moving vehicles. Thus, this paper explores the existing technologies for scavenging energy from sources available on roadways, both natural and related to vehicular transit. At first, to contextualize them within the application scenario, the available energy sources and transduction mechanisms were identified and described, arguing the main requirements that must be considered for developing harvesters applicable on roadways. Afterward, an overview of energy harvesting solutions presented in the scientific literature to recover energy from roadways is introduced, classifying them according to the transduction method (i.e., piezoelectric, triboelectric, electromagnetic, photovoltaic, etc.) and proposed system architecture. Later, a survey of commercial systems available on the market for scavenging energy from roadways is introduced, focusing on their architecture, performance, and installation methods. Lastly, comparative analyses are offered for each device category (i.e., scientific works and commercial products), providing insights to identify the most promising solutions and technologies for developing future self-sustainable smart roads.

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Wearable Urban Mobility Assistive Device for Visually Impaired Pedestrians Using a Smartphone and a Tactile-Foot Interface

2021 , Tachiquin, Ricardo , Velázquez, Ramiro , Del-Valle-Soto, Carolina , Gutierrez, Carlos A. , Miguel Carrasco , Roberto De Fazio , Andrés Trujillo-León , Paolo Visconti , Fernando Vidal-Verdú

This paper reports on the progress of a wearable assistive technology (AT) device designed to enhance the independent, safe, and efficient mobility of blind and visually impaired pedestrians in outdoor environments. Such device exploits the smartphone’s positioning and computing capabilities to locate and guide users along urban settings. The necessary navigation instructions to reach a destination are encoded as vibrating patterns which are conveyed to the user via a foot-placed tactile interface. To determine the performance of the proposed AT device, two user experiments were conducted. The first one requested a group of 20 voluntary normally sighted subjects to recognize the feedback provided by the tactile-foot interface. The results showed recognition rates over 93%. The second experiment involved two blind voluntary subjects which were assisted to find target destinations along public urban pathways. Results show that the subjects successfully accomplished the task and suggest that blind and visually impaired pedestrians might find the AT device and its concept approach useful, friendly, fast to master, and easy to use.

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Statistical Study of User Perception of Smart Homes during Vital Signal Monitoring with an Energy-Saving Algorithm

2022 , Del-Valle-Soto, Carolina , Juan Arturo Nolazco-Flores , Del-Puerto-Flores, J. Alberto , Velázquez, Ramiro , Valdivia, Leonardo , Rosas-caro, Julio , Paolo Visconti

Sensor networks are deployed in people’s homes to make life easier and more comfortable and secure. They might represent an interesting approach for elderly care as well. This work highlights the benefits of a sensor network implemented in the homes of a group of users between 55 and 75 years old, which encompasses a simple home energy optimization algorithm based on user behavior. We analyze variables related to vital signs to establish users’ comfort and tranquility thresholds. We statistically study the perception of security that users exhibit, differentiating between men and women, examining how it affects the person’s development at home, as well as the reactivity of the sensor algorithm, to optimize its performance. The proposed algorithm is analyzed under certain performance metrics, showing an improvement of 15% over a sensor network under the same conditions. We look at and quantify the usefulness of accurate alerts on each sensor and how it reflects in the users’ perceptions (for men and women separately). This study analyzes a simple, low-cost, and easy-to-implement home-based sensor network optimized with an adaptive energy optimization algorithm to improve the lives of older adults, which is capable of sending alerts of possible accidents or intruders with the highest efficiency.

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Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

2020 , Calabrese, Bernardo , Velázquez, Ramiro , Roberto de Fazio , Del-Valle-Soto, Carolina , Nicola Ivan Giannoccaro , Paolo Visconti

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

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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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An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation

2020 , Del-Valle-Soto, Carolina , Velázquez, Ramiro , Valdivia, Leonardo , Nicola Ivan Giannoccaro , Paolo Visconti

The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running.

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Comparison of Collaborative and Cooperative Schemes in Sensor Networks for Non-Invasive Monitoring of People at Home

2023 , Del-Valle-Soto, Carolina , Valdivia, Leonardo , López-Pimentel, Juan Carlos , Paolo Visconti

This paper looks at wireless sensor networks (WSNs) in healthcare, where they can monitor patients remotely. WSNs are considered one of the most promising technologies due to their flexibility and autonomy in communication. However, routing protocols in WSNs must be energy-efficient, with a minimal quality of service, so as not to compromise patient care. The main objective of this work is to compare two work schemes in the routing protocol algorithm in WSNs (cooperative and collaborative) in a home environment for monitoring the conditions of the elderly. The study aims to optimize the performance of the algorithm and the ease of use for people while analyzing the impact of the sensor network on the analysis of vital signs daily using medical equipment. We found relationships between vital sign metrics that have a more significant impact in the presence of a monitoring system. Finally, we conduct a performance analysis of both schemes proposed for the home tracking application and study their usability from the user’s point of view.

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Analysis and Correlation between a Non-Invasive Sensor Network System in the Room and the Improvement of Sleep Quality

2022 , Eduardo Morales-Vizcarra , Del-Valle-Soto, Carolina , Paolo Visconti , Cortes-Chavez, Fabiola

Good sleep quality is essential in human life due to its impact on health. Currently, technology has focused on providing specific features for quality sleep monitoring in people. This work represents a contribution to state of the art on non-invasive technologies that can help improve the quality of people’s sleep at a low cost. We reviewed the sleep quality of a group of people by analyzing their good and bad sleeping habits. We take that information to feed a proposed algorithm for a non-invasive sensor network in the person’s room for monitoring factors that help them fall asleep. We analyze vital signs and health conditions in order to be able to relate these parameters to the person’s way of sleeping. We help people get valuable information about their sleep with technology to live a healthy life, and we get about a 15% improvement in sleep quality. Finally, we compare the implementations given by the network with wearables to show the improvement in the behavior of the person’s sleep.

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Development of Sensors-Based Agri-Food Traceability System Remotely Managed by a Software Platform for Optimized Farm Management

2020 , Paolo Visconti , Roberto de Fazio , Velázquez, Ramiro , Del-Valle-Soto, Carolina , Nicola Ivan Giannoccaro

The huge spreading of Internet of things (IoT)-oriented modern technologies is revolutionizing all fields of human activities, leading several benefits and allowing to strongly optimize classic productive processes. The agriculture field is also affected by these technological advances, resulting in better water and fertilizers’ usage and so huge improvements of both quality and yield of the crops. In this manuscript, the development of an IoT-based smart traceability and farm management system is described, which calibrates the irrigations and fertigation operations as a function of crop typology, growth phase, soil and environment parameters and weather information; a suitable software architecture was developed to support the system decision-making process, also based on data collected on-field by a properly designed solar-powered wireless sensor network (WSN). The WSN nodes were realized by using the ESP8266 NodeMCU module exploiting its microcontroller functionalities and Wi-Fi connectivity. Thanks to a properly sized solar power supply system and an optimized scheduling scheme, a long node autonomy was guaranteed, as experimentally verified by its power consumption measures, thus reducing WSN maintenance. In addition, a literature analysis on the most used wireless technologies for agri-food products’ traceability is reported, together with the design and testing of a Bluetooth low energy (BLE) low-cost sensor tag to be applied into the containers of agri-food products, just collected from the fields or already processed, to monitor the main parameters indicative of any failure or spoiling over time along the supply chain. A mobile application was developed for monitoring the tracking information and storing conditions of the agri-food products. Test results in real-operative scenarios demonstrate the proper operation of the BLE smart tag prototype and tracking system.

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New Wearable Technologies and Devices to Efficiently Scavenge Energy from the Human Body: State of the Art and Future Trends

2022 , Roberto De Fazio , Roberta Proto , Del-Valle-Soto, Carolina , Velázquez, Ramiro , Paolo Visconti

Wearable technology represents a new technological paradigm for promoting physical activity, enabling monitoring of performances and athletic gestures. In addition, they can be employed for remote health monitoring applications, allowing continuous acquisition of users’ vital signs directly at home, emergency alerting, and computer-assisted rehabilitation. Commonly, these devices depend on batteries which are not the better option since researchers aim for dispositive who need minimal human intervention. Energy harvesting devices can be useful to extract energy from the human body, especially by integrating them into the garments, giving health monitoring devices enough energy for their independent operation. This review work focuses on the main new wearable technologies and devices to scavenge energy from the human body. First, the most suitable energy sources exploitable for wearable applications are investigated. Afterward, an overview of the main harvesting technologies (piezoelectric, triboelectric, thermoelectric, solar fabrics, and hybrid solution) is presented. In detail, we focused on flexible and thin textiles with energy harvesting capability, allowing easy integration into clothes fabric. Furthermore, comparative analyses of each harvesting technology are proposed, providing useful insights related to the best technologies for developing future self-sustainable wearable devices. Finally, a comparison between our review work and similar ones is introduced, highlighting its strengths in completeness and specificity.

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