Espinosa Loera, Ricardo Abel
Main Affiliation
Preferred name
Espinosa Loera, Ricardo Abel
Official Name
Espinosa Loera, Ricardo Abel
ORCID
0000-0003-2573-7853
Scopus Author ID
57211517018
20 results
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Item type:Publication, Deploying Real-Time Speech Recognition on ESP32 Using TinyML and Edge Impulse(Springer Nature Switzerland, 2025); ; The emergence of Tiny Machine Learning (TinyML) has enabled real-time on-device inference on ultra-low-power microcontrollers, eliminating reliance on cloud computing while significantly reducing latency, power consumption, and bandwidth requirements. This study explores the deployment of a TinyML-based speech recognition system on an ESP32 microcontroller, leveraging Edge Impulse for model development, Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, and TensorFlow Lite for Microcontrollers (TFLM) for efficient inference. The model was trained on a curated subset of the Google Speech Commands Dataset, incorporating background noise augmentation to enhance robustness in real-world environments. Using Edge Impulse’s EON Compiler, the model was fully quantized and optimized, achieving a 37% reduction in RAM usage and 27% in ROM. The final model attained 87.14% accuracy on testing data and 97.1% average classification confidence during real-time inference, with excellent noise rejection (99.6%) and latency of 266 ms. Compared to state-of-the-art systems deployed on more powerful platforms, the proposed approach achieves competitive accuracy while maintaining real-time inference and minimal resource consumption on ultra-low-power hardware. This makes it particularly suitable for battery-powered IoT, robotics, and embedded automation applications where connectivity and energy efficiency are critical. By balancing performance and efficiency, this research highlights the viability of deploying speech recognition systems on constrained microcontrollers. Future work will explore advanced architectures and enhanced feature extraction strategies to further improve recognition accuracy, especially for short or phonetically similar commands. ©The authors ©Springer. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Color-aware Exposure Correction for Endoscopic Imaging using a Lightweight Vision Transformer(2024); 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras(2020); ; Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.Scopus© Citations 21 51 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning(2021); Scopus© Citations 15 6 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Renewable Energy Prediction through Machine Learning Algorithms(2020)Scopus© Citations 10 9 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligent Management System for Micro-Grids using Internet-of-Things(2021); The current research work proposes the design of an intelligent platform for managing the generation, distribution, transmission, commercialization and consumption of electricity, allowing the decision-making process aimed at reducing costs and maximizing the use of energy resources. The technological proposal is a system of information made of integrated sensors. This creates an electrical real-time network that share energy in the cloud through LPWAN networks. Later, the data from the sensors are received in an intelligent platform (Max4 IoT) that employs super-computing systems and artificial intelligence for the analysis of individual and aggregated data. The system is able to learn about the electrical network, knowing the consumers’ behavior and energy trends, allowing to generate responses based on specific situations by sending SMS alerts and emails about abnormal situations or programmed tasks. In addition, the system allows the generation of reports on the network status in real-time and commands control signals on the switching on-and-off of equipment in response to consumption peaks and/or power supply outages. The platform allows interaction with distributed generation systems and micro-grids, through its mobile or web interface, increasing user interaction with the electrical system and the inclusion of renewable energy sources, thus influencing a better quality of service provided to the end user. © 2021 IEEE.14 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Un enfoque basado en la visión para la detección de caídas utilizando múltiples cámaras y redes neuronales convolucionales: un caso de estudio en UP-Fall Detection Data-Set(2019-12); 22 378 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, GYMetricPose: A light-weight angle-based graph adaptation for action quality assessment(2024); Gilberto Ochoa-Ruiz39 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection datasetThe automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-based approach for fall detection and classification systems due to the recent exponential increase in the use of cameras. Moreover, deep learning techniques have revolutionized vision-based approaches. These techniques are considered robust and reliable solutions for detection and classification problems, mostly using convolutional neural networks (CNNs). Recently, our research group released a public multimodal dataset for fall detection called the UP-Fall Detection dataset, and studies on modality approaches for fall detection and classification are required. Focusing only on a vision-based approach, in this paper, we present a fall detection system based on a 2D CNN inference method and multiple cameras. This approach analyzes images in fixed time windows and extracts features using an optical flow method that obtains information on the relative motion between two consecutive images. We tested this approach on our public dataset, and the results showed that our proposed multi-vision-based approach detects human falls and achieves an accuracy of 95.64% compared to state-of-the-art methods with a simple CNN network architecture. © 2019 Elsevier LtdScopus© Citations 96 29 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging(2023); Scopus© Citations 3 35 2