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IEEE International Conference on software engineering and Service sciences (ICSESS)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

Falls pose a significant health risk, especially for older adults, resulting in serious injuries, limited movement, and higher medical expenses. Precise and fast identification of falls is essential for immediate healthcare treatment and avoidance of additional complications. This paper presents a new method to improve the identification of individual falls by using movement inputs from smart wearable sensors and a hybrid deep learning model that combines convolutional neural networks (CNN) and bidirectional gated recurrent units (BiGRU). Intelligent wearable sensors, including accelerometers and gyroscopes, are used to collect real-time movement data from different parts of the human body. The movement signals gathered are subjected to preprocessing and segmentation into windows of a particular length to extract pertinent information. An advanced neural network structure comprising CNN layers for extracting spatial features and BiGRU layers for representing temporal sequences is specifically intended to acquire the distinctive patterns related to fall occurrences efficiently. The proposed CNN-BiGRU model is trained on a varied dataset that includes simulated falls and activities of daily living conducted by several people. The model's effectiveness is assessed using established measures, such as accuracy, sensitivity, specificity, and F1-score. The experimental findings demonstrate that the CNN-BiGRU model has a notable level of accuracy in recognizing instances of falls. Combining in-telligent wearable sensors and advanced deep-learning algorithms creates a dependable and highly effective system for detecting falls. The suggested strategy can significantly enhance the well-being of those prone to falls by offering prompt aid and mitigating the impact of injuries caused by falls.

关键词： Accuracy Sensitivity Neural networks Feature extraction Real-time systems Convolutional neural networks Older adults Wearable sensors Injuries software engineering

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A Comprehensive Review of Active EV Battery Cell Voltage Balancing Systems: Current Issues and Prospective Solutions

A Comprehensive Review of Active EV Battery Cell Voltage Bal...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Chandan Chetri Alvin Huynh Sheldon S. Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered.

关键词： Industrial electronics Electric potential Costs Equalizers Battery management systems Transformers Power electronics

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals

Deep Learning Networks for Human Knee Abnormality Detection ...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. The proposed ResNeXt model, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). Res NeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEM G and goniometer signals, aiding early disease detection and treatment.

关键词： Deep learning Accuracy Goniometers Signal processing Muscles Electromyography Telecommunications Surface treatment Long short term memory Diseases

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Leveraging Residual Deep Neural Networks and Multi-Device Sensors for Heterogeneous Activity Recognition

Leveraging Residual Deep Neural Networks and Multi-Device Se...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

This study introduces a novel approach to identifying human activities using wearable sensors, particularly smart-phones and smartwatches. By leveraging deep learning neural networks and data from the HHAR dataset, which includes accelerometer and gyroscope data from individuals engaged in various activities, our method, centered around the HAR-Res NeXt model, accurately detects six activities. Utilizing residual connections and multi-kernel blocks, our approach effectively captures temporal and spatial relationships in sensor data. Experimental results demonstrate superior performance to standard machine learning algorithms and other deep learning approaches for human activity recognition. HAR-ResNeXt achieves high accuracy rates, particularly in classifying smartphone sensor data, underscoring its adaptability across diverse scenarios. Comparative analysis reveals the effectiveness of smartphone sensors and emphasizes the importance of multi-modal sensor fusion for accurate activity detection.

关键词： Deep learning Accuracy Machine learning algorithms Multimodal sensors Signal processing Telecommunications Gyroscopes

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Enhancing Sensor-based Human Activity Recognition Using Hybrid Deep Learning and Data Augmentation

Enhancing Sensor-based Human Activity Recognition Using Hybr...

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Research, Invention, and Innovation Congress (RI2C)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Research Group in Statistical Learning and Inference Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331506018

ISBN: (纸本)9798331506025

Human Activity Recognition (HAR) is essential in various applications, including wellness tracking, automated residences, and fitness monitoring. In the past few decades, sensor-based HAR has become increasingly popular due to advancements in technologies for sensors. Nevertheless, HAR networks' effectiveness dramatically depends on the caliber and volume of the training data, which is frequently restricted and unevenly distributed. This research introduces a novel deep learning method called Multihead-CNN-BiGRU, which integrates one-dimensional convolutional neural networks with bidirectional gated recurrent units (BiGRU) to improve the accuracy of sensor-based HAR. To tackle the problem of insufficient a nd uneven training data, we utilize the synthetic minority over-sampling technique (SMOTE) to augment the data. The suggested model is assessed using the publicly accessible WISDM dataset, which comprises sensor data from diverse human actions. The ID-CNN is employed for extracting localized characteristics from the sensor data, while the BiGRU gathers temporal dependencies and contextual information. The hybrid architecture allows the model to acquire spatial and temporal patterns efficiently. In addition, the SMOTE technique is utilized to create artificial samples of the underrepresented classes, thus equalizing the distribution of classes and enhancing the model's capacity to generalize. The experimental findings show that our hybrid strategy provides exceptional outcomes when paired with SMOTE data augmentation. It obtains the most excellent accuracy of 99.51 % and the highest F1-score of 99.49% compared to the most advanced approaches. The suggested framework provides a reliable and precise solution for sensor-based HAR, which opens up opportunities for improved applications in other fields.

关键词： Deep learning Technological innovation Accuracy Training data Sensor phenomena and characterization Logic gates Data augmentation Human activity recognition Reliability Monitoring

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Hybrid Attention with CNN-BiLSTM and CBAM for Efficient Wearable Activity Recognition

Hybrid Attention with CNN-BiLSTM and CBAM for Efficient Wear...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

The use of widespread sensors for automatic activity recognition has become a dynamic area of research due to its broad applications in healthcare, fitness monitoring, and assisted living. To efficiently identify daily and sports activities from sensor data, this study suggests a hybrid deep learning model that integrates convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and convolutional block attention modules (CBAM). The model starts by extracting spatial features from the sensor data using CNN layers. These spatial features are then employed to offer temporal context information to the BiLSTM network. To conclude, the CBAM attention mechanism is applied to guide the model’s focus to the most informative segments of the BiLSTM feature maps. This approach enables the accurate recognition of patterns in both complex and daily sports activities. The effectiveness of the proposed hybrid model is evaluated using the UCI-DSA, a publicly available benchmark dataset that includes sports and daily activities collected through wearable motion sensors. In contrast to existing approaches, the outcomes reveal that a unified framework involving CNN, BiLSTM, and CBAM achieves outstanding performance in activity recognition, exceeding $99 \%$ in both accuracy and F1-score while maintaining computational efficiency.

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A Sensor-Based Deep Learning Approach for Recognizing Daily and Work Activities in Open Environments for Sanitation Workers

A Sensor-Based Deep Learning Approach for Recognizing Daily ...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

Sanitation workers play a crucial role in maintaining public hygiene and cleanliness. Understanding their daily routines and job responsibilities is essential for improving their work environment, task distribution, and overall supervision. However, tracking sanitation workers in open environments remains a persistent challenge. This paper introduces a deep learning model named CNN-BiGRU-CBAM, designed to recognize sanitation workers’ daily and working activities using smartwatch sensors. The model leverages advanced convolutional neural networks to extract highly informative features from multi-modal sensor data, including acceleration information, collected from smartwatches. The dataset used in this study is carefully curated and collected from sanitation workers during their daily work routines, encompassing activities such as walking, running, sweeping, lifting, and driving vehicles. These activities are systematically categorized to cover both work-related tasks and non-work activities. Experimental results demonstrate that the proposed model consistently achieves an average F1-score of over 94.50% across seven activity groups, outperforming baseline deep learning methods by an impressive margin of 5.62%. The model excels in accurately identifying operations in diverse and dynamic work settings. This research highlights the potential of smartwatches and deep learning in continuously analyzing sanitation workers’ duties and developing improved working regulations that consider their well-being and daily tasks.

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Detecting Face-Touching Gestures with Smartwatches and Deep Learning Networks

Detecting Face-Touching Gestures with Smartwatches and Deep ...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Research Group in Statistical Learning and Inference Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Everyday actions like scratching one's nose or resting the chin on one's hand may facilitate the spread of germs and diseases. Detecting these gestures holds the potential for innovative health monitoring and disease prevention applications. However, the identification of face-touching poses challenges due to the variety of human gestures and the limitations in accuracy associated with wearable sensors. This study introduces an original deep-learning system to identify face-touching gestures using standard smartwatches' inertial measurement unit sensors. The system on the smartwatch captures and pre-processes multi-channel time-series data from the accelerometer to generate robust input features. We utilize a benchmark, the Face Touching dataset, to evaluate the recognition performance of deep learning networks, including our proposed network. Our approach proposes a hybrid deep residual network architecture tailored for sequence-based gesture classification using signals from the smartwatch sensors. In our experiments, the developed deep learning framework achieves an impressive F1-score of 97.53% in detecting face-touch gestures. The suggested system takes a step forward in advancing the practical applications of face-touch gesture detection to smartwatch-based health sensing and disease prevention.

关键词： Deep learning Measurement units Prevention and mitigation Face recognition Signal processing Sensor systems Telecommunications

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Deep Learning Based Binary Classification for Alzheimer's Disease Detection using Brain MRI Images 15

Deep Learning Based Binary Classification for Alzheimer's Di...

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15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

作者： Hussain, Emtiaz Hasan, Mahmudul Hassan, Syed Zafrul Hassan Azmi, Tanzina Rahman, Md Anisur Zavid Parvez, Mohammad Brac University Software Engineering and Hci Research Group Department of Computer Science and Engineering Bangladesh Bangladesh

ISBN: (纸本)9781728151694

Alzheimer's disease is an irremediable, continuous brain disorder that gradually destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. It has become one of the critical diseases throughout the world. Moreover, there is no remedy for Alzheimer's disease. Machine learning techniques, especially deep learning-based Convolutional Neural Network (CNN), are used to improve the process for the detection of Alzheimer's disease. In recent days, CNN has achieved major success in MRI image analysis and biomedical research. A lot of research has been carried out for the detection of Alzheimer's disease based on brain MRI images using CNN. However, one of the fundamental limitations is that proper comparison between a proposed CNN model and pre-trained CNN models (InceptionV3, Xception, MobilenetV2, VGG) was not established. Therefore, in this paper, we present a model based on 12-layer CNN for binary classification and detection of Alzheimer's disease using brain MRI data. The performance of the proposed model is compared with some existing CNN models in terms of accuracy, precision, recall, F1 score, and ROC curve on the Open Access Series of Imaging Studies (OASIS) dataset. The main contribution of the paper is a 12-layer CNN model with an accuracy of 97.75%, which is higher than any other existing CNN models published on this dataset. The paper also shows side by side comparison between our proposed model and pre-trained CNN models (InceptioV3, Xception, MobilenetV2, VGG). The experimental results show the superiority of the proposed model over the existing models. © 2020 IEEE.

关键词： Magnetic resonance imaging

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Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering

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Synchrotron Radiation News 2022年第4期35卷 16-20页

作者： Drucker, Nathan C. Liu, Tongtong Chen, Zhantao Okabe, Ryotaro Chotrattanapituk, Abhijatmedhi Nguyen, Thanh Wang, Yao Li, Mingda Quantum Measurement Group MIT Cambridge MA United States School of Engineering and Applied Sciences Harvard University Cambridge MA United States Department of Physics MIT Cambridge MA United States Department of Mechanical Engineering MIT Cambridge MA United States Department of Chemistry MIT Cambridge MA United States Department of Electrical Engineering and Computer Science MIT Cambridge MA United States Department of Nuclear Science and Engineering MIT Cambridge MA United States Department of Physics and Astronomy Clemson University Clemson SC United States

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