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Integrating In-Ear Wearable Sensors with Deep Learning for Head and Facial Movement Analysis 5

Integrating In-Ear Wearable Sensors with Deep Learning for H...

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5th International Conference on Big Data Analytics and Practices (IBDAP)

作者： Mekruksavanich, Sakorn jantawong, ponnipa Jitpattanaku, Anuchit Univ Phayao Dept Comp Engn Sch Informat & Commun Technol Phayao Thailand King Mongkuts Univ Technol North Bangkok Dept Math Intelligent & Nonlinear Dynam Innovat Res Ctr Fac Appl Sci Bangkok Thailand

ISBN: (纸本)9798350391749

Accurate and inconspicuous identification of eating behavior has substantial implications for wellness surveillance, nutritional tracking, and technological assistance. Conventional approaches for detecting eating habits frequently rely on invasive or unfeasible techniques, which restrict their practicality in real-life situations. This research introduces an innovative eating recognition system that utilizes head and face motions recorded by in-ear wearing sensors and advanced deep learning algorithms. Our approach is a non-invasive and simple-to-use method that utilizes the unique patterns of head and face motions related to eating actions. We use a specially developed in-ear wearable sensor from the EarSet dataset to gather movement information while individuals are eating. The dataset comprises a range of cranial and facial motions recorded from 30 individuals. The unprocessed sensor data is subjected to pre-processing to eliminate interference and extract pertinent characteristics. We use advanced deep learning models, such as CNN, LSTM, BiL-STM, GRU, BiGRU, and our novel hybrid model CNN-LSTM, to acquire knowledge of distinctive patterns and identify eating occurrences. Our eating recognition system has been extensively tested and proven successful. The CNN-LSTM model achieved an accuracy of 95.12% and an F1-score of 95.09% during the 5-fold cross-validation studies. Our technique demonstrates superior efficiency and applicability compared to standard deep learning algorithms, as a comparative study shows. The suggested system can combine in-ear wearable sensors with deep learning to provide inconspicuous and accurate identification of eating activities. This study enhances the development of eating detection technology and introduces new opportunities for individualized wellness tracking and helpful applications. Subsequent research will prioritize the enlargement of the dataset, integration of supplementary modalities, enhancement of deep learning models for

关键词： eating detection in-ear wearable sensors deep learning head movements facial movements health monitoring

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Smartwatch-based Eating Detection and Cutlery Classification using a Deep Residual Network with Squeeze-and-Excitation Module 45

Smartwatch-based Eating Detection and Cutlery Classification...

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45th International Conference on Telecommunications and Signal Processing (TSP)

作者： Mekruksavanich, Sakorn jantawong, ponnipa Jitpattanakul, Anuchit Univ Phayao Dept Comp Engn Sch Informat & Commun Technol Phayao Thailand King Mongkuts Univ Technol North Bangkok Fac Sci Appl Intelligent & Nonlinear Dynam Innovat Res Ctr Dept Math Bangkok Thailand

ISBN: (数字)9781665469487

ISBN: (纸本)9781665469487

Several machine learning and deep learning algorithms have been developed to tackle the human behavior detection issue, emphasizing everyday tasks. Nevertheless, an intriguing and challenging human activity recognition (HAR) subject involves more complicated human behaviors, including eating-related actions. This study provided a smartwatch-based eating identification system based on hand-movement data. In the framework, we introduced a deep residual network named the ResNet-SE model that enhanced detection capability by using the benefits of shortcut connections and squeeze-and-excitation units. In addition, the effectiveness of standard deep learning models (CNN and LSTM) is evaluated and compared to that of the proposed model. In addition, we investigate the use of wristwatch sensor data for categorizing six kinds of food cutlery. To validate the correctness of the model, the accuracy, F1-score, and confusion matrices of the HAR metrics are applied to the EatingDetectionIJS dataset for evaluating the proposed framework. Experimentation findings demonstrate that the ResNetSE model surpasses existing deep residual models, achieving the greatest F1-score of 91.81% for eating identification and 91.43% for cutlery categorization.

关键词： eating detection cutlery classification smartwatch sensors deep residual network

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Deep Residual Networks for Human Activity Recognition based on Biosignals from Wearable Devices 45

Deep Residual Networks for Human Activity Recognition based ...

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45th International Conference on Telecommunications and Signal Processing (TSP)

作者： Mekruksavanich, Sakorn jantawong, ponnipa Jitpattanakul, Anuchit Univ Phayao Sch Informat & Commun Technol Dept Comp Engn Phayao Thailand King Mongkuts Univ Technol North Bangkok Dept Math Intelligent & Nonlinear Dynam Innovat Res Ctr Fac Sci Appl Bangkok Thailand

ISBN: (数字)9781665469487

ISBN: (纸本)9781665469487

Wearable inertial sensors are frequently employed in the area of human activity recognition (HAR), considering they provide the most meaningful time-series data available for non-visual datasets. Nevertheless, when just motion detectors are supplied, the recognition ability of individual complex activity detection is restricted. This study examines bio-signal data in order to improve the outcomes of complex HAR. Electrocardiogram (ECG) and photoplethysmogram (PPG) data are employed separately to identify complex HAR. We employed a benchmark bio-signal dataset (PPG-DaLiA) comprising ECG, PPG, and tri-axial accelerometer (3D-ACC) signals obtained from 15 people as they performed a series of uncomplicated and complex everyday activities. After incorporating early fusion conditions, we trained and evaluated deep learning models using 5-fold cross-validation. Our findings suggest that integrating biosignal characteristics with the 3D-ACC improves the classifier's effectiveness F1-scores by 2.17% and 2.27%, respectively, for ECG and PPG.

关键词： human activity recognition electrocardiogram photoplethysmogram inertial measurement unit bio-signal data

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Comparison of Different Deep Learning Networks to Classify Epilepsy Seizure Based on EEG Signals 21

Comparison of Different Deep Learning Networks to Classify E...

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21st International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Mekruksavanich, Sakorn jantawong, ponnipa Jitpattanakul, Anuchit Univ Phayao Sch Informat & Commun Technol Dept Comp Engn Phayao Thailand King Mongkuts Univ Technol North Bangkok Fac Appl Sci Intelligent & Nonlinear Dynam Innovat Res Ctr Dept Math Bangkok Thailand

ISBN: (纸本)9798350381566;9798350381559

Epilepsy, a widespread neurological condition impacting approximately 50 million people worldwide, is often identified and studied through electroencephalography (EEG) due to its rich temporal information. Convolutional neural networks and recurrent neural networks have demonstrated potential in automatically detecting seizures from EEG data. Despite this, there's a shortage of comprehensive studies comparing the detection capabilities of different designs. This research assesses and contrasts the effectiveness of five renowned deep neural network models - CNN, LSTM, BiLSTM, GRU, and BiGRU - in binary categorization of EEG segments into seizure and non-seizure groups. Training and evaluation utilize the publicly accessible Bonn dataset, specifically crafted for epileptic seizure detection. Performance metrics like accuracy, precision, recall, and F1-score are employed in the analysis. The principal findings highlight BiGRU's impressive accuracy at 95.80% and F1-score at 92.96% in individualized seizure identification. Additionally, the study introduces a groundbreaking deep learning architecture, the self-normalizing neural network (SNN), to enhance seizure detection accuracy. The recommended SNN exhibits outstanding performance, boasting an accuracy of 98.50% and an impressive F1-score of 98.50%.

关键词： epileptic seizure detection EEG classification deep learning network convolutional neural network recurrent neural network

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Enhancing Clinical Activity Recognition with Bidirectional RNNs and Accelerometer-ECG Fusion 21

Enhancing Clinical Activity Recognition with Bidirectional R...

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21st International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Mekruksavanich, Sakorn jantawong, ponnipa Jitpattanakul, Anuchit Univ Phayao Sch Informat & Commun Technol Dept Comp Engn Phayao Thailand King Mongkuts Univ Technol North Bangkok Fac Appl Sci Intelligent & Nonlinear Dynam Innovat Res Ctr Dept Math Bangkok Thailand

ISBN: (纸本)9798350381566;9798350381559

The ongoing monitoring of human activities has several applications in digital health. However, precisely identifying activities in medical settings is difficult because of the intricate nature of actions and patient differences. This study presents a sophisticated approach using deep learning to combine accelerometer and ECG data from wearable devices to identify clinical activities. The suggested approach involves using a dual-stream convolutional-recurrent neural network to capture spatial and temporal patterns accurately. An assessment was carried out using a dataset that is accessible to the public, including records of activities undertaken by both healthy individuals and patients diagnosed with pneumoconiosis. An analysis was conducted on five deep learning architectures, which included convolutional, recurrent, and bidirectional recurrent networks. The results demonstrated that bidirectional gated recurrent units exhibited the most fantastic accuracy, ranging from 97% to 99%, for both the healthy and sick categories. This performance is significantly above that of the long short-term memory and convolutional neural network models. The results indicate that combining convolutional feature extraction and bidirectional sequential modeling allows for the effective learning of valuable features from multi-modal clinical time-series data. The results enhance the monitoring of human activities in clinical settings by combining specific deep neural networks designed for mobile health applications.

关键词： clinical monitoring human activity recognition wearable sensor deep learning network ECG signals

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Effect of Sliding Window Sizes on Sensor-Based Human Activity Recognition Using Smartwatch Sensors and Deep Learning Approaches 5

Effect of Sliding Window Sizes on Sensor-Based Human Activit...

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5th International Conference on Big Data Analytics and Practices (IBDAP)

作者： Mekruksavanich, Sakorn jantawong, ponnipa Jitpattanaku, Anuchit Univ Phayao Sch Informat & Commun Technol Dept Comp Engn Phayao Thailand King Mongkuts Univ Technol North Bangkok Fac Appl Sci Intelligent & Nonlinear Dynam Innovat Res Ctr Dept Math Bangkok Thailand

ISBN: (纸本)9798350391749

Smartwatch sensors for human activity recognition (HAR) have gained significant attention due to their applications in healthcare and fitness monitoring. The effectiveness of HAR systems largely depends on the choice of sliding window widths for sensor data segmentation. This study investigates the impact of varying sliding window widths on the accuracy of HAR using wristwatch sensors and deep learning techniques. We conducted experiments using the daily human activity (DHA) dataset, comprising sensor data from 11 distinct activities. Data was preprocessed and segmented using window sizes ranging from 5 to 40 seconds. Four deep learning models (CNN, LSTM, BiLSTM, and CNN-LSTM) were employed and evaluated using accuracy, precision, recall, and F1-score. Window size significantly affected HAR performance. Smaller windows improved short-duration activity recognition but increased computational complexity, while larger windows reduced computational load but decreased accuracy for rapid activity changes. The CNN-LSTM hybrid model consistently outperformed other models, achieving 92.11% accuracy with a 20-second window and overlapping segmentation. This research provides valuable insights into balancing recognition accuracy and computational resources in smartwatch sensor-based HAR, contributing to the development of efficient and accurate systems for real-world applications.

关键词： human activity recognition smartwatch sensors sliding window deep learning sensor data segmentation

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Ensemble Deep Learning Network for Enhancing Performances of Sensor-based Physical Activity Recognition Based on IMU Sensor Data 5

Ensemble Deep Learning Network for Enhancing Performances of...

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5th International Conference on Big Data Analytics and Practices (IBDAP)

ISBN: (纸本)9798350391749

The application of wearable sensors to identify physical activities has drawn significant attention in the healthcare and fitness industries. This study presents a novel ensemble deep learning network to enhance the accuracy of physical movement recognition utilizing data from inertial measurement unit (IMU) sensors. The proposed Ens-CNN-LSTM method merges a convolutional neural network (CNN) with a long short-term memory (LSTM) network. This integration capitalizes on their strengths in analyzing sequential IMU data. The model further incorporates a random forest classifier to finalize predictions. Our proposed model was evaluated using the PAMAP2 benchmark dataset for human activity recognition. This dataset includes data from multiple individuals performing various activities recorded with IMU sensors. We assessed the ensemble model's performance against individual deep learning models through a 5-fold cross-validation method. Results reveal a significant enhancement in overall accuracy, achieving 99.63%, compared to 96.19% for CNN models and 97.35% for LSTM models. The Ens-CNN-LSTM model demonstrated remarkable performance with an accuracy of 99.61%, a recall of 99.63%, and an F1-score of 99.61%. It surpassed the individual models in all metrics. Furthermore, the ensemble model showed increased robustness to variations among individuals and noise in sensor data. The proposed approach improves the accuracy of physical activity recognition and extends its applicability to other domains and activities.

关键词： human activity recognition deep learning ensemble learning CNN LSTM wearable sensors

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Enhancement of Human Complex Activity Recognition using Wearable Sensors Data with InceptionTime Network 2

Enhancement of Human Complex Activity Recognition using Wear...

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2nd International Conference on Big Data Analytics and Practices, IBDAP 2021

作者： jantawong, ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665428415

Effective human activity recognition can be incredibly beneficial in big data applications like ambient healthcare-supported living. Deep learning (DL) techniques have considerably advanced research in human activity recognition (HAR). These deep learning algorithms outperform conventional machine learning methods in terms of automatic feature extraction. Many deep learning models have recently been demonstrated as state-of-the-art approaches to efficiently classify simple and complex human behaviors to address the HAR. This work proposes a new sensor-based HAR to classify complex activities with high performance using a DL model called InceptTime network. The proposed architecture is evaluated on a public benchmark complex activity dataset named PAMAP2. The experimental outcomes show that the proposed InceptTime model is significantly better than other baseline D L models on the same dataset with the highest accuracy of 88 %. © 2021 IEEE.

关键词： Wearable sensors

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Recognizing and Understanding Sport Activities based on Wearable Sensor Signals using Deep Residual Network 4

Recognizing and Understanding Sport Activities based on Wear...

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4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

作者： Mekruksavanich, Sakorn jantawong, ponnipa Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao 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: (纸本)9798350330618

The prevalence of intelligent wearable devices has significantly increased, offering advantages to individuals of all age groups. In the field of human activity recognition (HAR) research, wearable sensor data plays a crucial role in classifying various human actions. Sensor-based assessments are commonly employed in hazard analysis and risk assessments. However, traditional machine learning (ML) algorithms have faced challenges when applied to sports activities due to their unpredictable nature. These challenges have prompted the need for manual feature extraction, limiting the efficacy of ML methodologies in data categorization. To address this, our study introduces ResNet, a deep residual network, for effectively categorizing sports activities using wearable sensor data. To evaluate our proposed approach, we utilized a publicly available benchmark dataset consisting of sensor data collected from the dominant wrist, neck, and thigh of each study participant. The experimental results demonstrated the exceptional performance of the ResNet model, achieving an impressive accuracy rate of 99.83% in recognizing sport activities. Additionally, we compared the efficacy of our suggested model with five fundamental deep learning models, namely CNN, LSTM, BiLSTM, GRU, and BiGRU. The comparative analysis revealed that the ResNet model outperformed the other deep learning models, demonstrating superior performance in sport activity recognition. ©2023 IEEE.

关键词： Wearable sensors

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ResNet-based Deep Neural Network using Transfer Learning for Animal Activity Recognition 6

ResNet-based Deep Neural Network using Transfer Learning for...

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6th International Conference on Information Technology, InCIT 2022

作者： Mekruksavanich, Sakorn jantawong, ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665489126

Animal activity recognition (AAR) is a challenging and complex task for monitoring animal behavior to determine their health conditions and physical responses. To address this requirement, AAR research aims to develop an internet of things (IoT) platform that anticipates animal behavior based primarily on sensor data collected from wearable devices connected to animals. Because of its superior effectiveness on multi-dimensional sensor data, deep learning (DL) currently dominates sensor-based AAR applications. Nevertheless, DL techniques require significantly more training time than other learning-based strategies. This study employs transfer learning (TL) to generalize the DL model to overcome the issue of training time, indicating significant promise for future research on animal activity identification. Furthermore, we apply the TL to allow the reusability of a pre-trained DL model on entirely new data without the time-consuming operations of model training and data labeling. We conducted experiments on a publicly available AAR benchmark dataset (SAR) utilizing deep residual networks with automatically learned features. Utilizing source data, our technique achieved an overall accuracy of 96.46% (dataset collected by the first sensor) and 98.40% (dataset collected by the second sensor) based on evaluating findings. © 2022 IEEE.

关键词： Wearable sensors

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