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ResNet-based Network for Recognizing Daily and Transitional Activities based on Smartphone Sensors 3

ResNet-based Network for Recognizing Daily and Transitional ...

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science North Bangkok Bangkok Thailand

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

In contemporary wearable computing contexts, sensor-based human activity recognition (HAR) has become a popular research topic. Investigators from the Health Applications Research Institute presented promising discoveries to promote healthcare applications, including fall detection, athletic tracking and reporting, and a monitoring scheme for senior activities in intelligent homes. In these services, ordinary and transitory human actions are captured by smartphones' wearable sensors and analyzed as fundamental and complicated motions. Deep learning techniques demonstrated the usefulness and effectiveness of convolutional neural networks (CNNs) in extracting high-level features embedded in sensor data to develop reliable recognition models. CNN faces deterioration of gradient vanishing issues when networks require deeper convolution layers. To overcome the problem, we developed ResNet, a deep residual network for determining daily and transitory activities. Using a significant standard HAR dataset called the KU-HAR dataset that gathered smartphone sensor data of various human actions, we performed experiments to identify the most appropriate ResNet-based models. Experimental findings indicate that the ResNet-18 has the highest accuracy, at 93.54%. The acquired results surpass prior state-of-the-art models by 3.87% in terms of accuracy. © 2022 IEEE.

关键词： Smartphones

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Badminton Activity Recognition and Player Assessment based on Motion Signals using Deep Residual Network 13

Badminton Activity Recognition and Player Assessment based o...

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13th IEEE International Conference on Software engineering and Service science, ICSESS 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

With the fast expansion of digital technologies and sporting events, interpreting sports data has become an immensely complicated endeavor. Internet-sourced sports big data exhibit a significant development trend. Big data in sports offer a wealth of information on sportspeople, coaching, athletics, swimming, and badminton. Today, various sports data are freely accessible, and incredible data analysis tools based on wearable sensors have been established, allowing us to investigate the usefulness of these data thoroughly. In this research, we investigate the detection of badminton action and player evaluation based on movement data captured by wearable sensors. Movement data captured by an accelerometer, gyroscope, and magnetometer are utilized for training and validating a classification model for badminton actions. In addition, the movement signals are used to train a player evaluation model employing a deep residual network. To assess our suggested technique, we utilized a publicly available benchmark dataset consisting of inertial measurement unit (IMU) sensors attached to every investigator's dominant wrist, palm, and both legs. The experimental findings indicate that the proposed deep residual network obtained good performance with a maximum accuracy of 98.00% for identifying badminton activities and 98.56% for evaluating badminton players. © 2022 IEEE.

关键词： Wearable sensors

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Recognition of Shoulder Exercise Activity Based on EfficientNet Using Smartwatch Inertial Sensors 3

Recognition of Shoulder Exercise Activity Based on Efficient...

引用

3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

Recognition of human activity is an important research topic due to its potential applications in areas, such as the medical industry and other related fields. Sensor-based human activity recognition (HAR) employing deep learning (DL) techniques has grown in popularity as a result of its immense efficiency in detecting complicated tasks and its low cost in comparison to more traditional machine learning (ML) techniques. More recently, convolutional neural network (CNN) approaches have also been used with sensor-based HAR, and the results have shown promise for improvement. In this study, we applied the CNN-based architecture known as EfficientNet to perform sensor-based HAR. The goal of this research was to apply the EfficientNet architecture to the classification of complicated human activities, such as shoulder workout activities. To evaluate the recognition performance, we used a benchmark HAR dataset called SPARS9x, which collected sensor data from six sports activities. According to the results of the experiments, the EfficientNet-B3 had the best performance overall on the benchmark dataset receiving an F1-score of 98.87%. © 2022 IEEE.

关键词： Network architecture

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Multi-resolution CNN for Lower Limb Movement Recognition Based on Wearable Sensors 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Mekruksavanich, Sakorn Jitpattanakul, Anuchit Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakorn Pathom Thailand 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: (数字)9783031209925

ISBN: (纸本)9783031209918

Human activity recognition (HAR) remains a difficult challenge in human-computer interaction (HCI). The Internet of Healthcare Things (IoHT) and other technologies are expected to be used primarily in conjunction with HAR to support healthcare and elder care. In HAR research, lower limb movement recognition is a challenging research topic that can be applied to the daily care of the elderly, fragile, and disabled. Due to recent advances in deep learning, high-level autonomous feature extraction has become feasible, which is used to increase HAR efficiency. Deep learning approaches have also been used for sensor-based HAR in various domains. This study presents a novel method that uses convolutional neural networks (CNNs) with different kernel dimensions, referred to as multi-resolution CNNs, to detect high-level features at various resolutions. A publicly available benchmark dataset called HARTH was used to evaluate the recognition performance to collect acceleration data of the lower limb movements of 22 participants. The experimental results show that the proposed approach improves the F1 score and achieves a higher score of 94.76%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wearable sensors

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The Effect of Sensor Placement for Accurate Fall Detection based on Deep Learning Model

The Effect of Sensor Placement for Accurate Fall Detection b...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

The development of inertial sensor technology and the growing utilization of wearable electronics (such as smartwatches, smart bands, and other intelligent gadgets) have facilitated the advancement of studies into automated Fall Detection Systems (FDSs). In the last decade, there has been significant scientific interest in maintaining FDSs. Focused on assessing the data acquired by wearable inertial sensors, machine learning (ML) techniques have demonstrated high efficacy in distinguishing falls from typical motions or activities of daily living (ADLs). In most research, unfortunately, the effectiveness of machine learning classifiers was constrained by feature extraction and selection processes that relied on human-made decisions. Recently, deep learning (DL) model findings s how their effectiveness for FDS. One of these effective DL models is the ResNeXt model, a deep neural network that operates based on convolutional layers with aggregated residual transformation. This study investigates the influence of sensor placement on various body locations for the fall detection issue. The ResNeXt model was assessed and compared to other baseline deep learning algorithms using the public UMAFall dataset for fall detection. Employing sensor data on waist location, the suggested model attained the most significant classification ac curacy of 97.275% when classifying falls. © 2022 IEEE.

关键词： Wearable sensors

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Hyperparameter Tuning in Convolutional Neural Network for Face Touching Activity Recognition using Accelerometer Data

Hyperparameter Tuning in Convolutional Neural Network for Fa...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

People have been encouraged to wear masks and avoid touching their faces in public as part of the new measures to prevent the spread of coronavirus 2019 (COVID-19). During the COVID-19 epidemic, few research have examined the effect of everyday living on the frequency of facial touch activity. To develop a face touching avoidance system, deep learning algorithms have been proposed and have demonstrated their amazing performance. However, an important drawback of deep learning is its extensive dependence on hyperparameters. The results of deep learning algorithms may vary depending on hyperparameters, such as the size of the filters, the number of filters, the batch size, the number of epochs, and the training optimization technique used. In this paper, we present an effective approach for hyperparameter tuning of convolutional neural networks (CNNs) for efficiently recognized face touching activities based on accelerometer data. Two hyperparameter tuning methods (Grid search and Bayesian optimization) were evaluated in order to construct the CNN with high performance. The experiment results show that Bayesian optimization can provide suitable hyperparameters for CNNs for face touching recognition with the highest accuracy of 96.61%. © 2022 IEEE.

关键词： Convolutional neural networks

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Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network

Classification of Physical Exercise Activity from ECG, PPG a...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Human activity recognition (HAR) plays an increasingly vital role in several industrial applications, including medical services and rehabilitation surveillance. With the fast growth of information and communications technology, wearable technologies have recently triggered a new human-computer interaction. Wearable inertial sensors (IMUs) are commonly used in the area of HAR because this data source provides the most insightful motion signal data. Lately, HAR studies have examined the enhancement of activity recognition using bio-signals like Electrocardiogram (ECG) and Photoplethysmography (PPG). Nevertheless, current HAR research was constrained by machine learning techniques that relied on human-crafted feature extraction. This research proposed a deep learning technique to effectively identify physical activity behaviors using ECG, PPG, and IMU sensor data. ResNet-SE is a deep residual network that incorporates convolutional processes, shortcut connections, and squeeze-and-excitement. We trained and evaluated baseline deep learning models to assess the suggested network, including the proposed model, using the public HAR dataset called Wrist_PPG dataset. According to experimental findings, the suggested method earned the most fantastic accuracy of F1-score. In addition, our results indicate that the PPG data can be utilized to classify physical workouts. © 2022 IEEE.

关键词： Electrocardiograms

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A Deep Residual Network for Recognizing Transportation Vehicles using Smartphone Sensors 13

A Deep Residual Network for Recognizing Transportation Vehic...

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13th IEEE International Conference on Software engineering and Service science, ICSESS 2022

作者： Hnoohom, Narit Maitrichit, Nagorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand University of Phayao Department of Computer Engineering School of Information and Communication Technology Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The development of sensor technology has enabled the development of a variety of applications for human activity detection by wearable devices. Identifying transportation modes for contextual support in the execution of systems such as driver assistance and intelligent transportation planning is one of the advantageous applications of an intelligent transportation system (ITS). Due to the widespread use of smartphones in today's world, a mobile application-based solution is proposed that can significantly reduce the cost of implementing ITS. In this work, we recognized transportation vehicles using the accelerometer and gyroscope data collected by smartphones. To achieve the research goal, this work developed a deep residual network called DeepResNeXt that used convolutional kernels and residual connections for transportation vehicle recognition. We used a public benchmark dataset to evaluate the proposed deep residual network. Experimental results showed that DeepResNeXt was achieved better accuracy and F1-score than previous works. In addition, this work also investigated the effect of sensor types on recognition performance. The results showed that the deep residual network trained with accelerometers achieved higher accuracy and F1-score than the network trained with gyroscope data. © 2022 IEEE.

关键词： Accelerometers

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Heterogeneous Recognition of Human Activity with CNN and RNN-based Networks using Smartphone and Smartwatch Sensors 3

Heterogeneous Recognition of Human Activity with CNN and RNN...

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

With ongoing advancements in remote sensor technologies, the concept of wearable sensors has gained widespread acceptance and grown ubiquitous due to its broad applicability in situations including ambient assisted living, innovative healthcare, and home appliances. In this context, the demand for Heterogeneous Human Activity Recognition (HHAR) has expanded considerably, as individuals desire their daily activities to be collected and analyzed to provide valuable information. This wearable sensing data is taken in the form of time-series data utilizing different wearable sensors, such as accelerometers, gyroscopes, and magnetometers, to offer other movement characteristics for each individual tracked from their smartphones and smartwatches. This data may be analyzed using different deep learning architectures to provide an additional platform for capturing and classifying the numerous sensor-based actions an individual could be undertaking. This research compares HHAR models based on deep learning techniques. We study the influence of smartphone and wristwatch sensor data on model performance and classification of human activities. We train and evaluate deep learning models to achieve our study objective using a publically available standard dataset (HHAR) with a 5-fold cross-validation technique. Using smartphone sensors, research findings demonstrate that the RNN-based model is superior to CNN-based models, with the most fantastic accuracy of 98.42%. © 2022 IEEE.

关键词： Smartphones

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Hierarchical Human Activity Recognition Based on Smartwatch Sensors Using Branch Convolutional Neural Networks 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Hnoohom, Narit Maitrichit, Nagorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakorn Pathom Thailand 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: (数字)9783031209925

ISBN: (纸本)9783031209918

Human activity recognition (HAR) has become a popular research topic in artificial intelligence thanks to the development of smart wearable devices. The main goal of human activity recognition is to efficiently recognize human behavior based on available data sources such as videos and images, including sensory data from wearable devices. Recently, HAR research has achieved promising results using learning-based approaches, especially deep learning methods. However, the need for high performance is still an open problem for researchers proposing new methods. In this work, we investigated the improvement of HAR by hierarchical classification based on smartwatch sensors using deep learning (DL) methods. To achieve the research goal, we introduced branch convolutional neural networks (B-CNNs) to accurately recognize human activities hierarchically and compared them with baseline models. To evaluate the deep learning models, we used a complex HAR benchmark dataset called WISDM-HARB dataset that collects smartwatch sensor data from 18 physical activities. The experimental results showed that the B-CNNs outperformed the baseline convolutional neural network (CNN) models when the hierarchical connection between classes was not considered. Moreover, the results confirmed that branch CNNs with class hierarchy improved the recognition performance with the highest accuracy of 95.84%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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