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Item Metadata only Deep Learning Networks for Eating and Drinking Recognition based on Smartwatch Sensors(2022-01-01) Mekruksavanich S.; Jantawong P.; Hnoohom N.; Jitpattanakul A.; Mahidol Universityactivities such as eating-related activities. This paper proposes a sensor-based HAR framework using data from eating-related activities recorded by a smartwatch sensor. In this framework, five d eep learning networks (CNN, LSTM, BiLSTM, Stacked LSTM, CNN...Smartwatches are becoming more popular for recognizing and monitoring human actions in everyday life. These wearable devices are equipped with various IMU sensors for ubiquitous data processing and recording of human physical activity data. SensorItem Metadata only Heterogeneous Recognition of Human Activity with CNN and RNN-based Networks using Smartphone and Smartwatch Sensors(2022-01-01) Mekruksavanich S.; Jantawong P.; Hnoohom N.; Jitpattanakul A.; Mahidol University. 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... 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%.Item Metadata only ResNet-based Network for Recognizing Daily and Transitional Activities based on Smartphone Sensors(2022-01-01) Mekruksavanich S.; Jantawong P.; Hnoohom N.; Jitpattanakul A.; Mahidol UniversityIn 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... 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 93Item Metadata only Deep Learning Approaches for Unobtrusive Human Activity Recognition using Insole-based and Smartwatch Sensors(2022-01-01) Hnoohom N.; Maitrichit N.; Mekruksavanich S.; Jitpattanakul A.; Mahidol University-based HAR, various wearable sensors have been investigated for their usefulness in effectively detecting both simple and complex human activities. In this work, we studied HAR using signal data acquired from insole-based and smartwatch sensors... detecting abnormal activity and notifying the pertinent authorities, and increasing human contact with computers are all examples of HAR applications. The data collecting tools used in HAR research can also be categorized (sensors or cameras). In sensorItem Metadata only Recognition of Shoulder Exercise Activity Based on EfficientNet Using Smartwatch Inertial Sensors(2022-01-01) Hnoohom N.; Chotivatunyu P.; Mekruksavanich S.; Jitpattanakul A.; Mahidol UniversityRecognition 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... 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 architectureItem Metadata only Recognizing Driver Activities Using Deep Learning Approaches Based on Smartphone Sensors(2022-01-01) Mekruksavanich S.; Jantawong P.; Hnoohom N.; Jitpattanakul A.; Mahidol UniversityHuman motion detection based on smartphone sensors has gained popularity for identifying everyday activities and enhancing situational awareness in pervasive and ubiquitous computing research. Modern machine learning and deep learning classifiers... have been demonstrated on benchmark datasets to interpret people’s behaviors, including driving activities. While driving, driver behavior recognition may assist in activating accident detection. In this paper, we investigate driving behavior detectionItem Metadata only The Effect of Sensor Placement for Accurate Fall Detection based on Deep Learning Model(2022-01-01) Mekruksavanich S.; Jantawong P.; Hnoohom N.; Jitpattanakul A.; Mahidol UniversityThe 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 fallsItem Metadata only Refined LSTM Network for Sensor-based Human Activity Recognition in Real World Scenario(2022-01-01) Mekruksavanich S.; Jantawong P.; Hnoohom N.; Jitpattanakul A.; Mahidol UniversitySensor-based identification of human actions is an essential field of study in ubiquitous computing. This aims to facilitate the assessment or understanding of current occurrences and their context based on sensor signals. Activity recognitionItem Metadata only Time Series Classification Using Deep Learning for HAR Based on Smart Wearable Sensors(2022-01-01) Jantawong P.; Hnoohom N.; Jitpattanakul A.; Mekruksavanich S.; Mahidol UniversityIn the last decades, time series classification (TSC) has emerged as one of the most challenging issues in data mining, and extensive studies have been done on various methods, including algorithm-based and learning-based techniques. Sensor-based... human activity recognition (HAR) is a TSC issue that has become one of the most sought-after fields among business and academia specialists because of the proliferation of smartphone technology and wearable movement sensors. Conventional approachesItem Metadata only Deep Learning Models for Daily Living Activity Recognition based on Wearable Inertial Sensors(2022-01-01) Mekruksavanich S.; Jantawong P.; Hnoohom N.; Jitpattanakul A.; Mahidol University, among other applications. HAR based on wearable inertial sensors has been researched identification efficiency in various kinds of human actions considerably more than vision-based HAR. The sensor-based HAR is generally applicable to indoor and outdoor... proposed model using a standard HAR dataset called Daily Living Activity dataset. These datasets gathered mobility signal data from multimodal sensors (accelerometer, gyroscope, and magnetometer) in three distinct body areas (wrist, hip, and ankle
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