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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint similarity (OKs) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPs. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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A VAE-Based Data Augmentation Method for Fall Detection system using Wearable sensor 10

A VAE-Based Data Augmentation Method for Fall Detection Syst...

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10th International Conference on Applied system Innovation, ICAsI 2024

作者： Tu, Yu-Chen Wang, Hsuan-Chih Liu, Chien-Pin Hsieh, Chia-Yeh Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Engineering Taipei City112 Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City242062 Taiwan

ISBN: (纸本)9798350394924

Fall incidents among the elderly represent a significant global concern, often resulting in physical injuries and psychological distress. It is crucial to develop reliable fall detection systems which are capable of identifying fall events immediately and triggering alerts for assistance. However, real-world fall occurrences are infrequent, leading to a highly imbalanced class situation. Training a model with imbalanced datasets may result in biased models with poor performance in fall detection. To address this challenge, various techniques such as data transformation and synthetic Minority Oversampling Technique (sMOTE) have been proposed. However, these methods are constrained by issues such as limitations in input data size or sensitivity to outliers. Compared to other methods, variational autoencoder (VAE) can generate data with a similar probability distribution to the original input data while constraining the latent representation in a Gaussian distribution. This study proposes a VAE-based data augmentation method for wearable-based fall detection system. The proposed method is validated on the FallAllD public dataset, achieving a F-score of 99.46%. The performance has been increased by 2.21%. The results demonstrate the effectiveness of VAE-based data augmentation technique in enhancing fall detection systems and its superior performance compared with other traditional data augmentation methods. © 2024 IEEE.

关键词： Wearable sensors

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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation 48

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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48th IEEE International Conference on Acoustics, speech and signal Processing, ICAssP 2023

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Engineering Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications Taiwan

ISBN: (纸本)9781728163277

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems"using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms). © 2023 IEEE.

关键词： Inertial measurement units Knowledge distillation Pre-impact fall detection Vision transformer Wearable sensors

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Wearable-based Frozen shoulder Rehabilitation Exercise Recognition using Machine Learning Approaches

Wearable-based Frozen Shoulder Rehabilitation Exercise Recog...

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2023 IEEE International symposium on medical Measurements and applications, MeMeA 2023

作者： Liu, Chien-Pin Lai, Chih-Chun Liu, Kai-Chun Hsieh, Chia-Yeh Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Enginnering Taipei City112 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City115 Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications New Taipei City242062 Taiwan

ISBN: (纸本)9781665493840

Frozen shoulder is a disease that causes shoulder pain and stiffness. It limits the range of movement of the shoulder and has a great impact on the quality of daily life. One of the common treatment methods is to do frozen shoulder rehabilitation exercises. However, patients often fail to follow the instructions of the physical therapists during the program of home-based rehabilitation. Furthermore, clinical professionals are unavailable to track and monitor the home-based rehabilitation exercise performance of patients. To support clinical monitoring, we develop a wearable-based frozen shoulder rehabilitation exercise recognizer using different machine learning models and deep learning models. The proposed methods can automatically identify movement/silence segments from continuous signals and classify types of frozen shoulder rehabilitation exercises. Besides, we propose a finite state machine and fragmentation revision mechanism for error correction. Twenty subjects are invited to perform six types of rehabilitation exercises. The proposed methods achieve the best result of 95.6% accuracy, 95.83% F-score for the identification of movement/silence and 95.58% accuracy, 95.49% F-score for classification of exercise type, respectively. The results demonstrate the feasibility of the proposed method to automatically monitor the frozen shoulder rehabilitation exercise, which has the potential to provide objective, continuous and quantitative information for telerehabilitation. © 2023 IEEE.

关键词： Patient rehabilitation

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Deep Learning-based Fall Detection Algorithm Using Ensemble ...

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2023 IEEE International symposium on medical Measurements and applications, MeMeA 2023

作者： Liu, Chien-Pin Li, Ju-Hsuan Chu, En-Ping Hsieh, Chia-Yeh Liu, Kai-Chun Chan, Chia-Tai Tsao, Yu National Yang Ming Chiao Tung University Department of Biomedical Engineering Taipei City112 Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications New Taipei City242062 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City115 Taiwan

ISBN: (纸本)9781665493840

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a 'long-lie.' Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LsTM) for FD. © 2023 IEEE.

关键词： Fall detection

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An Analysis of Fluid Intake Assessment Approaches for Fluid Intake Monitoring system

Biosensors

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Biosensors 2024年第1期14卷 14页

作者： Hsieh, Chia-Yeh Huang, Hsiang-Yun Chan, Chia-Tai Chiu, Li-Tzu Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City24205 Taiwan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City11221 Taiwan

Monitoring fluid intake is essential to help people manage their individual fluid intake behaviors and achieve adequate hydration. Previous studies of fluid intake assessment approaches based on inertial sensors can be categorized into wrist-worn-based and smart-container-based approaches. This study aims to analyze wrist-worn-based and smart-container-based fluid intake assessment approaches using inertial sensors. The comparison of these two approaches should be analyzed according to gesture recognition and volume estimation. In addition, the influence of the fill level and sip size information on the performance is explored in this study. The accuracy of gesture recognition with postprocessing is 92.89% and 91.8% for the wrist-worn-based approach and smart-container-based approach, respectively. For volume estimation, sip-size-dependent models can achieve better performance than general sVR models for both wrist-worn-based and smart-container-based approaches. The improvement of MAPE, MAD, and RMsE can reach over 50% except MAPE for small sip sizes. The results demonstrate that the sip size information and recognition performance are important for fluid intake assessment approaches. © 2023 by the authors.

关键词： Containers

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A VAE-Based Data Augmentation Method for Fall Detection system using Wearable sensor

A VAE-Based Data Augmentation Method for Fall Detection Syst...

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International Conference on Applied system Innovation (ICAsI)

作者： Yu-Chen Tu Hsuan-Chih Wang Chien-Pin Liu Chia-Yeh Hsieh Chia-Tai Chan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

关键词： Training Technological innovation sensitivity Psychology Data augmentation Probability distribution Reliability

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PREFALLKD: PRE-IMPACT FALL DETECTION VIA CNN-VIT KNOWLEDGE DIsTILLATION

arXiv

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arXiv 2023年

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems" using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms). Copyright © 2023, The Authors. All rights reserved.

关键词： Fall detection

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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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International Conference on Acoustics, speech, and signal Processing (ICAssP)

作者： Tin-Han Chi Kai-Chun Liu Chia-Yeh Hsieh Yu Tsao Chia-Tai Chan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems" using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms).

关键词： Computational modeling system performance Aging Transformers Reliability Fall detection Usability

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Deep Learning-based Fall Detection Algorithm Using Ensemble ...

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IEEE International Workshop on medical Measurement and applications (MEMEA)

作者： Chien-Pin Liu Ju-Hsuan Li En-Ping Chu Chia-Yeh Hsieh Kai-Chun Liu Chia-Tai Chan Yu Tsao Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a “long-lie.” Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LsTM) for FD.

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