This paper presents a novel low-cost and fully-portable instrumented shoe system for gait phase detection. The instrumented shoe consists of 174 independent sensing units constructed based on an off-the-shelf force-sensitive film known as the Velostat conductive copolymer. A zero potential method was implemented to address the crosstalk effect among the matrix-formed sensing arrays. A customized algorithm for gait event and phase detection was developed to estimate stance sub-phases including initial contact, flat foot, and push off. Experiments were carried out to evaluate the performance of the proposed instrumented shoe system in gait phase detection for both straight-line walking and turning walking. The results showed that the mean absolute time differences between the estimated phases by the proposed instrumented shoe system and the reference measurement ranged from 45 to 58 ms during straight-line walking and from 51 to 77 ms during turning walking, which were comparable to the state of art.Clinical and Translational Impact Statement—By allowing convenient gait monitoring in home healthcare settings, the proposed system enables extensive ADL data collection and facilitates developing effective treatment and rehabilitation strategies for patients with movement disorders.
{"title":"A Low-Cost Instrumented Shoe System for Gait Phase Detection Based on Foot Plantar Pressure Data","authors":"Xinyao Hu;Qingsong Duan;Junpeng Tang;Gengshu Chen;Zhong Zhao;Zhenglong Sun;Chao Chen;Xingda Qu","doi":"10.1109/JTEHM.2023.3319576","DOIUrl":"10.1109/JTEHM.2023.3319576","url":null,"abstract":"This paper presents a novel low-cost and fully-portable instrumented shoe system for gait phase detection. The instrumented shoe consists of 174 independent sensing units constructed based on an off-the-shelf force-sensitive film known as the Velostat conductive copolymer. A zero potential method was implemented to address the crosstalk effect among the matrix-formed sensing arrays. A customized algorithm for gait event and phase detection was developed to estimate stance sub-phases including initial contact, flat foot, and push off. Experiments were carried out to evaluate the performance of the proposed instrumented shoe system in gait phase detection for both straight-line walking and turning walking. The results showed that the mean absolute time differences between the estimated phases by the proposed instrumented shoe system and the reference measurement ranged from 45 to 58 ms during straight-line walking and from 51 to 77 ms during turning walking, which were comparable to the state of art.Clinical and Translational Impact Statement—By allowing convenient gait monitoring in home healthcare settings, the proposed system enables extensive ADL data collection and facilitates developing effective treatment and rehabilitation strategies for patients with movement disorders.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"84-96"},"PeriodicalIF":3.4,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10264157","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135755244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The continuous measurement of percutaneous oxygen saturation (SpO2) enables diseases that cause hypoxemia to be detected early and patients’ conditions to be monitored. Currently, SpO2 is mainly measured using a pulse oximeter, which, owing to its simplicity, can be used in clinical settings and at home. However, the pulse oximeter requires a sensor to be in contact with the skin; therefore, prolonged use of the pulse oximeter for neonates or patients with sensitive skin may cause local inflammation or stress due to restricted movement. In addition, owing to COVID-19, there has been a growing demand for the contactless measurement of SpO2. Several studies on measuring SpO2 without contact used skin video images have been conducted. However, in these studies, the SpO2 values were estimated using a linear regression model or a look-up table that required reference values obtained using a contact-type pulse oximeter. In this study, we propose a new technique for the contactless measurement of SpO2 that does not require reference values. Specifically, we used certain approaches that reduced the influence of non-pulsating components and utilized different light wavelengths of video images that penetrated subcutaneously to different depths. We experimentally investigated the accuracy of SpO2 measurements using the proposed methods. The results indicate that the proposed methods were more accurate than the conventional method.
{"title":"Non-Contact Measurement of Blood Oxygen Saturation Using Facial Video Without Reference Values","authors":"Soma Sasaki;Norihiro Sugita;Takanori Terai;Makoto Yoshizawa","doi":"10.1109/JTEHM.2023.3318643","DOIUrl":"10.1109/JTEHM.2023.3318643","url":null,"abstract":"The continuous measurement of percutaneous oxygen saturation (SpO2) enables diseases that cause hypoxemia to be detected early and patients’ conditions to be monitored. Currently, SpO2 is mainly measured using a pulse oximeter, which, owing to its simplicity, can be used in clinical settings and at home. However, the pulse oximeter requires a sensor to be in contact with the skin; therefore, prolonged use of the pulse oximeter for neonates or patients with sensitive skin may cause local inflammation or stress due to restricted movement. In addition, owing to COVID-19, there has been a growing demand for the contactless measurement of SpO2. Several studies on measuring SpO2 without contact used skin video images have been conducted. However, in these studies, the SpO2 values were estimated using a linear regression model or a look-up table that required reference values obtained using a contact-type pulse oximeter. In this study, we propose a new technique for the contactless measurement of SpO2 that does not require reference values. Specifically, we used certain approaches that reduced the influence of non-pulsating components and utilized different light wavelengths of video images that penetrated subcutaneously to different depths. We experimentally investigated the accuracy of SpO2 measurements using the proposed methods. The results indicate that the proposed methods were more accurate than the conventional method.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"76-83"},"PeriodicalIF":3.4,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10261985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135702649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1109/JTEHM.2023.3317189
Ji Soo Baik;Ji Hong Min;Sung-Hwa Ko;Mi Sook Yun;Byunghoon Lee;Nae Yoon Kang;Byeongil Kim;Hyunsuk Lee;Yong-Il Shin
Objective: There is a growing importance for the home-based (HB) support services, and computerized cognitive training (CCT) has been reported as an effective intervention for cognitive impairment. However, there is still a need for further verification of the effect of HB-CCT. This study aimed to determine the effectiveness of HB-CCT on the cognitive function of community-dwelling adults with mild cognitive impairment (MCI) as well as safety in its use. Methods: Fifty community-dwelling adults with MCI were included, of which 25 each were randomized to either HB-CCT or control groups. Evaluations of comprehensive cognition, memory, attention, language, executive function, and depression were performed before and after the intervention, including three times a week for eight weeks in the intervention group and eight weeks apart with no intervention in the control group. Results: In baseline and post-evaluation comparisons, the HB-CCT group showed significant improvements, while the control group did not show significant changes. Statistically significant variations were noted between the HB-CCT and control groups in all post-intervention evaluations relative to baseline. Additionally, no side effects were observed. Conclusion: Beneficial effects on cognition and depression were noted in the intervention group compared with the control group, suggesting that HB-CCT may be a positive tool for cognitive improvement in adults with MCI.
{"title":"Effects of Home-Based Computerized Cognitive Training in Community-Dwelling Adults With Mild Cognitive Impairment","authors":"Ji Soo Baik;Ji Hong Min;Sung-Hwa Ko;Mi Sook Yun;Byunghoon Lee;Nae Yoon Kang;Byeongil Kim;Hyunsuk Lee;Yong-Il Shin","doi":"10.1109/JTEHM.2023.3317189","DOIUrl":"10.1109/JTEHM.2023.3317189","url":null,"abstract":"Objective: There is a growing importance for the home-based (HB) support services, and computerized cognitive training (CCT) has been reported as an effective intervention for cognitive impairment. However, there is still a need for further verification of the effect of HB-CCT. This study aimed to determine the effectiveness of HB-CCT on the cognitive function of community-dwelling adults with mild cognitive impairment (MCI) as well as safety in its use. Methods: Fifty community-dwelling adults with MCI were included, of which 25 each were randomized to either HB-CCT or control groups. Evaluations of comprehensive cognition, memory, attention, language, executive function, and depression were performed before and after the intervention, including three times a week for eight weeks in the intervention group and eight weeks apart with no intervention in the control group. Results: In baseline and post-evaluation comparisons, the HB-CCT group showed significant improvements, while the control group did not show significant changes. Statistically significant variations were noted between the HB-CCT and control groups in all post-intervention evaluations relative to baseline. Additionally, no side effects were observed. Conclusion: Beneficial effects on cognition and depression were noted in the intervention group compared with the control group, suggesting that HB-CCT may be a positive tool for cognitive improvement in adults with MCI.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"97-105"},"PeriodicalIF":3.4,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10255637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135550856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1109/JTEHM.2023.3311022
Fan Mao;Tianqi Huang;Longfei Ma;Xinran Zhang;Hongen Liao
During minimal invasive surgery (MIS), the laparoscope only provides a single viewpoint to the surgeon, leaving a lack of 3D perception. Many works have been proposed to obtain depth and 3D reconstruction by designing a new optical structure or by depending on the camera pose and image sequences. Most of these works modify the structure of the conventional laparoscopes and cannot provide 3D reconstruction of different magnification views. In this study, we propose a laparoscopic system based on double liquid lenses, which provide doctors with variable magnification rates, near observation, and real-time monocular 3D reconstruction. Our system composes of an optical structure that can obtain auto magnification change and autofocus without any physically moving element, and a deep learning network based on the Depth from Defocus (DFD) method, trained to suit inconsistent camera intrinsic situations and estimate depth from images of different focal lengths. The optical structure is portable and can be mounted on conventional laparoscopes. The depth estimation network estimates depth in real-time from monocular images of different focal lengths and magnification rates. Experiments show that our system provides a 0.68-1.44x zoom rate and can estimate depth from different magnification rates at 6fps. Monocular 3D reconstruction reaches at least 6mm accuracy. The system also provides a clear view even under 1mm close working distance. Ex-vivo experiments and implementation on clinical images prove that our system provides doctors with a magnified clear view of the lesion, as well as quick monocular depth perception during laparoscopy, which help surgeons get better detection and size diagnosis of the abdomen during laparoscope surgeries.
{"title":"A Monocular Variable Magnifications 3D Laparoscope System Using Double Liquid Lenses","authors":"Fan Mao;Tianqi Huang;Longfei Ma;Xinran Zhang;Hongen Liao","doi":"10.1109/JTEHM.2023.3311022","DOIUrl":"10.1109/JTEHM.2023.3311022","url":null,"abstract":"During minimal invasive surgery (MIS), the laparoscope only provides a single viewpoint to the surgeon, leaving a lack of 3D perception. Many works have been proposed to obtain depth and 3D reconstruction by designing a new optical structure or by depending on the camera pose and image sequences. Most of these works modify the structure of the conventional laparoscopes and cannot provide 3D reconstruction of different magnification views. In this study, we propose a laparoscopic system based on double liquid lenses, which provide doctors with variable magnification rates, near observation, and real-time monocular 3D reconstruction. Our system composes of an optical structure that can obtain auto magnification change and autofocus without any physically moving element, and a deep learning network based on the Depth from Defocus (DFD) method, trained to suit inconsistent camera intrinsic situations and estimate depth from images of different focal lengths. The optical structure is portable and can be mounted on conventional laparoscopes. The depth estimation network estimates depth in real-time from monocular images of different focal lengths and magnification rates. Experiments show that our system provides a 0.68-1.44x zoom rate and can estimate depth from different magnification rates at 6fps. Monocular 3D reconstruction reaches at least 6mm accuracy. The system also provides a clear view even under 1mm close working distance. Ex-vivo experiments and implementation on clinical images prove that our system provides doctors with a magnified clear view of the lesion, as well as quick monocular depth perception during laparoscopy, which help surgeons get better detection and size diagnosis of the abdomen during laparoscope surgeries.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"32-42"},"PeriodicalIF":3.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10237256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62231979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reactive postural control is an important component of the balance function for fall prevention. Perturbation-based balance exercises improve reactive postural control; however, these exercises require large, complex instruments and expert medical guidance. This study investigates the effects of unexpected perturbation-based balance exercises using a wearable balance exercise device (WBED) on reactive postural control. Eighteen healthy adult males participated in this study. Participants were assigned to the WBED and Sham groups. In the intervention session, participants in the WBED group randomly underwent unexpected perturbation in the mediolateral direction, while the Sham group performed the same exercises without perturbation. Before and after the intervention session, all participants underwent evaluation of reactive balance function using air cylinders. Peak displacement (D), time at peak displacement (T), peak velocity (V), and root mean square (RMS) of center of pressure (COP) data were measured. For mediolateral and anteroposterior COP (COPML and COP $_{mathrm {AP}}$ ), the main effects of group and time factors (pre/post) were investigated through the analysis of variance for split-plot factorial design. In the WBED group, the D-COPML and V-COPML of the post-test significantly decreased compared to those of the pre-test (p = 0.017 and p = 0.003, respectively). Furthermore, the D-COPAP and RMSAP of the post-test significantly decreased compared to those of the pre-test (p = 0.036 and p = 0.015, respectively). This study proved that the perturbation-based balance exercise using WBED immediately improved reactive postural control. Therefore, wearable exercise devices, such as WBED, may contribute to the prevention of falls and fall-related injuries.
{"title":"Perturbation-Based Balance Exercise Using a Wearable Device to Improve Reactive Postural Control","authors":"Masataka Yamamoto;Koji Shimatani;Daiki Yoshikawa;Taku Washida;Hiroshi Takemura","doi":"10.1109/JTEHM.2023.3310503","DOIUrl":"10.1109/JTEHM.2023.3310503","url":null,"abstract":"Reactive postural control is an important component of the balance function for fall prevention. Perturbation-based balance exercises improve reactive postural control; however, these exercises require large, complex instruments and expert medical guidance. This study investigates the effects of unexpected perturbation-based balance exercises using a wearable balance exercise device (WBED) on reactive postural control. Eighteen healthy adult males participated in this study. Participants were assigned to the WBED and Sham groups. In the intervention session, participants in the WBED group randomly underwent unexpected perturbation in the mediolateral direction, while the Sham group performed the same exercises without perturbation. Before and after the intervention session, all participants underwent evaluation of reactive balance function using air cylinders. Peak displacement (D), time at peak displacement (T), peak velocity (V), and root mean square (RMS) of center of pressure (COP) data were measured. For mediolateral and anteroposterior COP (COPML and COP\u0000<inline-formula> <tex-math>$_{mathrm {AP}}$ </tex-math></inline-formula>\u0000), the main effects of group and time factors (pre/post) were investigated through the analysis of variance for split-plot factorial design. In the WBED group, the D-COPML and V-COPML of the post-test significantly decreased compared to those of the pre-test (p = 0.017 and p = 0.003, respectively). Furthermore, the D-COPAP and RMSAP of the post-test significantly decreased compared to those of the pre-test (p = 0.036 and p = 0.015, respectively). This study proved that the perturbation-based balance exercise using WBED immediately improved reactive postural control. Therefore, wearable exercise devices, such as WBED, may contribute to the prevention of falls and fall-related injuries.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"515-522"},"PeriodicalIF":3.4,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10236569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62231963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.1109/JTEHM.2023.3308196
Chenlong Wang;Lei Liu;Wenhai Zhuo;Yun Xie
Objective: Epilepsy, an enduring neurological disorder, afflicts approximately 65 million individuals globally, significantly impacting their physical and mental wellbeing. Traditional epilepsy detection methods are labor-intensive, leading to inefficiencies. Although deep learning techniques for brain signal detection have gained traction in recent years, their clinical application advancement is hindered by the significant requirement for high-quality data and computational resources during training. Methods & Results: The neural network training initially involved merging two datasets of different data quality, namely Bonn University datasets and CHB-MIT datasets, to bolster its generalization capabilities. To tackle the issues of dataset size and class imbalance, we employed small window segmentation and Synthetic Minority Over-sampling Technique (SMOTE). algorithms to augment and equalize the data. A streamlined neural network architecture was then proposed, drastically reducing the model’s training parameters. Notably, a model trained with a mere 9,371 parameters yielded impressive results. The three-classification task on the combined dataset delivered an accuracy of 98.52%, sensitivity of 97.99%, specificity of 99.35%, and precision of 98.44%.Conclusion: The experimental findings of this study underscore the superiority of the proposed method over existing approaches in both model size reduction and accuracy enhancement. As a result, it is more apt for deployment in low-cost, low computational hardware devices, including wearable technology, and various clinical applications. Clinical and Translational Impact Statement— This study is a Pre-Clinical Research. The lightweight neural network is easily deployed on hardware device for real-time epileptic EEG detection.
{"title":"An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network","authors":"Chenlong Wang;Lei Liu;Wenhai Zhuo;Yun Xie","doi":"10.1109/JTEHM.2023.3308196","DOIUrl":"10.1109/JTEHM.2023.3308196","url":null,"abstract":"Objective: Epilepsy, an enduring neurological disorder, afflicts approximately 65 million individuals globally, significantly impacting their physical and mental wellbeing. Traditional epilepsy detection methods are labor-intensive, leading to inefficiencies. Although deep learning techniques for brain signal detection have gained traction in recent years, their clinical application advancement is hindered by the significant requirement for high-quality data and computational resources during training. Methods & Results: The neural network training initially involved merging two datasets of different data quality, namely Bonn University datasets and CHB-MIT datasets, to bolster its generalization capabilities. To tackle the issues of dataset size and class imbalance, we employed small window segmentation and Synthetic Minority Over-sampling Technique (SMOTE). algorithms to augment and equalize the data. A streamlined neural network architecture was then proposed, drastically reducing the model’s training parameters. Notably, a model trained with a mere 9,371 parameters yielded impressive results. The three-classification task on the combined dataset delivered an accuracy of 98.52%, sensitivity of 97.99%, specificity of 99.35%, and precision of 98.44%.Conclusion: The experimental findings of this study underscore the superiority of the proposed method over existing approaches in both model size reduction and accuracy enhancement. As a result, it is more apt for deployment in low-cost, low computational hardware devices, including wearable technology, and various clinical applications. Clinical and Translational Impact Statement— This study is a Pre-Clinical Research. The lightweight neural network is easily deployed on hardware device for real-time epileptic EEG detection.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"22-31"},"PeriodicalIF":3.4,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10229231","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62231512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-04DOI: 10.1109/JTEHM.2023.3300929
Kyle L. Jackson;Zoran Durić;Susannah M. Engdahl;Anthony C. Santago;Siddhartha Sikdar;Lynn H. Gerber
There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings.
{"title":"A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks","authors":"Kyle L. Jackson;Zoran Durić;Susannah M. Engdahl;Anthony C. Santago;Siddhartha Sikdar;Lynn H. Gerber","doi":"10.1109/JTEHM.2023.3300929","DOIUrl":"10.1109/JTEHM.2023.3300929","url":null,"abstract":"There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"10-21"},"PeriodicalIF":3.4,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10209065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62231501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-24DOI: 10.1109/JTEHM.2023.3294904
Daniel Bar-David;Laura Bar-David;Yinon Shapira;Rina Leibu;Dalia Dori;Aseel Gebara;Ronit Schneor;Anath Fischer;Shiri Soudry
– Objective: To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME). Methods: Prospective evaluation of OCT images of DME (n = 320) subject to elastic transformation, with the deformation intensity represented by ( $sigma$ ). Three sets of images, each comprising 100 pairs of scans (100 original & 100 modified), were grouped according to the range of ( $sigma$ ), including low-, medium- and high-degree of augmentation; ( $sigma $ = 1-6), ( $sigma $ = 7-12), and ( $sigma $ = 13-18), respectively. Three retina specialists evaluated all datasets in a blinded manner and designated each image as ’original‘ versus ’modified‘. The rate of assignment of ’original‘ value to modified images (false-negative) was determined for each grader in each dataset. Results: The false-negative rates ranged between 71-77% for the low-, 63-76% for the medium-, and 50-75% for the high-augmentation categories. The corresponding rates of correct identification of original images ranged between 75-85% ( $text{p}>$ 0.05) in the low-, 73-85% ( $text{p}>$ 0.05 for graders 1 & 2, p = 0.01 for grader 3) in the medium-, and 81-91% ( $text{p} < 0.005$ ) in the high-augmentation categories. In the subcategory ( $sigma $ = 7-9) the false-negative rates were 93-83%, whereas the rates of correctly identifying original images ranged between 89-99% ( $text{p}>$ 0.05 for all graders). Conclusions: Deformation of low-medium intensity ( $sigma $ = 1-9) may be applied without compromising OCT image representativeness in DME. Clinical and Translational Impact Statement—Elastic deformation may efficiently augment the size, robustness, and diversity of training datasets without altering their clinical value, enhancing the development of high-accuracy algorithms for automated interpretation of OCT images.
{"title":"Elastic Deformation of Optical Coherence Tomography Images of Diabetic Macular Edema for Deep-Learning Models Training: How Far to Go?","authors":"Daniel Bar-David;Laura Bar-David;Yinon Shapira;Rina Leibu;Dalia Dori;Aseel Gebara;Ronit Schneor;Anath Fischer;Shiri Soudry","doi":"10.1109/JTEHM.2023.3294904","DOIUrl":"10.1109/JTEHM.2023.3294904","url":null,"abstract":"– Objective: To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME). Methods: Prospective evaluation of OCT images of DME (n = 320) subject to elastic transformation, with the deformation intensity represented by (\u0000<inline-formula> <tex-math>$sigma$ </tex-math></inline-formula>\u0000). Three sets of images, each comprising 100 pairs of scans (100 original & 100 modified), were grouped according to the range of (\u0000<inline-formula> <tex-math>$sigma$ </tex-math></inline-formula>\u0000), including low-, medium- and high-degree of augmentation; (\u0000<inline-formula> <tex-math>$sigma $ </tex-math></inline-formula>\u0000 = 1-6), (\u0000<inline-formula> <tex-math>$sigma $ </tex-math></inline-formula>\u0000 = 7-12), and (\u0000<inline-formula> <tex-math>$sigma $ </tex-math></inline-formula>\u0000 = 13-18), respectively. Three retina specialists evaluated all datasets in a blinded manner and designated each image as ’original‘ versus ’modified‘. The rate of assignment of ’original‘ value to modified images (false-negative) was determined for each grader in each dataset. Results: The false-negative rates ranged between 71-77% for the low-, 63-76% for the medium-, and 50-75% for the high-augmentation categories. The corresponding rates of correct identification of original images ranged between 75-85% (\u0000<inline-formula> <tex-math>$text{p}>$ </tex-math></inline-formula>\u00000.05) in the low-, 73-85% (\u0000<inline-formula> <tex-math>$text{p}>$ </tex-math></inline-formula>\u00000.05 for graders 1 & 2, p = 0.01 for grader 3) in the medium-, and 81-91% (\u0000<inline-formula> <tex-math>$text{p} < 0.005$ </tex-math></inline-formula>\u0000) in the high-augmentation categories. In the subcategory (\u0000<inline-formula> <tex-math>$sigma $ </tex-math></inline-formula>\u0000 = 7-9) the false-negative rates were 93-83%, whereas the rates of correctly identifying original images ranged between 89-99% (\u0000<inline-formula> <tex-math>$text{p}>$ </tex-math></inline-formula>\u00000.05 for all graders). Conclusions: Deformation of low-medium intensity (\u0000<inline-formula> <tex-math>$sigma $ </tex-math></inline-formula>\u0000 = 1-9) may be applied without compromising OCT image representativeness in DME. Clinical and Translational Impact Statement—Elastic deformation may efficiently augment the size, robustness, and diversity of training datasets without altering their clinical value, enhancing the development of high-accuracy algorithms for automated interpretation of OCT images.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"487-494"},"PeriodicalIF":3.4,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3f/b0/jtehm-bardavid-3294904.PMC10561735.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1109/JTEHM.2023.3295775
Junhwan Kwon;Oyun Kwon;Kyeong Taek Oh;Jeongmin Kim;Sun K. Yoo
Breathing can be measured in a non-contact method using a thermal camera. The objective of this study investigates non-contact breathing measurements using thermal cameras, which have previously been limited to measuring the nostril only from the front where it is clearly visible. The previous method is challenging to use for other angles and frontal views, where the nostril is not well-represented. In this paper, we defined a new region called the breathing-associated-facial-region (BAFR) that reflects the physiological characteristics of breathing, and extract breathing signals from views of 45 and 90 degrees, including the frontal view where the nostril is not clearly visible. Experiments were conducted on fifteen healthy subjects in different views, including frontal with and without nostril, 45-degree, and 90-degree views. A thermal camera (A655sc model, FLIR systems) was used for non-contact measurement, and biopac (MP150, Biopac-systems-Inc) was used as a chest breathing reference. The results showed that the proposed algorithm could extract stable breathing signals at various angles and views, achieving an average breathing cycle accuracy of 90.9% when applied compared to 65.6% without proposed algorithm. The average correlation value increases from 0.587 to 0.885. The proposed algorithm can be monitored in a variety of environments and extract the BAFR at diverse angles and views.
{"title":"Breathing-Associated Facial Region Segmentation for Thermal Camera-Based Indirect Breathing Monitoring","authors":"Junhwan Kwon;Oyun Kwon;Kyeong Taek Oh;Jeongmin Kim;Sun K. Yoo","doi":"10.1109/JTEHM.2023.3295775","DOIUrl":"10.1109/JTEHM.2023.3295775","url":null,"abstract":"Breathing can be measured in a non-contact method using a thermal camera. The objective of this study investigates non-contact breathing measurements using thermal cameras, which have previously been limited to measuring the nostril only from the front where it is clearly visible. The previous method is challenging to use for other angles and frontal views, where the nostril is not well-represented. In this paper, we defined a new region called the breathing-associated-facial-region (BAFR) that reflects the physiological characteristics of breathing, and extract breathing signals from views of 45 and 90 degrees, including the frontal view where the nostril is not clearly visible. Experiments were conducted on fifteen healthy subjects in different views, including frontal with and without nostril, 45-degree, and 90-degree views. A thermal camera (A655sc model, FLIR systems) was used for non-contact measurement, and biopac (MP150, Biopac-systems-Inc) was used as a chest breathing reference. The results showed that the proposed algorithm could extract stable breathing signals at various angles and views, achieving an average breathing cycle accuracy of 90.9% when applied compared to 65.6% without proposed algorithm. The average correlation value increases from 0.587 to 0.885. The proposed algorithm can be monitored in a variety of environments and extract the BAFR at diverse angles and views.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"505-514"},"PeriodicalIF":3.4,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/fa/2d/jtehm-yoo-3295775.PMC10561734.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: It is important to improve caregiving skills to help reduce the strain on inexperienced caregivers. Previous studies on quantifying caregiving skills have predominantly relied on expensive equipment, such as motion-capture systems with multiple infrared cameras or acceleration sensors. To overcome the cost and space limitations of existing systems, we developed a simple evaluation system for transfer care skills that uses capacitive sensors composed of conductive embroidery fibers. The proposed system can be developed with a few thousand US dollars. Method: The developed evaluation system was used to compare the seating position and velocity of a care recipient during transfers from a nursing-care bed to a wheelchair between groups of inexperienced and expert caregivers. To validate the proposed system, we compare the motion data measured by our system and the data obtained from a conventional three-dimensional motion-capture system and force plate. Results: We analyze the relationship between changes in the center of pressure (CoP) recorded by the force plate and the center of gravity (CoG) obtained by the developed system. Evidently, the changes in CoP have a relation with the CoG. We show that the actual seating speed ( $v_{mathrm {z}}) $ measured by the motion-capture system is related to the speed coefficient calculated from our sensor output. A significant difference exists in $v_{mathrm {z}}$ between the inexperienced group and the physical therapists/occupational therapists’ group. Conclusions: The proposed system can effectively estimate a caregiver’s skill level in transferring patients from a bed to a wheelchair in terms of the seating position and velocity.
{"title":"Development of an Evaluation System for Transfer Care Skills Using Embroidered Body Pressure and Proximity Sensor","authors":"Hirofumi Kurosaki;Hiromu Shirahata;Junya Kawahara;Yasuhito Kondo;Ken Kondo;Bumsuk Lee;Masato Odagaki","doi":"10.1109/JTEHM.2023.3294062","DOIUrl":"10.1109/JTEHM.2023.3294062","url":null,"abstract":"Objective: It is important to improve caregiving skills to help reduce the strain on inexperienced caregivers. Previous studies on quantifying caregiving skills have predominantly relied on expensive equipment, such as motion-capture systems with multiple infrared cameras or acceleration sensors. To overcome the cost and space limitations of existing systems, we developed a simple evaluation system for transfer care skills that uses capacitive sensors composed of conductive embroidery fibers. The proposed system can be developed with a few thousand US dollars. Method: The developed evaluation system was used to compare the seating position and velocity of a care recipient during transfers from a nursing-care bed to a wheelchair between groups of inexperienced and expert caregivers. To validate the proposed system, we compare the motion data measured by our system and the data obtained from a conventional three-dimensional motion-capture system and force plate. Results: We analyze the relationship between changes in the center of pressure (CoP) recorded by the force plate and the center of gravity (CoG) obtained by the developed system. Evidently, the changes in CoP have a relation with the CoG. We show that the actual seating speed (\u0000<inline-formula> <tex-math>$v_{mathrm {z}}) $ </tex-math></inline-formula>\u0000 measured by the motion-capture system is related to the speed coefficient calculated from our sensor output. A significant difference exists in \u0000<inline-formula> <tex-math>$v_{mathrm {z}}$ </tex-math></inline-formula>\u0000 between the inexperienced group and the physical therapists/occupational therapists’ group. Conclusions: The proposed system can effectively estimate a caregiver’s skill level in transferring patients from a bed to a wheelchair in terms of the seating position and velocity.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"11 ","pages":"460-468"},"PeriodicalIF":3.4,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10177745","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41220008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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