Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson’s disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieved high resolution. Among these approaches, electrooculography (EOG)-based gaze estimation, as an economical and effective method, offers a promising solution for practical applications. Objective: In this paper, we systematically investigated the possible EOG electrode locations which are spatially distributed around the orbital cavity. Afterward, quantities of informative features to characterize physiological information of eye movement from the temporal-spectral domain are extracted from the seven differential channels. Methods and procedures: To select the optimum channels and relevant features, and eliminate irrelevant information, a heuristical search algorithm (i.e., forward stepwise strategy) is applied. Subsequently, a comparative analysis of the impacts of electrode placement and feature contributions on gaze estimation is evaluated via 6 classic models with 18 subjects. Results: Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from −50° to +50°. The MAE and RMSE can be improved to 2.80° and 3.74° ultimately, while only using 10 features extracted from 2 channels. Compared with the prevailing EOG-based techniques, the performance improvement of MAE and RMSE range from 0.70° to 5.48° and 0.66° to 5.42°, respectively. Conclusion: We proposed a robust EOG-based gaze estimation approach by systematically investigating the optimal channel/feature combination. The experimental results indicated not only the superiority of the proposed approach but also its potential for clinical application. Clinical and translational impact statement: Accurate gaze estimation is a key step for assisting disabilities and accurate diagnosis of various diseases including ASD, Parkinson’s disease, and ADHD. The proposed approach can accurately estimate the points of gaze via EOG signals, and thus has the potential for various related medical applications.
{"title":"A Robust Gaze Estimation Approach via Exploring Relevant Electrooculogram Features and Optimal Electrodes Placements","authors":"Zheng Zeng;Linkai Tao;Hangyu Zhu;Yunfeng Zhu;Long Meng;Jiahao Fan;Chen Chen;Wei Chen","doi":"10.1109/JTEHM.2023.3320713","DOIUrl":"10.1109/JTEHM.2023.3320713","url":null,"abstract":"Gaze estimation, as a technique that reflects individual attention, can be used for disability assistance and assisting physicians in diagnosing diseases such as autism spectrum disorder (ASD), Parkinson’s disease, and attention deficit hyperactivity disorder (ADHD). Various techniques have been proposed for gaze estimation and achieved high resolution. Among these approaches, electrooculography (EOG)-based gaze estimation, as an economical and effective method, offers a promising solution for practical applications. Objective: In this paper, we systematically investigated the possible EOG electrode locations which are spatially distributed around the orbital cavity. Afterward, quantities of informative features to characterize physiological information of eye movement from the temporal-spectral domain are extracted from the seven differential channels. Methods and procedures: To select the optimum channels and relevant features, and eliminate irrelevant information, a heuristical search algorithm (i.e., forward stepwise strategy) is applied. Subsequently, a comparative analysis of the impacts of electrode placement and feature contributions on gaze estimation is evaluated via 6 classic models with 18 subjects. Results: Experimental results showed that the promising performance was achieved both in the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) within a wide gaze that ranges from −50° to +50°. The MAE and RMSE can be improved to 2.80° and 3.74° ultimately, while only using 10 features extracted from 2 channels. Compared with the prevailing EOG-based techniques, the performance improvement of MAE and RMSE range from 0.70° to 5.48° and 0.66° to 5.42°, respectively. Conclusion: We proposed a robust EOG-based gaze estimation approach by systematically investigating the optimal channel/feature combination. The experimental results indicated not only the superiority of the proposed approach but also its potential for clinical application. Clinical and translational impact statement: Accurate gaze estimation is a key step for assisting disabilities and accurate diagnosis of various diseases including ASD, Parkinson’s disease, and ADHD. The proposed approach can accurately estimate the points of gaze via EOG signals, and thus has the potential for various related medical applications.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"56-65"},"PeriodicalIF":3.4,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10268026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135843030","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 : 2023-09-29DOI: 10.1109/JTEHM.2023.3320715
Qi Luo;Minglei Bai;Shuhan Chen;Kai Gao;Lairong Yin;Ronghua Du
Prosthetic hands are frequently rejected due to frustrations in daily uses. By adopting principles of human neuromuscular control, it could potentially achieve human-like compliance in hand functions, thereby improving functionality in prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of neuromuscular reflex for prosthetic control. This study further to explore the effect of feedforward electromyograph (EMG) decoding and proprioception on the biomimetic controller. The biomimetic controller included a feedforward Bayesian model for decoding alpha motor commands from stump EMG, a muscle model, and a closed-loop component with a model of muscle spindle modified with spiking afferents. Real-time control was enabled by neuromorphic hardware to accelerate evaluation of biologically inspired models. This allows us to investigate which aspects in the controller could benefit from biological properties for improvements on force control performance. 3 non-disabled and 3 amputee subjects were recruited to conduct a “press-without-break” task, subjects were required to press a transducer till the pressure stabilized in an expected range without breaking the virtual object. We tested whether introducing more complex but biomimetic models could enhance the task performance. Data showed that when replacing proportional feedback with the neuromorphic spindle, success rates of amputees increased by 12.2% and failures due to breakage decreased by 26.3%. More prominently, success rates increased by 55.5% and failures decreased by 79.3% when replacing a linear model of EMG with the Bayesian model in the feedforward EMG processing. Results suggest that mimicking biological properties in feedback and feedforward control may improve the manipulation of objects by amputees using prosthetic hands. Clinical and Translational Impact Statement: This control approach may eventually assist amputees to perform fine force control when using prosthetic hands, thereby improving the motor performance of amputees. It highlights the promising potential of the biomimetic controller integrating biological properties implemented on neuromorphic models as a viable approach for clinical application in prosthetic hands.
{"title":"Enhancing Force Control of Prosthetic Controller for Hand Prosthesis by Mimicking Biological Properties","authors":"Qi Luo;Minglei Bai;Shuhan Chen;Kai Gao;Lairong Yin;Ronghua Du","doi":"10.1109/JTEHM.2023.3320715","DOIUrl":"10.1109/JTEHM.2023.3320715","url":null,"abstract":"Prosthetic hands are frequently rejected due to frustrations in daily uses. By adopting principles of human neuromuscular control, it could potentially achieve human-like compliance in hand functions, thereby improving functionality in prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of neuromuscular reflex for prosthetic control. This study further to explore the effect of feedforward electromyograph (EMG) decoding and proprioception on the biomimetic controller. The biomimetic controller included a feedforward Bayesian model for decoding alpha motor commands from stump EMG, a muscle model, and a closed-loop component with a model of muscle spindle modified with spiking afferents. Real-time control was enabled by neuromorphic hardware to accelerate evaluation of biologically inspired models. This allows us to investigate which aspects in the controller could benefit from biological properties for improvements on force control performance. 3 non-disabled and 3 amputee subjects were recruited to conduct a “press-without-break” task, subjects were required to press a transducer till the pressure stabilized in an expected range without breaking the virtual object. We tested whether introducing more complex but biomimetic models could enhance the task performance. Data showed that when replacing proportional feedback with the neuromorphic spindle, success rates of amputees increased by 12.2% and failures due to breakage decreased by 26.3%. More prominently, success rates increased by 55.5% and failures decreased by 79.3% when replacing a linear model of EMG with the Bayesian model in the feedforward EMG processing. Results suggest that mimicking biological properties in feedback and feedforward control may improve the manipulation of objects by amputees using prosthetic hands. Clinical and Translational Impact Statement: This control approach may eventually assist amputees to perform fine force control when using prosthetic hands, thereby improving the motor performance of amputees. It highlights the promising potential of the biomimetic controller integrating biological properties implemented on neuromorphic models as a viable approach for clinical application in prosthetic hands.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"66-75"},"PeriodicalIF":3.4,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10268050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135844291","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 : 2023-09-28DOI: 10.1109/JTEHM.2023.3320132
Wei Li;Cheng Fang;Zhihao Zhu;Chuyi Chen;Aiguo Song
Electroencephalogram (EEG)-based emotion recognition is of great significance for aiding in clinical diagnosis, treatment, nursing and rehabilitation. Current research on this issue mainly focuses on utilizing various network architectures with different types of neurons to exploit the temporal, spectral, or spatial information from EEG for classification. However, most studies fail to take full advantage of the useful Temporal-Spectral-Spatial (TSS) information of EEG signals. In this paper, we propose a novel and effective Fractal Spike Neural Network (Fractal-SNN) scheme, which can exploit the multi-scale TSS information from EEG, for emotion recognition. Our designed Fractal-SNN block in the proposed scheme approximately simulates the biological neural connection structures based on spiking neurons and a new fractal rule, allowing for the extraction of discriminative multi-scale TSS features from the signals. Our designed training technique, inverted drop-path, can enhance the generalization ability of the Fractal-SNN scheme. Sufficient experiments on four public benchmark databases, DREAMER, DEAP, SEED-IV and MPED, under the subject-dependent protocols demonstrate the superiority of the proposed scheme over the related advanced methods. In summary, the proposed scheme provides a promising solution for EEG-based emotion recognition.
{"title":"Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition","authors":"Wei Li;Cheng Fang;Zhihao Zhu;Chuyi Chen;Aiguo Song","doi":"10.1109/JTEHM.2023.3320132","DOIUrl":"10.1109/JTEHM.2023.3320132","url":null,"abstract":"Electroencephalogram (EEG)-based emotion recognition is of great significance for aiding in clinical diagnosis, treatment, nursing and rehabilitation. Current research on this issue mainly focuses on utilizing various network architectures with different types of neurons to exploit the temporal, spectral, or spatial information from EEG for classification. However, most studies fail to take full advantage of the useful Temporal-Spectral-Spatial (TSS) information of EEG signals. In this paper, we propose a novel and effective Fractal Spike Neural Network (Fractal-SNN) scheme, which can exploit the multi-scale TSS information from EEG, for emotion recognition. Our designed Fractal-SNN block in the proposed scheme approximately simulates the biological neural connection structures based on spiking neurons and a new fractal rule, allowing for the extraction of discriminative multi-scale TSS features from the signals. Our designed training technique, inverted drop-path, can enhance the generalization ability of the Fractal-SNN scheme. Sufficient experiments on four public benchmark databases, DREAMER, DEAP, SEED-IV and MPED, under the subject-dependent protocols demonstrate the superiority of the proposed scheme over the related advanced methods. In summary, the proposed scheme provides a promising solution for EEG-based emotion recognition.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"106-118"},"PeriodicalIF":3.4,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10266337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135838992","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}
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}
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