Cry is the most common phenomenon among infants, and it has been reported that babies cry for multiple reasons. Infant cry signals are thought to convey much useful information about the physiological and pathological state of the baby. Hence, in this work we analyzed these audio signals in order to classify different reasons of cries. Cry signals were especially collected for this study including three causes, namely hunger, pain and uncertainty. Modified MFCC features besides basic acoustic features were extracted from each recording. After intergroup variance examination, nine features were selected and subjected to a novel matching process based on Dynamic Time Warping (DTW) for separating infant cries. Experiment results show that nine selected features are effective to recognize cries caused by hunger, pain and other uncertain reasons. The proposed approach for infant cry analysis will provide useful information for designing towards an automatic system for detecting physiological and pathological state of the baby
{"title":"An Investigation into Audio Features and DTW Algorithms for Infant Cry Classification","authors":"Xilin Yu, Laishuan Wang, Xian Zhao, Chunmei Lu, X. Long, Wei Chen","doi":"10.1145/3375923.3375929","DOIUrl":"https://doi.org/10.1145/3375923.3375929","url":null,"abstract":"Cry is the most common phenomenon among infants, and it has been reported that babies cry for multiple reasons. Infant cry signals are thought to convey much useful information about the physiological and pathological state of the baby. Hence, in this work we analyzed these audio signals in order to classify different reasons of cries. Cry signals were especially collected for this study including three causes, namely hunger, pain and uncertainty. Modified MFCC features besides basic acoustic features were extracted from each recording. After intergroup variance examination, nine features were selected and subjected to a novel matching process based on Dynamic Time Warping (DTW) for separating infant cries. Experiment results show that nine selected features are effective to recognize cries caused by hunger, pain and other uncertain reasons. The proposed approach for infant cry analysis will provide useful information for designing towards an automatic system for detecting physiological and pathological state of the baby","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75520571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: This paper aims on the physical strength reserves of diver after long time underwater delivery, to find the reasonable underwater working time which can maintain diver's work ability. Method: Select 16 divers for underwater deliver experiment for different length of time, and carry out PWC170 measurement analysis. Result: After 2.5 hours underwater delivery, the diver still has a certain level of physical strength reserves. But after 3 hours underwater delivery, the physical strength reserves close to zero. Conclusion: Through the experiment, we conclude that the reasonable underwater delivery time is 2.5 hours, which can maintain diver's work ability.
{"title":"Study on Physical Strength Reserves of Diver after Underwater Delivery","authors":"Fan Wei, Fu Xue Zhi, Liu Ping, Z. Yu","doi":"10.1145/3375923.3375932","DOIUrl":"https://doi.org/10.1145/3375923.3375932","url":null,"abstract":"Objective: This paper aims on the physical strength reserves of diver after long time underwater delivery, to find the reasonable underwater working time which can maintain diver's work ability. Method: Select 16 divers for underwater deliver experiment for different length of time, and carry out PWC170 measurement analysis. Result: After 2.5 hours underwater delivery, the diver still has a certain level of physical strength reserves. But after 3 hours underwater delivery, the physical strength reserves close to zero. Conclusion: Through the experiment, we conclude that the reasonable underwater delivery time is 2.5 hours, which can maintain diver's work ability.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80479420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Actions can be divided into two modes, one is driven by intention without stimulus, another is in response to external stimulus. Previous studies have shown that these two modes of movement may be manipulated by different physiological pathways in the brain. However, the neural coding of them in motor cortex is still unknown. In this study, we trained rhesus monkeys performing external stimulus-driven and internal intention-driven arm movement tasks, and recorded neuronal activity in primary motor cortex (M1). We aimed to compare the neuronal coding between stimulus-based and intention-based action modes. We found that neurons fired in different patterns while doing internal intention-driven arm movement. These neurons show no significant difference between intention-based and stimulus-based tasks during movement execution period. We also set up a general linear model to quantify the encoding strength of M1 neurons towards movement parameters in different action modes.
{"title":"Encoding of Stimulus-driven and Intention-driven Actions in Monkey's Primary Motor Cortex","authors":"Keyi Liu, Wenjuan Hu, Yao Chen","doi":"10.1145/3375923.3375945","DOIUrl":"https://doi.org/10.1145/3375923.3375945","url":null,"abstract":"Actions can be divided into two modes, one is driven by intention without stimulus, another is in response to external stimulus. Previous studies have shown that these two modes of movement may be manipulated by different physiological pathways in the brain. However, the neural coding of them in motor cortex is still unknown. In this study, we trained rhesus monkeys performing external stimulus-driven and internal intention-driven arm movement tasks, and recorded neuronal activity in primary motor cortex (M1). We aimed to compare the neuronal coding between stimulus-based and intention-based action modes. We found that neurons fired in different patterns while doing internal intention-driven arm movement. These neurons show no significant difference between intention-based and stimulus-based tasks during movement execution period. We also set up a general linear model to quantify the encoding strength of M1 neurons towards movement parameters in different action modes.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85181051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Echo-planar imaging suffers from Nyquist ghost (i.e., N/2 ghost) because of the imperfection of the gradient system and gradient delays. The phase mismatch between even and odd echoes can be eliminated by an extra reference scan without the phase encoding. However, due to the non-linear and time-varying local magnetic field changes or movement of the patients, the reference-based methods may have incorrect correction results. Other correction methods like parallel imaging reconstruction may suffer from the image noise amplification and signal-to-noise ratio penalty. In this study, a deep learning method is proposed to eliminate the phase error in k-space and correct the mismatch between even and odd echoes without reference scan and SNR penalty. The Fourier transform layer is introduced into the conventional U-Net structure, and the distortion-free images are directly reconstructed from the k-space EPI data. Turbo spin echo data and single-shot EPI data are tested using this network. The results show that this method has a good performance in ghost correction, and the ghost-to-signal ratio is effectively reduced compared to other state-of-the-art correction methods. The proposed deep learning method is reference-free and effective to correct Nyquist ghost in EPI, and can also combine with parallel imaging to achieve additional acceleration.
{"title":"Reference-free Correction for the Nyquist Ghost in Echo-planar Imaging using Deep Learning","authors":"Xudong Chen, Yufei Zhang, H. She, Yiping P. Du","doi":"10.1145/3375923.3375927","DOIUrl":"https://doi.org/10.1145/3375923.3375927","url":null,"abstract":"Echo-planar imaging suffers from Nyquist ghost (i.e., N/2 ghost) because of the imperfection of the gradient system and gradient delays. The phase mismatch between even and odd echoes can be eliminated by an extra reference scan without the phase encoding. However, due to the non-linear and time-varying local magnetic field changes or movement of the patients, the reference-based methods may have incorrect correction results. Other correction methods like parallel imaging reconstruction may suffer from the image noise amplification and signal-to-noise ratio penalty. In this study, a deep learning method is proposed to eliminate the phase error in k-space and correct the mismatch between even and odd echoes without reference scan and SNR penalty. The Fourier transform layer is introduced into the conventional U-Net structure, and the distortion-free images are directly reconstructed from the k-space EPI data. Turbo spin echo data and single-shot EPI data are tested using this network. The results show that this method has a good performance in ghost correction, and the ghost-to-signal ratio is effectively reduced compared to other state-of-the-art correction methods. The proposed deep learning method is reference-free and effective to correct Nyquist ghost in EPI, and can also combine with parallel imaging to achieve additional acceleration.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89913547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most of current prostheses and orthoses use physical springs and dampers with various control strategies to replicate the compliant behavior of a normal ankle during the gait. The springs, dampers and the control strategies are usually tuned for a single patient and for a fixed gait speed which does not allow adaptation to another patient or another gait speed. In this work, we propose a control strategy that overcomes those adaptation problems. The algorithm is based on an admittance control and replicates the ankle torque-angle curve to assist level-ground gait. The particularity of this control comes from the fact that the physical spring is replaced by a mechatronic spring. It uses principally force and position sensors in order to replicate the behavior of a physical spring. Thanks to the use of a mechatronic spring, the orthosis and the control strategy can easily be adapted to any individual and can adapt themselves to any gait speed.
{"title":"Control Algorithm for an Active Ankle-Foot Orthosis (AAFOs): Adaptative Admittance Control","authors":"Joseph Tsongo Vughuma, O. Verlinden","doi":"10.1145/3375923.3375931","DOIUrl":"https://doi.org/10.1145/3375923.3375931","url":null,"abstract":"Most of current prostheses and orthoses use physical springs and dampers with various control strategies to replicate the compliant behavior of a normal ankle during the gait. The springs, dampers and the control strategies are usually tuned for a single patient and for a fixed gait speed which does not allow adaptation to another patient or another gait speed. In this work, we propose a control strategy that overcomes those adaptation problems. The algorithm is based on an admittance control and replicates the ankle torque-angle curve to assist level-ground gait. The particularity of this control comes from the fact that the physical spring is replaced by a mechatronic spring. It uses principally force and position sensors in order to replicate the behavior of a physical spring. Thanks to the use of a mechatronic spring, the orthosis and the control strategy can easily be adapted to any individual and can adapt themselves to any gait speed.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89729458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Natsuda Navamajiti, Thammakorn Saethang, D. Wichadakul
One main function of long non-coding RNAs (lncRNAs) is to act as a scaffold facilitating multiple proteins to form complexes. Most of available prediction models for protein-RNA interactions, however, were proposed as a binary classifier, which limited on predicting the interaction between the non-coding RNAs and each individual RNA-binding protein (RBP). Hence, to predict if a lncRNA is acting as a scaffold, we consider this problem as a multiclass multilabel classification problem. To solve this problem, the high confident CLIP-seq data were selected from the POSTAR2 database with an augmentation of the data for the RBP classes with a small number of interacting lncRNAs. We then constructed a deep learning model for multiclass multilabel classification, called McBel-Plnc, based on the convolutional neural network (CNN) and long-short term memory (LSTM) using each of the five datasets randomly generated from the prepared data. Based on macro average, the test results showed the high precision of 0.9151 ± 0.0038 averaged from the five models with the lower recall of 0.5786 ± 0.0208. The small standard deviations confirmed the model stability. Comparing with iDeepE with a binary relevance method, iDeepE got the higher recall with the significantly lower precision (0.6912 and 0.1987, respectively). This result suggested that our model is competent to predict the protein-lncRNA interactions, especially with the lncRNAs targeted by multiple proteins. This suggested the potential to infer the insights of lncRNA functions and molecular mechanisms.
{"title":"McBel-Plnc: A Deep Learning Model for Multiclass Multilabel Classification of Protein-lncRNA Interactions","authors":"Natsuda Navamajiti, Thammakorn Saethang, D. Wichadakul","doi":"10.1145/3375923.3375953","DOIUrl":"https://doi.org/10.1145/3375923.3375953","url":null,"abstract":"One main function of long non-coding RNAs (lncRNAs) is to act as a scaffold facilitating multiple proteins to form complexes. Most of available prediction models for protein-RNA interactions, however, were proposed as a binary classifier, which limited on predicting the interaction between the non-coding RNAs and each individual RNA-binding protein (RBP). Hence, to predict if a lncRNA is acting as a scaffold, we consider this problem as a multiclass multilabel classification problem. To solve this problem, the high confident CLIP-seq data were selected from the POSTAR2 database with an augmentation of the data for the RBP classes with a small number of interacting lncRNAs. We then constructed a deep learning model for multiclass multilabel classification, called McBel-Plnc, based on the convolutional neural network (CNN) and long-short term memory (LSTM) using each of the five datasets randomly generated from the prepared data. Based on macro average, the test results showed the high precision of 0.9151 ± 0.0038 averaged from the five models with the lower recall of 0.5786 ± 0.0208. The small standard deviations confirmed the model stability. Comparing with iDeepE with a binary relevance method, iDeepE got the higher recall with the significantly lower precision (0.6912 and 0.1987, respectively). This result suggested that our model is competent to predict the protein-lncRNA interactions, especially with the lncRNAs targeted by multiple proteins. This suggested the potential to infer the insights of lncRNA functions and molecular mechanisms.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90296863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this research, topical lidocaine solution for ocular administration was assessed for biocompatibility as a non-invasive anesthetic drug delivery. The study aimed to investigate the cytotoxicity against human corneal epithelial cells (HCECs) and to evaluate drug permeation. In the case of cytotoxicity, HCECs were treated with drug solution, analyzed for percent viability. For permeation study, the modified-franz diffusion method was used to study permeation partition coefficient of lidocaine solution; moreover, the drug retained on the sclera was also determined. HCECs were treated with lidocaine solutions with the concentration range of 0.781 -100 g/L. Significantly decrease in cell viability with the concentration above 12 g/L was detected by Resazurin metabolic rate assay. The permeation coefficient of lidocaine hydrochloride solution could not be determined because of drug absence in the receptor chamber. The entire drug loaded remained in the donor chamber and adsorbed on the surface of sclera tissue. The results suggested that topical lidocaine solution showed reasonably safe and lidocaine drops did not absorbed through the sclera. In present study, local topical anesthetic delivery of lidocaine was considered safe for ophthalmologic treatment.
{"title":"In Vitro Safety Assessment and Permeation Study of Topical Lidocaine Solution for Ocular Administration","authors":"Sirikool Thamnium, V. Panapisal, J. Luckanagul","doi":"10.1145/3375923.3375949","DOIUrl":"https://doi.org/10.1145/3375923.3375949","url":null,"abstract":"In this research, topical lidocaine solution for ocular administration was assessed for biocompatibility as a non-invasive anesthetic drug delivery. The study aimed to investigate the cytotoxicity against human corneal epithelial cells (HCECs) and to evaluate drug permeation. In the case of cytotoxicity, HCECs were treated with drug solution, analyzed for percent viability. For permeation study, the modified-franz diffusion method was used to study permeation partition coefficient of lidocaine solution; moreover, the drug retained on the sclera was also determined. HCECs were treated with lidocaine solutions with the concentration range of 0.781 -100 g/L. Significantly decrease in cell viability with the concentration above 12 g/L was detected by Resazurin metabolic rate assay. The permeation coefficient of lidocaine hydrochloride solution could not be determined because of drug absence in the receptor chamber. The entire drug loaded remained in the donor chamber and adsorbed on the surface of sclera tissue. The results suggested that topical lidocaine solution showed reasonably safe and lidocaine drops did not absorbed through the sclera. In present study, local topical anesthetic delivery of lidocaine was considered safe for ophthalmologic treatment.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75171335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Tharmakulasingam, Cihan Topal, Warnakulasuriya Anil Chandana Fernando, R. M. Ragione
The timely identification of pathogens is vital in order to effectively control diseases and avoid antimicrobial resistance. Non-invasive point-of-care diagnostic tools are recently trending in identification of the pathogens and becoming a helpful tool especially for rural areas. Machine learning approaches have been widely applied on biological markers for predicting diseases and pathogens. However, there are few studies in the literature that have utilized volatile organic compounds (VOCs) as non-invasive biological markers to identify bacterial pathogens. Furthermore, there is no comprehensive study investigating the effect of different distance and similarity metrics for pathogen classification based on VOC data. In this study, we compared various non-Euclidean distance and similarity metrics with Euclidean metric to identify significantly contributing VOCs to predict pathogens. In addition, we also utilized backward feature elimination (BFE) method to accurately select the best set of features. The dataset we utilized for experiments was composed from the publications published between 1977 and 2016, and consisted of associations in between 703 VOCs and 11 pathogens.We performed extensive set of experiments with five different distance metrics in both uniform and weighted manner. Comprehensive experiments showed that it is possible to correctly predict pathogens by using 68 VOCs among 703 with 78.6% accuracy using k-nearest neighbour classifier and Sorensen distance metric.
{"title":"Improved Pathogen Recognition using Non-Euclidean Distance Metrics andWeighted kNN","authors":"M. Tharmakulasingam, Cihan Topal, Warnakulasuriya Anil Chandana Fernando, R. M. Ragione","doi":"10.1145/3375923.3375956","DOIUrl":"https://doi.org/10.1145/3375923.3375956","url":null,"abstract":"The timely identification of pathogens is vital in order to effectively control diseases and avoid antimicrobial resistance. Non-invasive point-of-care diagnostic tools are recently trending in identification of the pathogens and becoming a helpful tool especially for rural areas. Machine learning approaches have been widely applied on biological markers for predicting diseases and pathogens. However, there are few studies in the literature that have utilized volatile organic compounds (VOCs) as non-invasive biological markers to identify bacterial pathogens. Furthermore, there is no comprehensive study investigating the effect of different distance and similarity metrics for pathogen classification based on VOC data. In this study, we compared various non-Euclidean distance and similarity metrics with Euclidean metric to identify significantly contributing VOCs to predict pathogens. In addition, we also utilized backward feature elimination (BFE) method to accurately select the best set of features. The dataset we utilized for experiments was composed from the publications published between 1977 and 2016, and consisted of associations in between 703 VOCs and 11 pathogens.We performed extensive set of experiments with five different distance metrics in both uniform and weighted manner. Comprehensive experiments showed that it is possible to correctly predict pathogens by using 68 VOCs among 703 with 78.6% accuracy using k-nearest neighbour classifier and Sorensen distance metric.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85801831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Determining the subcellular localization of viral proteins is indispensable for understanding the activity of the virus and inferring viral protein functions. Although previous studies about predicting viral protein subcellular localization have been developed, they often have the following disadvantages: (i) only focusing on a part of proteins of a species (ii) not considering the presence of multi-location proteins and (iii) lacking interpretability for the results. To address these problems, this paper is firstly predicting all the subcellular localization of the whole viral proteome in the UniProtKB and is interpretable for the results. This paper gives high prediction accuracy for the single-location and multi-location viral proteins by the FUEL-mLoc predictor. More importantly, we did deeply analysis and interpretation of the subcellular localization of all viral proteins. Finally, we have found some essential GO terms which are interpretable for the results and are significant in predicting the subcellular localization of the viral proteins.
{"title":"Comprehensive Prediction and Interpretation of Viral Protein Subcellular Localization","authors":"Xiyu Liu","doi":"10.1145/3375923.3375950","DOIUrl":"https://doi.org/10.1145/3375923.3375950","url":null,"abstract":"Determining the subcellular localization of viral proteins is indispensable for understanding the activity of the virus and inferring viral protein functions. Although previous studies about predicting viral protein subcellular localization have been developed, they often have the following disadvantages: (i) only focusing on a part of proteins of a species (ii) not considering the presence of multi-location proteins and (iii) lacking interpretability for the results. To address these problems, this paper is firstly predicting all the subcellular localization of the whole viral proteome in the UniProtKB and is interpretable for the results. This paper gives high prediction accuracy for the single-location and multi-location viral proteins by the FUEL-mLoc predictor. More importantly, we did deeply analysis and interpretation of the subcellular localization of all viral proteins. Finally, we have found some essential GO terms which are interpretable for the results and are significant in predicting the subcellular localization of the viral proteins.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88336563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Voon, Joachim Engan Sigau, Joshua E.H. Voon, Grace E. H. Voon
In this paper, the roles of attitudinal factors and fish were investigated to explore their relationships with the health performance of individuals. Specifically, the variables included were: Health Orientation (HO), Diet Orientation (DO), Life Satisfaction (LS), Attitude toward Fish (ATF), Fish Consumption (FC) Demographic variables and Health Performance (HP). A total of 300 respondents participated in the structured questionnaire survey. The data analyses included multi-item scale reliability, Chi-square, means, t-Test, ANOVA and multiple regression analyses. The influences of the various socio-demographic variables on attitudes and personal health were investigated accordingly. The attitudinal factors (i.e., Attitudes towards Health and Eating, Life Satisfaction and Attitudes towards Fish) had shown significant positive relationships with the personal health performance of individuals. The results suggested that the human factor engineering in biomedical sciences is essential. The attitudinal factors are potential determinants for personal health and should be managed effectively and efficiently.
{"title":"Attitudinal Factors for Personal Health","authors":"B. Voon, Joachim Engan Sigau, Joshua E.H. Voon, Grace E. H. Voon","doi":"10.1145/3375923.3375939","DOIUrl":"https://doi.org/10.1145/3375923.3375939","url":null,"abstract":"In this paper, the roles of attitudinal factors and fish were investigated to explore their relationships with the health performance of individuals. Specifically, the variables included were: Health Orientation (HO), Diet Orientation (DO), Life Satisfaction (LS), Attitude toward Fish (ATF), Fish Consumption (FC) Demographic variables and Health Performance (HP). A total of 300 respondents participated in the structured questionnaire survey. The data analyses included multi-item scale reliability, Chi-square, means, t-Test, ANOVA and multiple regression analyses. The influences of the various socio-demographic variables on attitudes and personal health were investigated accordingly. The attitudinal factors (i.e., Attitudes towards Health and Eating, Life Satisfaction and Attitudes towards Fish) had shown significant positive relationships with the personal health performance of individuals. The results suggested that the human factor engineering in biomedical sciences is essential. The attitudinal factors are potential determinants for personal health and should be managed effectively and efficiently.","PeriodicalId":20457,"journal":{"name":"Proceedings of the 2019 6th International Conference on Biomedical and Bioinformatics Engineering","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87570382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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