Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962787
Ruojun Li, E. Agu, G. Balakrishnan, D. Herman, Ana M. Abrantes, Michael Stein, Jane Metrik
The use of marijuana is now legal for medical purposes in 39 of the 50 United States. Eleven of these 39 states have also legalized marijuana for non-medical usage. Marijuana impairs the motor skills of users, making Driving Under the Influence of Marijuana (DUIM) a growing public health concern. There are currently few accessible and accurate methods to assess the impairment levels of drivers who have used marijuana. Current assessment methods include self-reports and testing urine, oral fluid, and blood. However, self-reports are often biased and biological tests are cumbersome to perform in situ. In this paper, we investigate whether dose-dependent changes in participants gait (walk) can be detected using data gathered from their smartphone motion sensors (accelerometer and gyroscope). We envision WeedGait, a smartphone sensing system that will assess the gait of marijuana users passively and warn them when they are too impaired to drive safely. To the best of our knowledge, this is the first study on using smartphones to assess marijuana-induced gait impairment. Gait data was collected from 10 subjects and pre-processing steps included low pass filtering, step cycle detection and segmentation, and normalization. We present a novel gait analysis approach that analyzes normalized, single-step segments to achieve higher accuracy than prior approaches. We compared the classification results of various machine and deep learning models, and found that Long Short Time Memory (LSTM) and Support Vector Machines performed best, discriminating the gait of subjects after smoking either marijuana with 3% or 7.2% THC versus smoking a placebo marijuana cigarette with an accuracy of 92.1%. These results suggest that smartphone-based marijuana testing is more accurate than urine-based tests but slightly less accurate than oral fluid based testing. Moreover, smartphone sensing of marijuana is completely passive and hence more convenient, which facilitates pervasive testing in natural settings and could have massive impact due to the near-ubiquity of smartphones.
{"title":"WeedGait: Unobtrusive Smartphone Sensing of Marijuana-Induced Gait impairment By Fusing Gait Cycle Segmentation and Neural Networks","authors":"Ruojun Li, E. Agu, G. Balakrishnan, D. Herman, Ana M. Abrantes, Michael Stein, Jane Metrik","doi":"10.1109/HI-POCT45284.2019.8962787","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962787","url":null,"abstract":"The use of marijuana is now legal for medical purposes in 39 of the 50 United States. Eleven of these 39 states have also legalized marijuana for non-medical usage. Marijuana impairs the motor skills of users, making Driving Under the Influence of Marijuana (DUIM) a growing public health concern. There are currently few accessible and accurate methods to assess the impairment levels of drivers who have used marijuana. Current assessment methods include self-reports and testing urine, oral fluid, and blood. However, self-reports are often biased and biological tests are cumbersome to perform in situ. In this paper, we investigate whether dose-dependent changes in participants gait (walk) can be detected using data gathered from their smartphone motion sensors (accelerometer and gyroscope). We envision WeedGait, a smartphone sensing system that will assess the gait of marijuana users passively and warn them when they are too impaired to drive safely. To the best of our knowledge, this is the first study on using smartphones to assess marijuana-induced gait impairment. Gait data was collected from 10 subjects and pre-processing steps included low pass filtering, step cycle detection and segmentation, and normalization. We present a novel gait analysis approach that analyzes normalized, single-step segments to achieve higher accuracy than prior approaches. We compared the classification results of various machine and deep learning models, and found that Long Short Time Memory (LSTM) and Support Vector Machines performed best, discriminating the gait of subjects after smoking either marijuana with 3% or 7.2% THC versus smoking a placebo marijuana cigarette with an accuracy of 92.1%. These results suggest that smartphone-based marijuana testing is more accurate than urine-based tests but slightly less accurate than oral fluid based testing. Moreover, smartphone sensing of marijuana is completely passive and hence more convenient, which facilitates pervasive testing in natural settings and could have massive impact due to the near-ubiquity of smartphones.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128639399","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}
Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962891
D. S. Wickramasuriya, Mikayla K. Tessmer, R. Faghih
Automated emotion recognition from physiological signals is an ongoing research area. Many studies rely on self-reported emotion scores from subjects to generate classification labels. This can introduce labeling inconsistencies due to inter-subject variability. Facial expressions provide a more consistent means of generating labels. We generate labels by selecting locations at which subjects either displayed a visibly averse/negative reaction or laughed in video recordings. We next use a supervised learning approach for classifying these emotional responses based on electrocardiogram (EKG) and respiration signal features in an experiment where different movie/video clips were utilized to elicit feelings of joy, disgust, amusement, etc. As features, we extract wavelet coefficient patches from EKG RR-interval time series and respiration waveform parameters. We use principal component analysis for dimensionality reduction and support vector machines for classification. We achieved an overall classification accuracy of 78.3%.
{"title":"Facial Expression-Based Emotion Classification using Electrocardiogram and Respiration Signals","authors":"D. S. Wickramasuriya, Mikayla K. Tessmer, R. Faghih","doi":"10.1109/HI-POCT45284.2019.8962891","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962891","url":null,"abstract":"Automated emotion recognition from physiological signals is an ongoing research area. Many studies rely on self-reported emotion scores from subjects to generate classification labels. This can introduce labeling inconsistencies due to inter-subject variability. Facial expressions provide a more consistent means of generating labels. We generate labels by selecting locations at which subjects either displayed a visibly averse/negative reaction or laughed in video recordings. We next use a supervised learning approach for classifying these emotional responses based on electrocardiogram (EKG) and respiration signal features in an experiment where different movie/video clips were utilized to elicit feelings of joy, disgust, amusement, etc. As features, we extract wavelet coefficient patches from EKG RR-interval time series and respiration waveform parameters. We use principal component analysis for dimensionality reduction and support vector machines for classification. We achieved an overall classification accuracy of 78.3%.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123114624","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}
Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962647
K. Kolli, S. H. Park, J. Min, H. Chang, D. Han, H. Gransar, J. Lee, Su-Yeon Choi, E. Chun, H. Jung, J. Sung, H. Han
Coronary artery calcium (CAC) is an established surrogate marker for coronary atherosclerotic disease (CAD) burden. The CAC score is also an independent predictor of adverse events with significant incremental prognostic value over traditional/clinical risk stratification algorithms. The objective of this study was to examine the prognostic ability of Machine learning (ML) based algorithms to predict multi-class CAC (0: normal; 1–100: low risk CAD; 101–400 Intermediate risk CAD; >400 severe/high risk CAD) from available electronic health record (EHR) data. A retrospective observation study of 60,923 asymptomatic patients with clinically evaluated CAC score along with sixty five clinical and laboratory parameters were included in developing the ML algorithm (data split into 70% [training] and 30% [test]). In addition, a separate cohort of 7,552 patients was used to externally validate the developed ML algorithm. Classification performance was assessed using the area under the receiver operating curve (AUC). The prediction algorithm derived from the ML method showed high predictive value for CAC risk category.
{"title":"Machine learning algorithm to predict coronary artery calcification in asymptomatic healthy population","authors":"K. Kolli, S. H. Park, J. Min, H. Chang, D. Han, H. Gransar, J. Lee, Su-Yeon Choi, E. Chun, H. Jung, J. Sung, H. Han","doi":"10.1109/HI-POCT45284.2019.8962647","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962647","url":null,"abstract":"Coronary artery calcium (CAC) is an established surrogate marker for coronary atherosclerotic disease (CAD) burden. The CAC score is also an independent predictor of adverse events with significant incremental prognostic value over traditional/clinical risk stratification algorithms. The objective of this study was to examine the prognostic ability of Machine learning (ML) based algorithms to predict multi-class CAC (0: normal; 1–100: low risk CAD; 101–400 Intermediate risk CAD; >400 severe/high risk CAD) from available electronic health record (EHR) data. A retrospective observation study of 60,923 asymptomatic patients with clinically evaluated CAC score along with sixty five clinical and laboratory parameters were included in developing the ML algorithm (data split into 70% [training] and 30% [test]). In addition, a separate cohort of 7,552 patients was used to externally validate the developed ML algorithm. Classification performance was assessed using the area under the receiver operating curve (AUC). The prediction algorithm derived from the ML method showed high predictive value for CAC risk category.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132783203","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}
Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962888
Lei Lin, Beilei Xu, Wencheng Wu, Trevor W. Richardson, Edgar A. Bernal, Bill Martens, C. Thornton, C. Heatwole
Myotonia, which refers to delayed muscle relaxation after contraction, is the main symptom of myotonic dystrophy patients. The relaxation time after a hand squeeze has been used as a biomarker for diagnostic purposes and in clinical trials to quantify the effectiveness of a treatment. Current processes that rely on handcrafted features tend to be sensitive to data acquisition noise and intra- and inter-patient variability. In this work, we develop a deep metric learning framework for analyzing the hand-grip time series based on triplet-networks. Experiments show that the learned embedding space can be used to quantify the symptoms, evaluate the effectiveness of treatments, and design new data collection protocols.
{"title":"Deep Metric Learning with Triplet Networks: Application to Hand-grip Myotonia Quantification","authors":"Lei Lin, Beilei Xu, Wencheng Wu, Trevor W. Richardson, Edgar A. Bernal, Bill Martens, C. Thornton, C. Heatwole","doi":"10.1109/HI-POCT45284.2019.8962888","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962888","url":null,"abstract":"Myotonia, which refers to delayed muscle relaxation after contraction, is the main symptom of myotonic dystrophy patients. The relaxation time after a hand squeeze has been used as a biomarker for diagnostic purposes and in clinical trials to quantify the effectiveness of a treatment. Current processes that rely on handcrafted features tend to be sensitive to data acquisition noise and intra- and inter-patient variability. In this work, we develop a deep metric learning framework for analyzing the hand-grip time series based on triplet-networks. Experiments show that the learned embedding space can be used to quantify the symptoms, evaluate the effectiveness of treatments, and design new data collection protocols.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128727486","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}
Pub Date : 2019-11-01DOI: 10.1109/hi-poct45284.2019.8962894
{"title":"HI-POCT 2019 EMBS Information","authors":"","doi":"10.1109/hi-poct45284.2019.8962894","DOIUrl":"https://doi.org/10.1109/hi-poct45284.2019.8962894","url":null,"abstract":"","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125421689","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}
Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962692
Corey Norton, Kurt Wagner, U. Hassan
A novel framework to quantify the phagocytic ability of a septic patient’s immune system is proposed for Point-of-Care (PoC) diagnostic applications. The design utilizes biofunctionalized ferromagnetic particles to affect the flow rate of phagocytes passing through an impedimetric sensor. The electrical, microfluidic, and magnetic subsystems of the design are analyzed. Preliminary simulation and experimental results demonstrate the feasibility of the system. Additionally, fabrication procedures and system calibrations are discussed, and a control assay is proposed.
{"title":"Magnetic Phagocyte Quantification Framework for Point-of-Care Diagnostics","authors":"Corey Norton, Kurt Wagner, U. Hassan","doi":"10.1109/HI-POCT45284.2019.8962692","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962692","url":null,"abstract":"A novel framework to quantify the phagocytic ability of a septic patient’s immune system is proposed for Point-of-Care (PoC) diagnostic applications. The design utilizes biofunctionalized ferromagnetic particles to affect the flow rate of phagocytes passing through an impedimetric sensor. The electrical, microfluidic, and magnetic subsystems of the design are analyzed. Preliminary simulation and experimental results demonstrate the feasibility of the system. Additionally, fabrication procedures and system calibrations are discussed, and a control assay is proposed.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133680244","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}
Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962846
Shreya Prakash, Maxwell B. Nagarajan, P. Doyle, R. Bashir, U. Hassan
Multiplexing is a method of analyzing multiple analytes in a biological assay in a single step. Multiplexing provides advantages of sample sparring, shorter time to result and reduce tests cost. To achieve multiplexing we have used barcoded particles which were fabricated by a Stop Flow Lithography process in a microfluidic environment. Here, we present a microfluidic system for electrical differentiation of barcoded particles and its sensitivity to enumerate blood cells. The barcoded particles conjugated with different sized microspheres simulating blood cells generated distinct electrical signatures when passed through a microfluidic coulter counter, highlighting its ability for multiplexed analyte quantification. Such multiplexing system can be used for detecting multiple diagnostics and prognostic biomarkers in diseases like Sepsis, Acute Kidney Injury, and AIDS diagnostic and management.
{"title":"Conjugated Barcoded Particles for Multiplexed Biomarker Quantification with a Microfluidic Biochip","authors":"Shreya Prakash, Maxwell B. Nagarajan, P. Doyle, R. Bashir, U. Hassan","doi":"10.1109/HI-POCT45284.2019.8962846","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962846","url":null,"abstract":"Multiplexing is a method of analyzing multiple analytes in a biological assay in a single step. Multiplexing provides advantages of sample sparring, shorter time to result and reduce tests cost. To achieve multiplexing we have used barcoded particles which were fabricated by a Stop Flow Lithography process in a microfluidic environment. Here, we present a microfluidic system for electrical differentiation of barcoded particles and its sensitivity to enumerate blood cells. The barcoded particles conjugated with different sized microspheres simulating blood cells generated distinct electrical signatures when passed through a microfluidic coulter counter, highlighting its ability for multiplexed analyte quantification. Such multiplexing system can be used for detecting multiple diagnostics and prognostic biomarkers in diseases like Sepsis, Acute Kidney Injury, and AIDS diagnostic and management.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116106705","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}
Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962702
Nipun Thamatam, J. Christen
Lateral Flow Immunoassays (LFIAs) are among the most successful Point of Care (POC) tests. However, factors like reagent stability, reaction rates, and binding kinetics limit the performance and robustness of LFIAs. One of the factors that affects the overall performance of LFIA is the fluid flow rate, and hence, it is desirable to maintain a predictable fluid velocity in porous media. The main objective of this study is to build a statistical model that estimates the fluid velocity in porous media for any given ambient condition to enable us to determine the optimal design parameters for achieving a desired fluid velocity in porous media.
{"title":"Effects of relative humidity, temperature, and geometry on fluid flow rate in lateral flow immunoassays","authors":"Nipun Thamatam, J. Christen","doi":"10.1109/HI-POCT45284.2019.8962702","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962702","url":null,"abstract":"Lateral Flow Immunoassays (LFIAs) are among the most successful Point of Care (POC) tests. However, factors like reagent stability, reaction rates, and binding kinetics limit the performance and robustness of LFIAs. One of the factors that affects the overall performance of LFIA is the fluid flow rate, and hence, it is desirable to maintain a predictable fluid velocity in porous media. The main objective of this study is to build a statistical model that estimates the fluid velocity in porous media for any given ambient condition to enable us to determine the optimal design parameters for achieving a desired fluid velocity in porous media.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133234261","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}
Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962889
Brittany Hertneky, J. Eger, Mark S. Bailly, J. Christen
Point-of-care (PoC) testing systems aim to bring affordable and convenient diagnostics to resource limited locations. In our previous work in detecting human papilloma virus (HPV) via lateral flow immunoassays and fluorescence detection, we determined that the performance of the assay depends on the temperature and humidity. Thus, we need to maintain a fixed environment for the assay to produce reliable results. Therefore, we define the need for a portable, climate-controlled chamber for field work in low resource settings. By combining low-cost electronics and household items, a simple feedback loop is designed to regulate the internal conditions of the testing environment. The ability of our chamber to maintain a desired climate will be tested for accuracy and stability to ensure that it is competent for in-field usage.
{"title":"Mobile and Efficient Temperature and Humidity Control Chamber for Point-of-Care Diagnostics","authors":"Brittany Hertneky, J. Eger, Mark S. Bailly, J. Christen","doi":"10.1109/HI-POCT45284.2019.8962889","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962889","url":null,"abstract":"Point-of-care (PoC) testing systems aim to bring affordable and convenient diagnostics to resource limited locations. In our previous work in detecting human papilloma virus (HPV) via lateral flow immunoassays and fluorescence detection, we determined that the performance of the assay depends on the temperature and humidity. Thus, we need to maintain a fixed environment for the assay to produce reliable results. Therefore, we define the need for a portable, climate-controlled chamber for field work in low resource settings. By combining low-cost electronics and household items, a simple feedback loop is designed to regulate the internal conditions of the testing environment. The ability of our chamber to maintain a desired climate will be tested for accuracy and stability to ensure that it is competent for in-field usage.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123012427","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}
Pub Date : 2019-11-01DOI: 10.1109/HI-POCT45284.2019.8962720
B. Moatamed, Sajad Darabi, M. Sarrafzadeh
Athletic performance is multifaceted, and it is affected by a wide range of factors. Athletes and coaches are interested in collecting as much data as possible to provide insight into performance and training effectiveness. However, it can be difficult for athletes to identify a relationship between these factors and their performance, and even more difficult for a coach who may be responsible for monitoring dozens of metrics in dozens of athletes. Here we outline an approach for condensing a range of wellness factors into a single score, as well as a method for condensing jump height consistency and improvement into a separate performance score. These scoring systems allow for wellness and performance to be evaluated at a glance, allowing for early intervention to reduce injury, an understanding of performance, and effective training.
{"title":"Scoring System for Conditioning and Wellness Assessment in Athletic Population","authors":"B. Moatamed, Sajad Darabi, M. Sarrafzadeh","doi":"10.1109/HI-POCT45284.2019.8962720","DOIUrl":"https://doi.org/10.1109/HI-POCT45284.2019.8962720","url":null,"abstract":"Athletic performance is multifaceted, and it is affected by a wide range of factors. Athletes and coaches are interested in collecting as much data as possible to provide insight into performance and training effectiveness. However, it can be difficult for athletes to identify a relationship between these factors and their performance, and even more difficult for a coach who may be responsible for monitoring dozens of metrics in dozens of athletes. Here we outline an approach for condensing a range of wellness factors into a single score, as well as a method for condensing jump height consistency and improvement into a separate performance score. These scoring systems allow for wellness and performance to be evaluated at a glance, allowing for early intervention to reduce injury, an understanding of performance, and effective training.","PeriodicalId":269346,"journal":{"name":"2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131663471","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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