scholarly journals Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2845 ◽  
Author(s):  
Michael B. Del Del Rosario ◽  
Nigel H. Lovell ◽  
Stephen J. Redmond

Features were developed which accounted for the changing orientation of the inertial measurement unit (IMU) relative to the body, and demonstrably improved the performance of models for human activity recognition (HAR). The method is proficient at separating periods of standing and sedentary activity (i.e., sitting and/or lying) using only one IMU, even if it is arbitrarily oriented or subsequently re-oriented relative to the body; since the body is upright during walking, learning the IMU orientation during walking provides a reference orientation against which sitting and/or lying can be inferred. Thus, the two activities can be identified (irrespective of the cohort) by analyzing the magnitude of the angle of shortest rotation which would be required to bring the upright direction into coincidence with the average orientation from the most recent 2.5 s of IMU data. Models for HAR were trained using data obtained from a cohort of 37 older adults (83.9 ± 3.4 years) or 20 younger adults (21.9 ± 1.7 years). Test data were generated from the training data by virtually re-orienting the IMU so that it is representative of carrying the phone in five different orientations (relative to the thigh). The overall performance of the model for HAR was consistent whether the model was trained with the data from the younger cohort, and tested with the data from the older cohort after it had been virtually re-oriented (Cohen’s Kappa 95% confidence interval [0.782, 0.793]; total class sensitivity 95% confidence interval [84.9%, 85.6%]), or the reciprocal scenario in which the model was trained with the data from the older cohort, and tested with the data from the younger cohort after it had been virtually re-oriented (Cohen’s Kappa 95% confidence interval [0.765, 0.784]; total class sensitivity 95% confidence interval [82.3%, 83.7%]).

2021 ◽  
Vol 11 (6) ◽  
pp. 2723
Author(s):  
Fatih Uysal ◽  
Fırat Hardalaç ◽  
Ozan Peker ◽  
Tolga Tolunay ◽  
Nil Tokgöz

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.


Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2832 ◽  
Author(s):  
Juyoung Lee ◽  
Sang Chul Ahn ◽  
Jae-In Hwang

People are interested in traveling in an infinite virtual environment, but no standard navigation method exists yet in Virtual Reality (VR). The Walking-In-Place (WIP) technique is a navigation method that simulates movement to enable immersive travel with less simulator sickness in VR. However, attaching the sensor to the body is troublesome. A previously introduced method that performed WIP using an Inertial Measurement Unit (IMU) helped address this problem. That method does not require placement of additional sensors on the body. That study proved, through evaluation, the acceptable performance of WIP. However, this method has limitations, including a high step-recognition rate when the user does various body motions within the tracking area. Previous works also did not evaluate WIP step recognition accuracy. In this paper, we propose a novel WIP method using position and orientation tracking, which are provided in the most PC-based VR HMDs. Our method also does not require additional sensors on the body and is more stable than the IMU-based method for non-WIP motions. We evaluated our method with nine subjects and found that the WIP step accuracy was 99.32% regardless of head tilt, and the error rate was 0% for squat motion, which is a motion prone to error. We distinguish jog-in-place as “intentional motion” and others as “unintentional motion”. This shows that our method correctly recognizes only jog-in-place. We also apply the saw-tooth function virtual velocity to our method in a mathematical way. Natural navigation is possible when the virtual velocity approach is applied to the WIP method. Our method is useful for various applications which requires jogging.


2019 ◽  
Vol 47 (1) ◽  
pp. 162-169
Author(s):  
Yendelela L. Cuffee ◽  
Lee Hargraves ◽  
Milagros Rosal ◽  
Becky A. Briesacher ◽  
Jeroan J. Allison ◽  
...  

Background. John Henryism is defined as a measure of active coping in response to stressful experiences. John Henryism has been linked with health conditions such as diabetes, prostate cancer, and hypertension, but rarely with health behaviors. Aims. We hypothesized that reporting higher scores on the John Henryism Scale may be associated with poorer medication adherence, and trust in providers may mediate this relationship. Method. We tested this hypothesis using data from the TRUST study. The TRUST study included 787 African Americans with hypertension receiving care at a safety-net hospital. Ordinal logistic regression was used to examine the relationship between John Henryism and medication adherence. Results. Within our sample of African Americans with hypertension, lower John Henryism scores was associated with poorer self-reported adherence (low, 20.62; moderate, 19.19; high, 18.12; p < .001). Higher John Henryism scores were associated with lower trust scores (low John Henryism: 40.1; high John Henryism: 37.9; p < .001). In the adjusted model, each 1-point increase in the John Henryism score decreased the odds of being in a better cumulative medication adherence category by a factor of 4% (odds ratio = 0.96, p = .014, 95% confidence interval = 0.93-0.99). Twenty percent of the association between medication adherence and John Henryism was mediated by trust (standard deviation = 0.205, 95% confidence interval = 0.074-0.335). Discussion. This study provides important insights into the complex relationship between psychological responses and health behaviors. It also contributes to the body of literature examining the construct of John Henryism among African Americans with hypertension. Conclusion. The findings of this study support the need for interventions that promote healthful coping strategies and patient–provider trust.


2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S297-S297
Author(s):  
Gabrielle Gussin ◽  
Raveena Singh ◽  
Izabela Coimbra Ibraim ◽  
Raheeb Saavedra ◽  
Thomas Tjoa ◽  
...  

Abstract Background Federal mandate requires NHs to perform weekly COVID-19 testing of staff. Testing is effective due to barriers to disclosing mild illness, but it is unclear how long the mandate will last. We explored if environmental samples can be used to signal staff COVID-19 cases as an alternative screening tool in NHs. Methods We conducted a cross sectional study to assess the value of environmental sampling as a trigger for COVID-19 testing of NH staff using data from currently performed weekly staff sweeps. We performed 35 sampling sweeps across 21 NHs from 6/2020-2/2021. For each sweep, we sampled up to 24 high touch objects in NH breakrooms (N=226), entryways (N=216), and nursing stations (N=194) assuming that positive samples were due to contamination from infected staff. Total staff and positive staff counts were tallied for the staff testing sweeps performed the week of and week prior to environmental sampling. Object samples were processed for SARS-CoV-2 using PCR (StepOnePlus) with a 1 copy/mL limit of detection. We evaluated concordance between object and staff positivity using Cohen’s kappa and calculated the positive and negative predictive value (PPV, NPV) of environmental sweeps for staff positivity, including the attributable capture of positive staff. We tested the association between the proportion of staff positivity and object contamination by room type in a linear regression model when clustering by NH. Results Among 35 environmental sweeps, 49% had SARS-CoV-2 positive objects and 69% had positive staff in the same or prior week. Mean positivity was 16% (range 0-83%) among objects and 4% (range 0-22%) among staff. Overall, NPV was 61% and Cohen’s kappa was 0.60. PPV of object sampling as an indicator of positive staff was 100% for every room type, with an attributable capture of positive staff of 76%, with values varying by room type (Table). Breakroom samples were the strongest indicator of any staff cases. Each percent increase in object positivity was associated with an increase in staff positivity in entryways (7.2% increased staff positivity, P=0.01) and nursing stations (5.7% increased staff positivity, P=0.05). Conclusion If mandatory weekly staff testing ends in NHs, environmental sampling may serve as an effective tool to trigger targeted COVID-19 testing sweeps of NH staff. Disclosures Gabrielle Gussin, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic products) Raveena Singh, MA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic products) Raheeb Saavedra, AS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic products) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)


2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Renata Baronaite ◽  
Merete Engelhart ◽  
Troels Mørk Hansen ◽  
Gorm Thamsborg ◽  
Hanne Slott Jensen ◽  
...  

Anti-nuclear antibodies (ANA) have traditionally been evaluated using indirect fluorescence assays (IFA) with HEp-2 cells. Quantitative immunoassays (EIA) have replaced the use of HEp-2 cells in some laboratories. Here, we evaluated ANA in 400 consecutive and unselected routinely referred patients using IFA and automated EIA techniques. The IFA results generated by two independent laboratories were compared with the EIA results from antibodies against double-stranded DNA (dsDNA), from ANA screening, and from tests of the seven included subantigens. The final IFA and EIA results for 386 unique patients were compared. The majority of the results were the same between the two methods (n=325, 84%); however, 8% (n=30) yielded equivocal results (equivocal-negative and equivocal-positive) and 8% (n=31) yielded divergent results (positive-negative). The results showed fairly good agreement, with Cohen’s kappa value of 0.30 (95% confidence interval (CI) = 0.14–0.46), which decreased to 0.23 (95% CI = 0.06–0.40) when the results for dsDNA were omitted. The EIA method was less reliable for assessing nuclear and speckled reactivity patterns, whereas the IFA method presented difficulties detecting dsDNA and Ro activity. The automated EIA method was performed in a similar way to the conventional IFA method using HEp-2 cells; thus, automated EIA may be used as a screening test.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Minliang He ◽  
Xuming Wang ◽  
Yijun Zhao

AbstractMusculoskeletal disorders affect the locomotor system and are the leading contributor to disability worldwide. Patients suffer chronic pain and limitations in mobility, dexterity, and functional ability. Musculoskeletal (bone) X-ray is an essential tool in diagnosing the abnormalities. In recent years, deep learning algorithms have increasingly been applied in musculoskeletal radiology and have produced remarkable results. In our study, we introduce a new calibrated ensemble of deep learners for the task of identifying abnormal musculoskeletal radiographs. Our model leverages the strengths of three baseline deep neural networks (ConvNet, ResNet, and DenseNet), which are typically employed either directly or as the backbone architecture in the existing deep learning-based approaches in this domain. Experimental results based on the public MURA dataset demonstrate that our proposed model outperforms three individual models and a traditional ensemble learner, achieving an overall performance of (AUC: 0.93, Accuracy: 0.87, Precision: 0.93, Recall: 0.81, Cohen’s kappa: 0.74). The model also outperforms expert radiologists in three out of the seven upper extremity anatomical regions with a leading performance of (AUC: 0.97, Accuracy: 0.93, Precision: 0.90, Recall:0.97, Cohen’s kappa: 0.85) in the humerus region. We further apply the class activation map technique to highlight the areas essential to our model’s decision-making process. Given that the best radiologist performance is between 0.73 and 0.78 in Cohen’s kappa statistic, our study provides convincing results supporting the utility of a calibrated ensemble approach for assessing abnormalities in musculoskeletal X-rays.


2001 ◽  
Vol 26 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Christof Schuster

If two raters assign targets to categories, the ratings can be arranged in a two-dimensional contingency table. A model for the frequencies in such a contingency table is presented for which Cohen’s kappa is a parameter. The model is based on two assumptions. First, the joint classification probabilities for the raters satisfy symmetry; second, the ratio of observed agreement to chance agreement is constant across categories. The model is illustrated using data from a study of the psychobiology of depression.


Author(s):  
Miriam Athmann ◽  
Roya Bornhütter ◽  
Nicolaas Busscher ◽  
Paul Doesburg ◽  
Uwe Geier ◽  
...  

AbstractIn the image forming methods, copper chloride crystallization (CCCryst), capillary dynamolysis (CapDyn), and circular chromatography (CChrom), characteristic patterns emerge in response to different food extracts. These patterns reflect the resistance to decomposition as an aspect of resilience and are therefore used in product quality assessment complementary to chemical analyses. In the presented study, rocket lettuce from a field trial with different radiation intensities, nitrogen supply, biodynamic, organic and mineral fertilization, and with or without horn silica application was investigated with all three image forming methods. The main objective was to compare two different evaluation approaches, differing in the type of image forming method leading the evaluation, the amount of factors analyzed, and the deployed perceptual strategy: Firstly, image evaluation of samples from all four experimental factors simultaneously by two individual evaluators was based mainly on analyzing structural features in CapDyn (analytical perception). Secondly, a panel of eight evaluators applied a Gestalt evaluation imbued with a kinesthetic engagement of CCCryst patterns from either fertilization treatments or horn silica treatments, followed by a confirmatory analysis of individual structural features. With the analytical approach, samples from different radiation intensities and N supply levels were identified correctly in two out of two sample sets with groups of five samples per treatment each (Cohen’s kappa, p = 0.0079), and the two organic fertilizer treatments were differentiated from the mineral fertilizer treatment in eight out of eight sample sets with groups of three manure and two minerally fertilized samples each (Cohen’s kappa, p = 0.0048). With the panel approach based on Gestalt evaluation, biodynamic fertilization was differentiated from organic and mineral fertilization in two out of two exams with 16 comparisons each (Friedman test, p < 0.001), and samples with horn silica application were successfully identified in two out of two exams with 32 comparisons each (Friedman test, p < 0.001). Further research will show which properties of the food decisive for resistance to decomposition are reflected by analytical and Gestalt criteria, respectively, in CCCryst and CapDyn images.


Genetics ◽  
2000 ◽  
Vol 155 (1) ◽  
pp. 463-473
Author(s):  
Bruno Goffinet ◽  
Sophie Gerber

Abstract This article presents a method to combine QTL results from different independent analyses. This method provides a modified Akaike criterion that can be used to decide how many QTL are actually represented by the QTL detected in different experiments. This criterion is computed to choose between models with one, two, three, etc., QTL. Simulations are carried out to investigate the quality of the model obtained with this method in various situations. It appears that the method allows the length of the confidence interval of QTL location to be consistently reduced when there are only very few “actual” QTL locations. An application of the method is given using data from the maize database available online at http://www.agron.missouri.edu/.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandre Maciel-Guerra ◽  
Necati Esener ◽  
Katharina Giebel ◽  
Daniel Lea ◽  
Martin J. Green ◽  
...  

AbstractStreptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen’s kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen’s kappa to 92.2% and 84.1% respectively. A computational framework integrating protein–protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.


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