Fast and Robust Algorithm for Detecting Body Posture Using Wrist-Worn Accelerometers

2020 ◽  
Vol 3 (4) ◽  
pp. 285-293
Author(s):  
Marcin Straczkiewicz ◽  
Nancy W. Glynn ◽  
Vadim Zipunnikov ◽  
Jaroslaw Harezlak
Keyword(s):  
True Positive Rate ◽  
Community Dwelling ◽  
Body Posture ◽  
True Positive ◽  
Standing Posture ◽  
Free Living ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Positive Rate

Background: The increasing popularity of wrist-worn accelerometers introduces novel challenges to the research on physical activity and sedentary behavior. Estimation of body posture is one such challenge. Methods: The authors proposed an approach called SedUp to differentiate between sedentary (sitting/lying) and standing postures. SedUp is based on the logistic regression classifier, using the wrist elevation and the motion variability extracted from raw accelerometry data collected on the axis parallel to the forearm. The authors developed and tested our method on data from N = 45 community-dwelling older adults. All subjects wore ActiGraph GT3X+ accelerometers on the left and right wrist, and activPAL was placed on the thigh in the free-living environment for 7 days. ActivPAL provided ground truth about body posture. The authors reported SedUp’s classification accuracy for each wrist separately. Results: Using the data from the left wrist, SedUp estimated the standing posture with median true positive rate = 0.83 and median true negative rate = 0.91. Using the data from the right wrist, SedUp estimated the standing posture with median true positive rate = 0.86 and median true negative rate = 0.93. Conclusions: SedUp provides accurate classification of body posture using wrist-worn accelerometers. The separate validation for each wrist allows for the application of SedUp in a wide spectrum of free-living studies.

Mastoid, middle ear and inner ear analysis in CT scan – a possible contribution for the identification of remains

2020 ◽  
Vol 60 (2) ◽  
pp. 102-111
Author(s):  
Henrique Rodrigues ◽  
Rosa Ramos ◽  
Leoni Fagundes ◽  
Orlando Galego ◽  
David Navega ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Visual Assessment ◽  
True Positive Rate ◽  
Observer Agreement ◽  
True Positive ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Positive Rate

Objective We aimed to evaluate whether the internal structures of the human ear have anatomical characteristics that are sufficiently distinctive to contribute to human identification and use in a forensic context. Materials and methods After data anonymisation, a dataset containing temporal bone CT scans of 100 subjects was processed by a radiologist who was not involved in the study. Four reference images were selected for each subject. Of the original sample, 10 examinations were used for visual comparison, case by case, against the dataset of 100 patients. This visual assessment was performed independently by four observers, who evaluated the anatomical agreement using a Likert scale (1–5). Inter-observer agreement, true positive rate, positive predictive value, true negative rate, negative predictive value, false positive rate, false negative rate and positive likelihood ratio (LR+) were evaluated. Results Inter-observer agreement obtained an overall Cohen’s Kappa = 99.59%. True positive rate, positive predictive value, true negative rate and negative predictive value were all 100%. Conclusion Visual assessment of the mastoid examinations was shown to be a robust and reliable approach to identify unique osseous features and contribute to human identification. The statistical analysis indicates that regardless of the examiner’s background and training, the approach has a high degree of accuracy.

scholarly journals IDENTIFIKASI EMOSI BERDASARKAN ACTION UNIT MENGGUNAKAN METODE BÉZIER CURVE

SINERGI ◽  
2016 ◽  
Vol 20 (1) ◽  
pp. 74
Author(s):  
Puji Aswari ◽  
Nova Eka Diana
Keyword(s):  
True Positive Rate ◽  
True Positive ◽  
Coding System ◽  
Facial Action Coding System ◽  
True Negative ◽  
Action Unit ◽  
Facial Action ◽  
True Negative Rate ◽  
Negative Rate ◽  
Positive Rate

Ekspresi wajah menjadi bahasa yang universal. Bahkan perubahan ekspresi wajah dapat membantu pengambilan keputusan. Pada tahun 1972, Paul Ekman mengklasifikasikan emosi dasar manusia ke dalam enam jenis: senang, sedih, terkejut, marah, takut, dan jijik. Kemudian Ekman dan Wallace Friesen mengembangkan sebuah alat untuk mengukur pergerakan pada wajah yang disebut Facial Action Coding System (FACS). FACS menentukan ekspresi wajah berdasarkan pergerakan otot wajah, yang diistilahkan Action Unit (AU). Penelitian ini bertujuan untuk mengetahui emosi tertarik yang dialami seseorang berdasarkan AU yang telah ditentukan oleh Paul Ekman dengan cara membandingkan dua buah citra, yaitu citra wajah tanpa ekspresi dan citra wajah berekspresi. Hasil penelitian ini memperoleh sebuah aplikasi yang mampu mengidentifikasi emosi tertarik dengan akurasi sebesar 80%, True Positive Rate 80%, dan True Negative Rate 80%. Dengan adanya penelitian ini diharapkan dapat diketahui karakteristik action unit yang membentuk emosi tertarik, juga memberikan masukan bagi proses evaluasi belajar mengajar mata kuliah pemrograman.

scholarly journals The Validity of the Tool “statcheck” in Discovering Statistical Reporting Inconsistencies

2017 ◽  
Author(s):  
Michele B. Nuijten ◽  
Marcel A. L. M. van Assen ◽  
Chris Hubertus Joseph Hartgerink ◽  
Sacha Epskamp ◽  
Jelte M. Wicherts
Keyword(s):  
Multiple Testing ◽  
Degrees Of Freedom ◽  
R Package ◽  
True Positive Rate ◽  
P Value ◽  
True Positive ◽  
Test Statistic ◽  
True Negative ◽  
True Negative Rate ◽  
Positive Rate

The R package “statcheck” (Epskamp & Nuijten, 2016) is a tool to extract statistical results from articles and check whether the reported p-value matches the accompanying test statistic and degrees of freedom. A previous study showed high interrater reliabilities (between .76 and .89) between statcheck and manual coding of inconsistencies (.76 - .89; Nuijten, Hartgerink, Van Assen, Epskamp, & Wicherts, 2016). Here we present an additional, detailed study of the validity of statcheck. In Study 1, we calculated its sensitivity and specificity. We found that statcheck’s sensitivity (true positive rate) and specificity (true negative rate) were high: between 85.3% and 100%, and between 96.0% and 100%, respectively, depending on the assumptions and settings. The overall accuracy of statcheck ranged from 96.2% to 99.9%. In Study 2, we investigated statcheck’s ability to deal with statistical corrections for multiple testing or violations of assumptions in articles. We found that the prevalence of corrections for multiple testing or violations of assumptions in psychology was higher than we initially estimated in Nuijten et al. (2016). Although we found numerous reporting inconsistencies in results corrected for violations of the sphericity assumption, we demonstrate that inconsistencies associated with statistical corrections are not what is causing the high estimates of the prevalence of statistical reporting inconsistencies in psychology.

scholarly journals Online asynchronous decoding of error-related potentials during the continuous control of a robot

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Catarina Lopes-Dias ◽  
Andreea I. Sburlea ◽  
Gernot R. Müller-Putz
Keyword(s):  
True Positive Rate ◽  
Robotic Arm ◽  
Continuous Control ◽  
True Positive ◽  
True Negative ◽  
Computer Interfaces ◽  
True Negative Rate ◽  
Positive Rate ◽  
Related Potentials ◽  
Neural Signature

AbstractError-related potentials (ErrPs) are the neural signature of error processing. Therefore, the detection of ErrPs is an intuitive approach to improve the performance of brain-computer interfaces (BCIs). The incorporation of ErrPs in discrete BCIs is well established but the study of asynchronous detection of ErrPs is still in its early stages. Here we show the feasibility of asynchronously decoding ErrPs in an online scenario. For that, we measured EEG in 15 participants while they controlled a robotic arm towards a target using their right hand. In 30% of the trials, the control of the robotic arm was halted at an unexpected moment (error onset) in order to trigger error-related potentials. When an ErrP was detected after the error onset, participants regained the control of the robot and could finish the trial. Regarding the asynchronous classification in the online scenario, we obtained an average true positive rate (TPR) of 70% and an average true negative rate (TNR) of 86.8%. These results indicate that the online asynchronous decoding of ErrPs was, on average, reliable, showing the feasibility of the asynchronous decoding of ErrPs in an online scenario.

scholarly journals Logistic versus linear regression-based Reliable Change Index: implications for clinical studies with diverse sample sizes

2021 ◽  
Author(s):  
Rafael De Andrade Moral ◽  
Unai Diaz-Orueta ◽  
Javier Oltra-Cucarella
Keyword(s):  
Linear Regression ◽  
True Positive Rate ◽  
Reliable Change Index ◽  
True Positive ◽  
Longitudinal Assessment ◽  
True Negative ◽  
Reliable Change ◽  
Memory Impairments ◽  
True Negative Rate ◽  
Positive Rate

The linear regression-based Reliable Change Index (RCI) is widely used to identify memory impairments through longitudinal assessment. However, the minimum sample size required for estimates to be reliable has never been specified. Using the Alzheimer’s Disease Neuroimaging Initiative data as true parameters, we run simulations for samples of size 10 to 1000 and analyzed the percentage of times the estimates are significant, their coverage rate, and the accuracy of the models including both the True Positive Rate (TPR) and the True Negative Rate (TNR). We compared the linear RCI with a logistic RCI for discrete, bounded scores. We found that the logistic RCI is more accurate than the linear RCI overall, with the linear RCI approximating the logistic RCI for samples of size 200 or greater. We provide an R code for researchers and clinicians to calculate the logistic RCI with samples smaller than 200.

scholarly journals An Attention-Based Network for Textured Surface Anomaly Detection

2020 ◽  
Vol 10 (18) ◽  
pp. 6215
Author(s):  
Gaokai Liu ◽  
Ning Yang ◽  
Lei Guo
Keyword(s):  
Anomaly Detection ◽  
True Positive Rate ◽  
Textured Surface ◽  
True Positive ◽  
Textured Surfaces ◽  
True Negative ◽  
Detection Rates ◽  
Adaptive Scheme ◽  
True Negative Rate ◽  
Positive Rate

Textured surface anomaly detection is a significant task in industrial scenarios. In order to further improve the detection performance, we proposed a novel two-stage approach with an attention mechanism. Firstly, in the segmentation network, the feature extraction and anomaly attention modules are designed to capture the detail information as much as possible and focus on the anomalies, respectively. To strike dynamic balances between these two parts, an adaptive scheme where learnable parameters are gradually optimized is introduced. Subsequently, the weights of the segmentation network are frozen, and the outputs are fed into the classification network, which is trained independently in this stage. Finally, we evaluate the proposed approach on DAGM 2007 dataset which consists of diverse textured surfaces with weakly-labeled anomalies, and the experiments demonstrate that our method can achieve 100% detection rates in terms of TPR (True Positive Rate) and TNR (True Negative Rate).

scholarly journals STUDI KOMPARASI METODE SEGMENTASI PARU-PARU PADA CITRA CT-SCAN AKSIAL

2019 ◽  
Vol 7 (3) ◽  
pp. 283
Author(s):  
Made Satria Wibawa
Keyword(s):  
Ground Truth ◽  
True Positive Rate ◽  
Computer Assisted ◽  
True Positive ◽  
Ct Diagnosis ◽  
True Negative ◽  
Fuzzy C Means ◽  
True Negative Rate ◽  
Positive Rate ◽  
Assisted Diagnosis

Kanker paru dapat diobati jika diagnosis dini dilakukan. Diagnosis dapat dilakukan menggunakan modalitas citra Computed Tomography (CT). Diagnosis kanker paru melalui citra CT dilakukan oleh tenaga medis. Untuk membantu diagnosis kanker, tenaga medis dapat dibantu dengan Computer Assisted Diagnosis (CAD). Dalam CAD, tahapan pertama yang paling penting adalah segmentasi citra paru-paru. Penelitian ini melakukan studi komparasi metode segmentasi citra CT paru-paru. Terdapat tiga metode segmentasi yang digunakan, yaitu Otsu, K-Means dan Fuzzy C-Means. Proses evaluasi menggunakan metrik akurasi, true negative rate dan true positive rate. Berdasarkan nilai yang diperoleh dari ketiga parameter evaluasi tersebut, ketiga metode segmentasi dapat memberikan hasil segmentasi yang mendekati citra ground truth. Namun, dilihat dari sebaran hasil nilai ketiga parameter evaluasi yang didapatkan dari seluruh citra, metode Otsu sedikit lebih unggul dibandingkan metode K-Means dan Fuzzy C-Means.

scholarly journals Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

2020 ◽  
Author(s):  
Lawrence Hall ◽  
Dmitry Goldgof ◽  
Rahul Paul ◽  
Gregory M. Goldgof
Keyword(s):  
False Negative ◽  
True Positive Rate ◽  
Small Data ◽  
Good Resolution ◽  
X Rays ◽  
True Positive ◽  
Data Set ◽  
X Ray ◽  
Negative Rate ◽  
Positive Rate

<p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 135 chest X-rays of COVID-19 and 320 chest X-rays of viral and bacterial pneumonia. </p><p> A pre-trained deep convolutional neural network, Resnet50 was tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were </p><p> an overall accuracy of 89.2% with a COVID-19 true positive rate of 0.8039 and an AUC of 0.95. Pre-trained Resnet50 and VGG16 plus our own small CNN were tuned or trained on a balanced set of COVID-19 and pneumonia chest X-rays. An ensemble of the three types of CNN classifiers was applied to a test set of 33 unseen COVID-19 and 218 pneumonia cases. The overall accuracy was 91.24% with the true positive rate for COVID-19 of 0.7879 with 6.88% false positives for a true negative rate of 0.9312 and AUC of 0.94. </p><p> This preliminary study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images at good resolution will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19.</p>

Performance of Fineneedle Aspiration Cytology of the Breast. Clinical Experience in Ravenna (Italy)

1993 ◽  
Vol 79 (6) ◽  
pp. 413-417
Author(s):  
Lauro Bucchi ◽  
Patrizia Schincaglia ◽  
Giangiuseppe Melandri ◽  
Nori Morini ◽  
Carlo Naldoni ◽  
...  
Keyword(s):  
Predictive Value ◽  
Breast Cancer Case ◽  
True Positive Rate ◽  
Cancer Case ◽  
Breast Lesions ◽  
Additional Contribution ◽  
True Positive ◽  
Aspiration Cytology ◽  
True Negative ◽  
Positive Rate

Aims and background Fineneedle aspiration cytology (FNAC) is a routine test in the evaluation of breast lesions. We assessed the diagnostic accuracy of mammography (MG), physical examination (PE), ultrasonography (US) and FNAC in 1064 histologically confirmed breast lesions (638 malignant, 426 benign) observed consecutively at the Cancer Prevention Center of Ravenna (Italy). Methods The performance of each test and the additional contribution of FNAC were determined. Results FNAC was done in 69.6 % of cancers and 39.7 % of benign lesions (P = 0.00000), the frequency of aspiration being significantly associated with severity at MG, PE, and US. For FNAC, the true positive rate was 95.1 % and the true negative rate 67.4 %. Only one breast cancer case was detected by FNAC alone (additional true positive rate 0.2 %). The positive predictive value of FNAC in the absence of other abnormalities was 5 %. The negative predictive value of a benign report at MG, PE, US and FNAC was 100 %. Conclusions All breast lesions should be evaluated by all available techniques, especially FNAC, and open biopsy should be avoided for those reported as benign at all tests.

scholarly journals genuMet: distinguish genuine untargeted metabolic features without quality control samples

2019 ◽  
Author(s):  
L Cao ◽  
C Clish ◽  
FB Hu ◽  
MA Martínez-González ◽  
C Razquin ◽  
...  
Keyword(s):  
Quality Control ◽  
Large Scale ◽  
Control Method ◽  
R Package ◽  
True Positive Rate ◽  
Supplementary Information ◽  
True Negative ◽  
True Negative Rate ◽  
Positive Rate ◽  
Measurement Artifacts

AbstractMotivationLarge-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.ResultsWe introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.Availability and implementationgenuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.ContactLiming Liang: [email protected] informationSupplementary data are available at ….

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