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2 results on '"Stephen Tang"'

1. Simplified Mathematical Model to Evaluate Sperm Concentration in Kremer'S Capillary Tube Test: A Preliminary Study Report

Author

Stephen Tang, Hung-Sheng Chen, Jau-Nan Lee, and Eing-Mei Tsai

Subjects
capillary tube test, cervical mucus penetration, fertility investigation, Gynecology and obstetrics, RG1-991
Abstract

Objective: The in vitro cervical mucus penetration assay, which is rarely used now, plays a role in basic research and is used to determine the clinical importance of cervical hostility and immunologic causes of infertility. Sperm concentration observed in a capillary tube test, where mucus-filled cylindrical tubes are placed with an end immersed in a semen reservoir mounted on a glass slide, indicates general sperm function and has a prognostic value for subsequent in vivo pregnancy. Because cylindrical tubes are optically inferior and may hinder the counting of sperm, the assessment of the efficiency of sperm penetration into cervical mucus is a time-consuming procedure and requires complex calculations. The present study investigated the feasibility of applying a simplified calculation model to estimate sperm concentration using the results of a capillary tube test. The postulated equation (sperm concentration ! N × 103/0.339 sperm/mL) was derived from a mathematical model of definite integrals used to calculate the volume of intersection between two cylinders. Materials and Methods: Semen samples from 46 male subjects were allowed to penetrate mid-cycle human cervical mucus contained in cylindrical tubes and incubated at 37° C for 60 minutes. Assessments of sperm concentration in the mucus column were performed using the postulated equation and a control method whereby the total number of sperm was assessed after extracting all contents from the capillary. Results: Estimates of sperm concentration obtained using the postulated equation (149.2 ± 30.0 × 103/mL) were similar and correlated with those obtained using the control method (166.3 ± 29.9 × 103/mL; r = 0.79; p < 0.05). The mean difference in sperm concentration was not significant (17.2 ± 19.2 × 103/mL). Conclusion: The strong correlation of sperm concentrations between these two methods suggests that the postulated equation may provide a simplified calculation model to indicate penetration efficiency in the capillary tube test.

Published
2005
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2. Multiple density maps information fusion for effectively assessing intensity pattern of lifelogging physical activity

Author

Stephen Tang, Po Yang, Martin Hanneghan, and Jun Qi

Subjects
business.industry, Computer science, Cognitive Neuroscience, Automatic identification and data capture, Physical activity, Health technology, Wearable computer, 020206 networking & telecommunications, 02 engineering and technology, Lifelog, Machine learning, computer.software_genre, Computer Science Applications, QA76, Artificial Intelligence, Health care, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Mobile device, Wearable technology
Abstract

Physical activity (PA) measurement is a crucial task in healthcare technology aimed at monitoring the progression and treatment of many chronic diseases. Traditional lifelogging PA measures require relatively high cost and can only be conducted in controlled or semi-controlled environments, though they exhibit remarkable precision of PA monitoring outcomes. Recent advancement of commercial wearable devices and smartphones for recording one’s lifelogging PA has popularized data capture in uncontrolled environments. However, due to diverse life patterns and heterogeneity of connected devices as well as the PA recognition accuracy, lifelogging PA data measured by wearable devices and mobile phones contains much uncertainty thereby limiting their adoption for healthcare studies. To improve the feasibility of PA tracking datasets from commercial wearable/mobile devices, this paper proposes a lifelogging PA intensity pattern decision making approach for lifelong PA measures. The method is to firstly remove some irregular uncertainties (IU) via an Ellipse fitting model, and then construct a series of monthly based hour-day density map images for representing PA intensity patterns with regular uncertainties (RU) on each month. Finally it explores Dempster-Shafer theory of evidence fusing information from these density map images for generating a decision making model of a final personal lifelogging PA intensity pattern. The approach has significantly reduced the uncertainties and incompleteness of datasets from third party devices. Two case studies on a mobile personalized healthcare platform MHA [1] connecting the mobile app Moves are carried out. The results indicate that the proposed approach can improve effectiveness of PA tracking devices or apps for various types of people who frequently use them as a healthcare indicator.

Published
2017

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