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1. Male cellular telephone exposure, fecundability, and semen quality: results from two preconception cohort studies

   https://doi.org/10.1093/humrep/deab001  

   Hatch, E E; Willis, S K; Wesselink, A K; Mikkelsen, E M; Eisenberg, M L; Sommer, G J; Sorensen, H T; Rothman, K J; Wise, L A (February 2021, Human Reproduction)

   Abstract STUDY QUESTION To what extent is exposure to cellular telephones associated with male fertility? SUMMARY ANSWER Overall, we found little association between carrying a cell phone in the front pants pocket and male fertility, although among leaner men (BMI <25 kg/m2), carrying a cell phone in the front pants pocket was associated with lower fecundability. WHAT IS KNOWN ALREADY Some studies have indicated that cell phone use is associated with poor semen quality, but the results are conflicting. STUDY DESIGN, SIZE, DURATION Two prospective preconception cohort studies were conducted with men in Denmark (n = 751) and in North America (n = 2349), enrolled and followed via the internet from 2012 to 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS On the baseline questionnaire, males reported their hours/day of carrying a cell phone in different body locations. We ascertained time to pregnancy via bi-monthly follow-up questionnaires completed by the female partner for up to 12 months or until reported conception. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% confidence intervals (CIs) for the association between male cell phone habits and fecundability, focusing on front pants pocket exposure, within each cohort separately and pooling across the cohorts using a fixed-effect meta-analysis. In a subset of participants, we examined selected semen parameters (semen volume, sperm concentration and sperm motility) using a home-based semen testing kit. MAIN RESULTS AND THE ROLE OF CHANCE There was little overall association between carrying a cell phone in a front pants pocket and fecundability: the FR for any front pants pocket exposure versus none was 0.94 (95% CI: 0.0.83–1.05). We observed an inverse association between any front pants pocket exposure and fecundability among men whose BMI was <25 kg/m2 (FR = 0.72, 95% CI: 0.59–0.88) but little association among men whose BMI was ≥25 kg/m2 (FR = 1.05, 95% CI: 0.90–1.22). There were few consistent associations between cell phone exposure and semen volume, sperm concentration, or sperm motility. LIMITATIONS, REASONS FOR CAUTION Exposure to radiofrequency radiation from cell phones is subject to considerable non-differential misclassification, which would tend to attenuate the estimates for dichotomous comparisons and extreme exposure categories (e.g. exposure 8 vs. 0 h/day). Residual confounding by occupation or other unknown or poorly measured factors may also have affected the results. WIDER IMPLICATIONS OF THE FINDINGS Overall, there was little association between carrying one’s phone in the front pants pocket and fecundability. There was a moderate inverse association between front pants pocket cell phone exposure and fecundability among men with BMI <25 kg/m2, but not among men with BMI ≥25 kg/m2. Although several previous studies have indicated associations between cell phone exposure and lower sperm motility, we found few consistent associations with any semen quality parameters. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the National Institutes of Health, grant number R03HD090315. In the last 3 years, PRESTO has received in-kind donations from Sandstone Diagnostics (for semen kits), Swiss Precision Diagnostics (home pregnancy tests), Kindara.com (fertility app), and FertilityFriend.com (fertility app). Dr. L.A.W. is a fibroid consultant for AbbVie, Inc. Dr. H.T.S. reports that the Department of Clinical Epidemiology is involved in studies with funding from various companies as research grants to and administered by Aarhus University. None of these studies are related to the current study. Dr. M.L.E. is an advisor to Sandstone Diagnostics, Ro, Dadi, Hannah, and Underdog. Dr. G.J.S. holds ownership in Sandstone Diagnostics Inc., developers of the Trak Male Fertility Testing System. In addition, Dr. G.J.S. has a patent pending related to Trak Male Fertility Testing System issued. TRIAL REGISTRATION NUMBER N/A 

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2. Pervasive Pose Estimation for Fall Detection

   https://doi.org/10.1145/3478027  

   Luo, Jiaqing; Bai, Ruiyu; He, Suining; Shin, Kang G. (July 2022, ACM Transactions on Computing for Healthcare)

   Falls are the second leading cause of accidental or unintentional injuries/deaths worldwide. Accurate pose estimation using commodity mobile devices will help early detection and injury assessment of falls, which are essential for the first aid of elderly falls. By following the definition of fall, we propose a P ervasive P ose Est imation scheme for fall detection ( P \( ^2 \) Est ), which measures changes in tilt angle and height of the human body. For the tilt measurement, P \( ^2 \) Est leverages the pointing of the mobile device, e.g., the smartphone, when unlocking to associate the Device coordinate system with the World coordinate system. For the height measurement, P \( ^2 \) Est exploits the fact that the person’s height remains unchanged while walking to calibrate the pressure difference between the device and the floor. We have prototyped and tested P \( ^2 \) Est in various situations and environments. Our extensive experimental results have demonstrated that P \( ^2 \) Est can track the body orientation irrespective of which pocket the phone is placed in. More importantly, it enables the phone’s barometer to detect falls in various environments with decimeter-level accuracy. 

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3. Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection

   https://doi.org/10.1109/COMPSAC.2019.10252  

   Gentry, Austin; Mongan, William; Lee, Brent; Montgomery, Owen; Dandekar, Kapil (July 2019, 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC))

   Using a wearable electromyography (EMG) and an accelerometer sensor, classification of subject activity state (i.e., walking, sitting, standing, or ankle circles) enables detection of prolonged "negative" activity states in which the calf muscles do not facilitate blood flow return via the deep veins of the leg. By employing machine learning classification on a multi-sensor wearable device, we are able to classify human subject state between "positive" and "negative" activities, and among each activity state, with greater than 95% accuracy. Some negative activity states cannot be accurately discriminated due to their similar presentation from an accelerometer (i.e., standing vs. sitting); however, it is desirable to separate these states to better inform the risk of developing a Deep Vein Thrombosis (DVT). Augmentation with a wearable EMG sensor improves separability of these activities by 30%. 

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4. The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study

   https://doi.org/10.2196/23681  

   Davoudi, Anis; Mardini, Mamoun T; Nelson, David; Albinali, Fahd; Ranka, Sanjay; Rashidi, Parisa; Manini, Todd M (January 2021, JMIR mHealth and uHealth)

   Background Research has shown the feasibility of human activity recognition using wearable accelerometer devices. Different studies have used varying numbers and placements for data collection using sensors. Objective This study aims to compare accuracy performance between multiple and variable placements of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. Methods In total, 93 participants (mean age 72.2 years, SD 7.1) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary versus nonsedentary, locomotion versus nonlocomotion, and lifestyle versus nonlifestyle activities (eg, leisure walk vs computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on 5 different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used to develop random forest models to assess activity category recognition accuracy and MET estimation. Results Model performance for both MET estimation and activity category recognition were strengthened with the use of additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03-0.09 MET increase in prediction error compared with wearing all 5 devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for the detection of locomotion (balanced accuracy decrease range 0-0.01), sedentary (balanced accuracy decrease range 0.05-0.13), and lifestyle activities (balanced accuracy decrease range 0.04-0.08) compared with all 5 placements. The accuracy of recognizing activity categories increased with additional placements (accuracy decrease range 0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. Conclusions Additional accelerometer devices slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults. 

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5. SecTap: Secure Back of Device Input System for Mobile Devices

   https://doi.org/10.1109/INFOCOM.2018.8485939  

   Ling, Zhen; Luo, Junzhou; Liu, Yaowen; Yang, Ming; Wu, Kui; Fu, Xinwen (April 2018, IEEE Conference on Computer Communications (INFOCOM))

   Smart mobile devices have become an integral part of people's life and users often input sensitive information on these devices. However, various side channel attacks against mobile devices pose a plethora of serious threats against user security and privacy. To mitigate these attacks, we present a novel secure Back-of-Device (BoD) input system, SecTap, for mobile devices. To use SecTap, a user tilts her mobile device to move a cursor on the keyboard and tap the back of the device to secretly input data. We design a tap detection method by processing the stream of accelerometer readings to identify the user's taps in real time. The orientation sensor of the mobile device is used to control the direction and the speed of cursor movement. We also propose an obfuscation technique to randomly and effectively accelerate the cursor movement. This technique not only preserves the input performance but also keeps the adversary from inferring the tapped keys. Extensive empirical experiments were conducted on different smart phones to demonstrate the usability and security on both Android and iOS platforms. 

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