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1. Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable

   https://doi.org/10.1017/wtc.2023.3  

   Davarzani, Samaneh; Saucier, David; Talegaonkar, Purva; Parker, Erin; Turner, Alana; Middleton, Carver; Carroll, Will; Ball, John E.; Gurbuz, Ali; Chander, Harish; et al (January 2023, Wearable Technologies)

   Abstract The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R -squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system. 

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2. Tidal Wetland Gross Primary Production Across the Continental United States, 2000–2019

   https://doi.org/10.1029/2019GB006349  

   Feagin, R. A.; Forbrich, I.; Huff, T. P.; Barr, J. G.; Ruiz‐Plancarte, J.; Fuentes, J. D.; Najjar, R. G.; Vargas, R.; Vázquez‐Lule, A.; Windham‐Myers, L.; et al (February 2020, Global Biogeochemical Cycles)

   Abstract We mapped tidal wetland gross primary production (GPP) with unprecedented detail for multiple wetland types across the continental United States (CONUS) at 16‐day intervals for the years 2000–2019. To accomplish this task, we developed the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field‐based eddy covariance tower data into a single Bayesian framework, and used a super computer network and remote sensing imagery (Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index). We found a strong fit between the BC model and eddy covariance data from 10 different towers (r²= 0.83,p< 0.001, root‐mean‐square error = 1.22 g C/m²/day, average error was 7% with a mean bias of nearly zero). When compared with NASA's MOD17 GPP product, which uses a generalized terrestrial algorithm, the BC model reduced error by approximately half (MOD17 hadr²= 0.45,p< 0.001, root‐mean‐square error of 3.38 g C/m²/day, average error of 15%). The BC model also included mixed pixels in areas not covered by MOD17, which comprised approximately 16.8% of CONUS tidal wetland GPP. Results showed that across CONUS between 2000 and 2019, the average daily GPP per m²was 4.32 ± 2.45 g C/m²/day. The total annual GPP for the CONUS was 39.65 ± 0.89 Tg C/year. GPP for the Gulf Coast was nearly double that of the Atlantic and Pacific Coasts combined. Louisiana alone accounted for 15.78 ± 0.75 Tg C/year, with its Atchafalaya/Vermillion Bay basin at 4.72 ± 0.14 Tg C/year. The BC model provides a robust platform for integrating data from disparate sources and exploring regional trends in GPP across tidal wetlands. 

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3. Assessment of Current Capabilities in Modeling the Ionospheric Climatology for Space Weather Applications: foF2 and hmF2‐II

   https://doi.org/10.1029/2024SW004166  

   Jee, G.; Shim, J_S; Song, I‐S; Kwak, Y‐S; Tsagouri, I.; Goncharenko, L.; Singh, D.; Rastaetter, L.; Yue, J.; Chou, M.; et al (June 2025, Space Weather)

   Abstract We expand the assessment study of modeling capabilities in the prediction of foF2 and hmF2 for the ionospheric climatology (Tsagouri et al., 2018,https://doi.org/10.1029/2018sw002035) by using updated empirical (IRI and MIT Empirical model) and physics‐based models (CTIPe, WACCM‐X, and TIE‐GCM) as well as the additional observations in the southern hemisphere. Monthly medians of foF2 and hmF2 are considered to evaluate the model performance for the entire year of 2012. For quantitative evaluation, we employ several metrics including the correlation coefficient (R), coefficient of determination (R²), root‐mean square error (RMSE), mean error (ME), and mean relative error (MRE). The linear regression analysis shows that the empirical models perform much better than physics‐based models for foF2 but to a lesser degree for hmF2. There are negligible hemispheric differences in the predictions from empirical models. All the physics‐based models show relatively good correlations with the observations for foF2 in the northern hemisphere compared to the southern hemisphere, but the hemispheric differences are small for hmF2. The results of the study indicate that recent versions of empirical models tend to perform better than old versions of the models, but this is not always true for physics‐based models. 

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4. Data Driven Machine Learning for Estimation of PFAS Partitioning on Various Surface Materials

   Harsh V. Patel, Hyoshin Park (June 2023, AEESP Research and Education Conference 2023)

   This study, data driven machine learning model was developed to estimate the partitioning of Per- and Poly-fluoroalkyl Substances (PFAS) compounds during aqueous adsorption on various adsorbent materials with a vision to potentially replace the time-consuming and labor-intensive adsorption experiments. Various regression models were trained and tested using previously published data. 290 data points and 170 data points for activated carbon and mineral adsorbents, respectively, were mined for training the models and 10 data points were used to test the trained models. Statistical parameters, such as Root-Mean-Square Error (RSME), R-Squared, Mean Average Error (MAE), Mean Squared Error (MSE), etc., were used to compare the regression models. It was found that rational quadratic GPR (R-squared = 0.9966) and fine regression tree (R-Squared = 0.9427) models had the highest estimation accuracy for carbon-based and mineral-based adsorbents, respectively. These models were then validated for prediction accuracy using 10 data points from previous studies as an outer test set. Rational quadratic GPR was able to achieve 99% prediction accuracy for carbon-based adsorbent, while fine tree regression model was able to achieve 94% prediction accuracy. Despite such high estimation accuracy, the data mining process revealed the data shortage and the need for more research on PFAS adsorption to present real-world models. This study, as one of the first, shed a light on the determination of key parameters in aquatic chemistry with data mining and machine learning approaches. 

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5. Closing the Wearable Gap—Part IV: 3D Motion Capture Cameras Versus Soft Robotic Sensors Comparison of Gait Movement Assessment

   https://doi.org/10.3390/electronics8121382  

   Saucier, David; Davarzani, Samaneh; Turner, Alana; Luczak, Tony; Nguyen, Phuoc; Carroll, Will; Burch V, Reuben F.; Ball, John E.; Smith, Brian K.; Chander, Harish; et al (December 2019, Electronics)

   The purpose of this study was to use 3D motion capture and stretchable soft robotic sensors (SRS) to collect foot-ankle movement on participants performing walking gait cycles on flat and sloped surfaces. The primary aim was to assess differences between 3D motion capture and a new SRS-based wearable solution. Given the complex nature of using a linear solution to accurately quantify the movement of triaxial joints during a dynamic gait movement, 20 participants performing multiple walking trials were measured. The participant gait data was then upscaled (for the SRS), time-aligned (based on right heel strikes), and smoothed using filtering methods. A multivariate linear model was developed to assess goodness-of-fit based on mean absolute error (MAE; 1.54), root mean square error (RMSE; 1.96), and absolute R2 (R2; 0.854). Two and three SRS combinations were evaluated to determine if similar fit scores could be achieved using fewer sensors. Inversion (based on MAE and RMSE) and plantar flexion (based on R2) sensor removal provided second-best fit scores. Given that the scores indicate a high level of fit, with further development, an SRS-based wearable solution has the potential to measure motion during gait- based tasks with the accuracy of a 3D motion capture system. 

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