Introduction A common criterion in decision making regarding return to sport (RTS) after knee ligament injury is that athletes should achieve symmetrical bilateral movement between the injured limb and the noninjured limb. Body-worn wireless inertial measurement units (IMU) can provide clinicians with valuable information about lower-limb kinematics and athletic performance. Methods The IMU-based novel kinematic metrics were developed. The Transitional Angular Displacement of Segment (TADS) and Symmetry Index (SI) measures that quantify lower-limb motions and interlimb symmetry during the 4-m side step test (FmSST) were developed. Test–retest reliability was measured in 20 healthy adults. Experimental application of the metrics was also determined in 15 National Collegiate Athletic Association Division I collegiate athletes who completed rehabilitation after a knee ligament injury. Results The intraclass correlation coefficient for test–retest reliability for FmSST, TADS right lower limb, TADS left lower limb, and TADS SI was 0.90 (95% confidence interval, [0.61–0.95]); 0.87 [0.63–0.96]; 0.89 [0.64–0.96], and 0.81 [0.58–0.92], respectively. The differences between TADS SI at baseline (preinjury) and RTS were also compared with those between the total times for performing the FmSST at baseline and RTS. There was no significant difference in the FmSST times between baseline and RTS (P = 0.32); however, TADS SI at the time of RTS was significantly lower than at baseline (P = 0.046). A large effect size (d = −1.04) was observed for the change in TADS SI from baseline to RTS. Conclusions Using IMU sensor technology can provide quantitative and discrete analysis to detect kinematic differences during agility after a knee ligament injury in the field or nonlaboratory setting. This approach has the potential to help clinicians improve decisions about rehabilitation at a time when an athlete is reintegrating back into sport.
We discuss image segmentation algorithms and additional space considerations for BeaverCube-2, a project under development between the MIT Space Telecommunications, Astronomy, Radiation (STAR) Lab and the Northrop Grumman Corporation that aims to demonstrate the use of an Artificial Intelligence (AI) Computational Accelerator System-on-a-Chip (SoC) on a 3U CubeSat in Low-Earth Orbit (LEO). The processing power afforded by the SoC will allow the use of modern artificial intelligence techniques as part of an Earth observation mission to obtain and process visible and infrared imagery of coastal features.We focus on three algorithms used for cloud segmentation in satellite imagery. These are a luminosity-thresholding method, a random forest method, and an autoencoder-based deep learning method. Our luminosity thresholding method classifies each pixel based on its luminosity and achieved 84% accuracy using 2 MB of memory. Our random forest method contextualizes pixels within a 3 × 3 kernel and classifies them based on the luminosity of each pixel in the kernel -it achieved 90% accuracy, with a memory usage of 700 MB. Finally, our U-Net-based deep learning method achieved 92% accuracy with 1500 MB memory usage, demonstrating modest gains over the two simpler methods, with higher accuracy in snow scenes.
<p>Small-scale ocean fronts play a significant role in absorbing the excess heat and CO2 generated by climate change, yet their dynamics are not well understood. Existing in-situ and remote sensing measurements of the ocean have inadequate spatial and temporal coverage to map small-scale ocean fronts globally. Additionally, conventional algorithms to generate ocean front maps are computationally intensive and require data with long lead times. We propose machine learning (ML) models to detect temperature and chlorophyll ocean fronts from unprocessed and radiometrically uncorrected satellite im- agery by transfer learning from existing models for edge detection. We use two separate datasets: one based on conventional approaches to ocean front detection, and a second based on human annotated ground truth1. The deep learning front detection approach significantly reduces the resources and overall lead times needed for detecting ocean fronts. The deep learning models are developed with resource-constrained edge compute platforms like CubeSats in mind, as such platforms can address the spatial and temporal coverage challenges. The highest performing models achieve accuracies of 96% and make predictions in milliseconds using unoptimized desktop CPUs and using less than 100 MB of storage; these capabilities are well- suited for CubeSat deployment. </p>
<p>Small-scale ocean fronts play a significant role in absorbing the excess heat and CO2 generated by climate change, yet their dynamics are not well understood. Existing in-situ and remote sensing measurements of the ocean have inadequate spatial and temporal coverage to map small-scale ocean fronts globally. Additionally, conventional algorithms to generate ocean front maps are computationally intensive and require data with long lead times. We propose machine learning (ML) models to detect temperature and chlorophyll ocean fronts from unprocessed and radiometrically uncorrected satellite im- agery by transfer learning from existing models for edge detection. We use two separate datasets: one based on conventional approaches to ocean front detection, and a second based on human annotated ground truth1. The deep learning front detection approach significantly reduces the resources and overall lead times needed for detecting ocean fronts. The deep learning models are developed with resource-constrained edge compute platforms like CubeSats in mind, as such platforms can address the spatial and temporal coverage challenges. The highest performing models achieve accuracies of 96% and make predictions in milliseconds using unoptimized desktop CPUs and using less than 100 MB of storage; these capabilities are well- suited for CubeSat deployment. </p>
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