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1. Vortex Fiber Nulling for Exoplanet Observations: First Direct Detection of M Dwarf Companions around HIP 21543, HIP 94666, and HIP 50319

   https://doi.org/10.3847/2041-8213/ad3619  

   Echeverri, Daniel; Xuan, Jerry W.; Monnier, John D.; Delorme, Jacques-Robert; Wang, Jason J.; Jovanovic, Nemanja; Horstman, Katelyn; Ruane, Garreth; Mennesson, Bertrand; Serabyn, Eugene; et al (April 2024, The Astrophysical Journal Letters)

   Abstract Vortex fiber nulling (VFN) is a technique for detecting and characterizing faint companions at small separations from their host star. A near-infrared (∼2.3μm) VFN demonstrator mode was deployed on the Keck Planet Imager and Characterizer (KPIC) instrument at the Keck Observatory and presented earlier. In this Letter, we present the first VFN companion detections. Three targets, HIP 21543 Ab, HIP 94666 Ab, and HIP 50319 B, were detected with host–companion flux ratios between 70 and 430 at and within one diffraction beamwidth (λ/D). We complement the spectra from KPIC VFN with flux ratio and position measurements from the CHARA Array to validate the VFN results and provide a more complete characterization of the targets. This Letter reports the first direct detection of these three M dwarf companions, yielding their first spectra and flux ratios. Our observations provide measurements of bulk properties such as effective temperatures, radial velocities, and $v\sin i$, and verify the accuracy of the published orbits. These detections corroborate earlier predictions of the KPIC VFN performance, demonstrating that the instrument mode is ready for science observations. 

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2. The Effect of Hip Assistance Levels on Human Energetic Cost Using Robotic Hip Exoskeletons

   https://doi.org/10.1109/LRA.2019.2890896  

   Kang, Inseung; Hsu, Hsiang; Young, Aaron (April 2019, IEEE Robotics and Automation Letters)
3. Unaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip

   https://doi.org/10.1115/1.4064988  

   Perry, Sheridan; Folkman, Matthew; O'Brien, Takara; Wilson, Lauren A.; Coyle, Eric; Liu, Raymond W.; Price, Charles T.; Huayamave, Victor A. (November 2024, Journal of Engineering and Science in Medical Diagnostics and Therapy)

   Abstract Developmental dysplasia of the hip (DDH) is a condition in which the acetabular socket inadequately contains the femoral head (FH). If left untreated, DDH can result in degenerative changes in the hip joint. Several imaging techniques are used for DDH assessment. In radiographs, the acetabular index (ACIN), center-edge angle, Sharp's angle (SA), and migration percentage (MP) metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify radiographic measurements and improve traditional methods of identifying DDH. The dataset consisted of 60 subject radiographs rotated along the craniocaudal and mediolateral axes 25 times, generating 1500 images. A CNN detection algorithm was used to identify key radiographic metrics for the diagnosis of DDH. The algorithm was able to detect the metrics with reasonable accuracy in comparison to the manually computed metrics. The CNN performed well on images with high contrast margins between bone and soft tissues. In comparison, the CNN was not able to identify some critical points for metric calculation on a few images that had poor definition due to low contrast between bone and soft tissues. This study shows that CNNs can efficiently measure clinical parameters to assess DDH on radiographs with high contrast margins between bone and soft tissues with purposeful rotation away from an ideal image. Results from this study could help inform and broaden the existing bank of information on using CNNs for radiographic measurement and medical condition prediction. 

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4. The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study

   https://doi.org/10.1249/mss.0000000000002705  

   Greenwood-Hickman, Mikael Anne; Nakandala, Supun; Jankowska, Marta M.; Rosenberg, Dori; Tuz-Zahra, Fatima; Bellettiere, John; Carlson, Jordan; Hibbing, Paul R.; Zou, Jingjing; LaCroix, Andrea Z.; et al (January 2021, Medicine & Science in Sports & Exercise)

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5. Anticipation and Delayed Estimation of Sagittal Plane Human Hip Moments using Deep Learning and a Robotic Hip Exoskeleton

   https://doi.org/10.1109/ICRA48891.2023.10161286  

   Molinaro, Dean D.; Park, Ethan O.; Young, Aaron J. (May 2023, IEEE)

   Estimating human joint moments using wearable sensors has utility for personalized health monitoring and generalized exoskeleton control. Data-driven models have potential to map wearable sensor data to human joint moments, even with a reduced sensor suite and without subject-specific calibration. In this study, we quantified the RMSE and R 2 of a temporal convolutional network (TCN), trained to estimate human hip moments in the sagittal plane using exoskeleton sensor data (i.e., a hip encoder and thigh- and pelvis-mounted inertial measurement units). We conducted three analyses in which we iteratively retrained the network while: 1) varying the input sequence length of the model, 2) incorporating noncausal data into the input sequence, thus delaying the network estimates, and 3) time shifting the labels to train the model to anticipate (i.e., predict) human hip moments. We found that 930 ms of causal input data maintained model performance while minimizing input sequence length (validation RMSE and R 2 of 0.141±0.014 Nm/kg and 0.883±0.025, respectively). Further, delaying the model estimate by up to 200 ms significantly improved model performance compared to the best causal estimators (p<0.05), improving estimator fidelity in use cases where delayed estimates are acceptable (e.g., in personalized health monitoring or diagnoses). Finally, we found that anticipating hip moments further in time linearly increased model RMSE and decreased R 2 (p<0.05); however, performance remained strong (R 2 >0.85) when predicting up to 200 ms ahead. 

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