PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed pre-impact fall detection systems using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms)

Clinical applicability of quantitative nailfold capillaroscopy in differential diagnosis of connective tissue diseases with Raynaud's phenomenon

Background/PurposeNailfold capillaroscopy is a useful tool to distinguish primary from secondary Raynaud's phenomenon (RP) by examining the morphology of nailfold capillaries but its role in disease diagnosis is not clearly established. The purpose of this study was to evaluate the roles of quantitative nailfold capillaroscopy in differential diagnosis of connective tissue diseases (CTDs) with RP.MethodsThe data between the year 2005 and 2009 were retrieved from the nailfold capillaroscopic database of National Taiwan University Hospital (NTUH). Only the data from the patients with RP were analyzed. The criteria for interpretation of capillaroscopic findings were predefined. The final diagnoses of the patients were based on the American College of Rheumatology classification criteria for individual diseases, independent of nailfold capillaroscopic findings. The sensitivity and the specificity of each capillaroscopic pattern to the diseases were determined.ResultsThe data from a total of 67 patients were qualified for the current study. We found the sensitivity and specificity of scleroderma pattern for systemic sclerosis (SSc) were 89.47% and 80%, and the specificity of the early, active, and late scleroderma patterns for SSc reached 87.5%, 97.5%, and 95%, respectively. The sensitivity/specificity of systemic lupus erythematosus (SLE) pattern for SLE and polymyositis/dermatomyositis (PM/DM) pattern for PM/DM were 33.33%/95.45% and 60%/96.3%, respectively. The sensitivity/specificity of mixed connective tissue disease (MCTD) pattern for MCTD were 20%/100%.ConclusionThe nailfold capillaroscopic (NC) patterns may be useful in the differential diagnosis of CTDs with RP. The NC patterns for SSc and PM/DM are both sensitive and specific to the diseases, while the SLE and MCTD patterns exhibit high specificity but relatively low sensitivity

Pion PDFs confronted by Fixed-Target Charmonium Production

The pion, as the Goldstone boson of the strong interaction, is the lightest QCD bound state and responsible for the long-range nucleon-nucleon interaction inside the nucleus. Our knowledge on the pion partonic structure is limited by the existing Drell-Yan data which are primarily sensitive to the pion valence-quark distributions. The recent progress of global analysis of pion's parton distribution functions (PDFs) utilizing various experimental approaches are introduced. From comparisons between the pion-induced $J/\psi$ and $\psi(2S)$ production data with theoretical calculations using the CEM and NRQCD models, we show how these charmonium production data could provide useful constraints on the pion PDFs.Comment: 17 pages, 6 figures; invited review. arXiv admin note: text overlap with arXiv:2006.06947, arXiv:2209.0407