Summary: Data sharing is central to the rapid translation of research into advances in clinical medicine and public health practice.In the context of COVID-19, there has been a rush to share data marked by an explosion of population-specific and discipline-specific resources for collecting, curating, and disseminating participant-level data.We cond
A Review of the Therapeutic Targeting of SCN9A and Nav1.7 for Pain Relief in Current Human Clinical Trials
Anton Dormer,1 Mahesh Narayanan,1 Jerome Schentag,1 Daniel Achinko,1 Elton Norman,1 James Kerrigan,2 Gary Jay,2 William Heydorn2 1Research and Development, Pepvax, Inc, Silver Spring, MD, USA; 2Research and Development, Navintus, Inc, Princeton, NJ, USACorrespondence: Anton Dormer, Research and Development, PepVax, Inc, 8720 Georgia Ave #1000,
An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection
The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors.However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful in
Human activity prediction using saliency-aware motion enhancement and weighted LSTM network
Abstract In recent years, great progress has been made in recognizing human activities in complete image sequences.However, predicting human activity earlier in a video is still a challenging task.In this paper, a novel framework named weighted long short-term memory network (WLSTM) with saliency-aware motion enhancement (SME) is proposed for video