Community-Supported Shared Infrastructure in Support of Speech Accessibility.

Purpose: The Speech Accessibility Project (SAP) intends to facilitate research and development in automatic speech recognition (ASR) and other machine learning tasks for people with speech disabilities. The purpose of this article is to introduce this project as a resource for researchers, including baseline analysis of the first released data package.

Method: The project aims to facilitate ASR research by collecting, curating, and distributing transcribed U.

A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery.

Objective: State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging.

Methods: In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes.

System events: readily accessible features for surgical phase detection.

Purpose: Surgical phase recognition using sensor data is challenging due to high variation in patient anatomy and surgeon-specific operating styles. Segmenting surgical procedures into constituent phases is of significant utility for resident training, education, self-review, and context-aware operating room technologies. Phase annotation is a highly labor-intensive task and would benefit greatly from automated solutions.

3D Sensing Algorithms Towards Building an Intelligent Intensive Care Unit.

Intensive Care Units (ICUs) are chaotic places where hundreds of tasks are carried out by many different people. Timely and coordinated execution of these tasks are directly related to quality of patient outcomes. An improved understanding of the current care process can aid in improving quality.