Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort.

Objective: To identify the risk of acute respiratory distress syndrome (ARDS) and in-hospital mortality using long short-term memory (LSTM) framework in a mechanically ventilated (MV) non-COVID-19 cohort and a COVID-19 cohort.

Methods: We included MV ICU patients between 2017 and 2018 and reviewed patient records for ARDS and death. Using active learning, we enriched this cohort with MV patients from 2016 to 2019 (MV non-COVID-19, n=3905).

A Deep Neural Network-based method for estimation of 3D lifting motions.

The aim of this study is developing and validating a Deep Neural Network (DNN) based method for 3D pose estimation during lifting. The proposed DNN based method addresses problems associated with marker-based motion capture systems like excessive preparation time, movement obstruction, and controlled environment requirement. Twelve healthy adults participated in a protocol and performed nine lifting tasks with different vertical heights and asymmetry angles.

A computer vision based method for 3D posture estimation of symmetrical lifting.

Work-related musculoskeletal disorders (WMSD) are commonly observed among the workers involved in material handling tasks such as lifting. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks. Such an assessment has been mainly conducted using surface marker-based methods, which is time consuming and tedious.

Using a marker-less method for estimating L5/S1 moments during symmetrical lifting.

The aim of this study is to analyze the validity of a computer vision-based method to estimate 3D L5/S1 joint moment during symmetrical lifting. An important criterion to identify the non-ergonomic lifting task is the value of net moment at L5/S1 joint. This is usually calculated in a laboratory environment which is not practical for on-site biomechanical analysis.