Study and validation of an explainable machine learning-based mortality prediction following emergency surgery in the elderly: A prospective observational study.

Introduction: The heterogeneity of procedures and the variety of comorbidities of the patients undergoing surgery in an emergency setting makes perioperative risk stratification, planning, and risk mitigation crucial. In this optic, Machine Learning has the capability of deriving data-driven predictions based on multivariate interactions of thousands of instances. Our aim was to cross-validate and test interpretable models for the prediction of post-operative mortality after any surgery in an emergency setting on elderly patients.

Grasp force estimation from the transient EMG using high-density surface recordings.

Objective: Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e.

Dielectric elastomer actuators for octopus inspired suction cups.

Suction cups are often found in nature as attachment strategy in water. Nevertheless, the application of the artificial counterpart is limited by the dimension of the actuators and their usability in wet conditions. A novel design for the development of a suction cup inspired by octopus suckers is presented.