Development of an AI-supported exercise therapy for advanced cancer patients

Abstract

Exercise therapy is able to reduce symptom burden in advanced cancer patients (ACP). However, ACP daily form differs between days e.g. tumor and therapy induced. We included five ACP to classify individual exercise capacity due to cardiovascular parameters. Features are extracted from the electrocardiogram and then processed with a neural network after feature selection. Results indicate a high classification quality with an F1 score up to 0.95 ± 0.05. Including neuronal networks for training control can potentially help to manage exercise intensity ideal.

A Study on Caregiver Activity Recognition for the Elderly at Home Based on the XGBoost Model

“…This field has gained significant importance, especially in the domains of medicine, military, and security applications. For instance, exercise therapy can reduce symptom burden in advanced cancer patients [12]. Additionally, robots have been employed in certain studies to directly facilitate activity recognition, such as utilizing them for gait training and other rehabilitation exercises aimed at patients with ambulatory impairments [13].…”