Automatic Recognition of Daily Physical Activities for an Intelligent-Portable Oxygen Concentrator (iPOC)

In recent years, new autonomous physiological close-loop controlled (PCLC) medical devices for oxygen delivery are being researched. Most of this PCLC devices are based on the feedback of arterial oxygen saturation, measured using a pulse oximeter. However, pulse oximeters may provide spuriously low or high SpO₂ values. In this work, a different approach to adjust automatically oxygen dosing in portable oxygen concentrators (POC) according to the physical activity performed by patients with COPD is presented. To that purpose, the ability of various machine-learning algorithms to recognize four human daily activities from sensor signals collected from a single waist-worn tri-axial accelerometer is evaluated. A set of 56 features was considered and recognition accuracy of up to 91.15% on the four activities of daily living was obtained using a SVM classifier. The associated activity recognition error rate was lower than 5%, ensuring a low percentage of time wrongly assigned to a certain activity. The underlying idea is the hardware implementation of the SVM classifier to control the oxygen flow in intelligent portable oxygen concentrators.