Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor

TLDR

The study introduces a real‑time algorithm that automatically recognizes physical activities and, in some cases, their intensities using five wireless triaxial accelerometers and a wireless heart‑rate monitor. The algorithm was evaluated on 30 gymnasium activities collected from 21 participants at two laboratories. It achieved 94.6 % accuracy with subject‑dependent training and 56.3 % with subject‑independent training, and adding heart‑rate data improved accuracy by only 1.2 % and 2.1 % respectively; without intensity differentiation, subject‑independent performance reached 80.6 %, indicating heart‑rate contributes little discriminative power.

Abstract

In this paper, we present a real-time algorithm for automatic recognition of not only physical activities, but also, in some cases, their intensities, using five triaxial wireless accelerometers and a wireless heart rate monitor. The algorithm has been evaluated using datasets consisting of 30 physical gymnasium activities collected from a total of 21 people at two different labs. On these activities, we have obtained a recognition accuracy performance of 94.6% using subject-dependent training and 56.3% using subject-independent training. The addition of heart rate data improves subject-dependent recognition accuracy only by 1.2% and subject-independent recognition only by 2.1%. When recognizing activity type without differentiating intensity levels, we obtain a subject-independent performance of 80.6%. We discuss why heart rate data has such little discriminatory power.