Classification of Hand-Based and Non-Hand-Based Physical Activities Using Wearable Sensors

Das D., Birant D.

4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020, İstanbul, Turkey, 22 - 24 October 2020 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/ismsit50672.2020.9255171
  • City: İstanbul
  • Country: Turkey
  • Keywords: classification, human activity recognition, machine learning, Physical activity classification, wearable sensors
  • Dokuz Eylül University Affiliated: Yes


This study presents the application of machine learning techniques for physical activity recognition on data obtained from wearable sensors. For this purpose, it proposes the separate classification of hand-based (i.e., eating, writing, clapping) and non-hand-based (i.e., walking, jogging, sitting) human activities recorded by the accelerometer and gyroscope sensors of smartphones and watches. Different machine learning algorithms were compared to build the most appropriate model for the application. The experimental results showed that building two separate classification models for hand-based and non-hand-based activities achieved better accuracy (94.96%) than a single classification model (92.10%) which covered all activities.