Deep Architectures for Human Activity Recognition using Sensors

  • Zartasha Baloch
  • Faisal Karim Shaikh
  • Mukhtiar Ali Unar


Human activity recognition (HAR) is a renowned research field in recent years due to its applications such as physical fitness monitoring, assisted living,
elderly–care, biometric authentication and many more. The ubiquitous nature of sensors makes them a good choice to use for activity recognition. The latest
smart gadgets are equipped with most of the wearable sensors i.e. accelerometer, gyroscope, GPS, compass, camera, microphone etc. These sensors measure
various aspects of an object, and are easy to use with less cost. The use of sensors in the field of HAR opens new avenues for machine learning (ML) researchers to
accurately recognize human activities. Deep learning (DL) is becoming popular among HAR researchers due to its outstanding performance over conventional
ML techniques. In this paper, we have reviewed recent research studies on deep models for sensor–based human activity recognition. The aim of this article is to
identify recent trends and challenges in HAR.