Sensor-based Human Activity Recognition

Sensor-based systems and technologies for human activity recognition have recently gained a momentous interest from research and industry communities. Accelerated by the rapid advancement in Internet of Things, wearable and wireless sensor technology, there has been a continuous growth of research innovation and real-world applications including remote patient monitoring, elderly monitoring, daily-life activity monitoring, accident monitoring (fall detection), vital signs monitoring, sleep monitoring, fitness/wellbeing tracking, and so on.

Key research areas related to sensor-based activity recognition include machine/deep learning, pattern recognition, digital signal processing, context-aware computing, data management and processing, information fusion and remote sensing.

Research areas

  • Machine learning methods including deep learning, transfer learning, reinforcement learning, and hybrid semi-supervised learning
  • Online and active learning for sensor stream with noisy data
  • Behavior profiling and anomaly detection
  • Device-free indoor localisation and tracking
  • Non-contact sensing methods based on IoT sensors, passive infrared sensors, wifi, radar or visible light
  • Non-contact vital signs monitoring using UWB or mmWave radar
  • Human activity recognition for smart/remote healthcare
  • Computational intelligence methods for privacy-aware and secured data management, processing, and analysis
  • Case studies and technical evaluation of existing human activity recognition methods and systems
  • Any topics related to human activity recognition and its applications (further reading)

Current projects

  • Deep learning methods for IoT sensor-based activity recognition in a multi-resident environment
  • Non-intrusive, device-free indoor localisation and tracking of multiple people
  • Radar-based human detection and activity recognition based on mmWave sensors
  • Radar-based vital signs monitoring based on mmWave sensors
  • Intelligent data management and computational offloading in IoT, Fog, and Edge computing

Research networks


Theme leader

Theme member

Student members

  • Anuradha Singh (PhD)
  • Punsisi Pemarathne (PhD)
  • Kan Ngamakeur (PhD)
  • Kinza Sarwar (PhD)
  • Dong Chen (PhD)
  • Weijie Lu (MCIS)
  • Xin Lei (MCIS)
  • Bohan Wang (MCIS)