Human activity recognition using ML and AI methods

- 01.01.2019


Guest professor 

Neil Rubens
Stanford University (USA)
Neil Rubens

Dr Neil Rubens is a graduate of the Massachusetts Institute of Technology, visiting professor and leading project manager at Stanford University (USA) on projects, related to applications of artificial intelligence for industry analysis, was working as assistant professor at University of Electro-Communications in Tokyo (Japan), has excellent work experience in business sector as software engineer, as well as founder of own startup in Japan.

Project Type: Academic

Main Challenge:

Extracting information from data became useful in various fields. Human activity can be recognized from the raw sensors data and can be used for functional, and behavioral health assessment, remote monitoring of patients, smart home activity recognition, sports analytics, fitness tracking, transportation analytics. Human Activity Recognition refers to human daily activity recognition in an automated fashion using data mining and machine learning approaches.

Funding: Internal

DA&AI tools:

  • Adversarial Autoencoders
  • Transfer learning


  • Balabka, D., Shkliarenko, D., 2021. Human activity recognition with AutoML using smartphone radio data, in: Proceedings of the 2021 ACM International Symposium on Wearable Computers. ACM, Virtual USA, pp. 346-352.
  • Balabka, D. (2019). Semi-supervised learning for human activity recognition using adversarial autoencoders. 10.1145/3341162.3344854.

Student theses:

  • Balabka, D. (2019). Human Activity Recognition with Smartphone Sensors using machine Learning Algorithms

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