Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms

Sensors (Basel). 2022 Jan 28;22(3):1016. doi: 10.3390/s22031016.

Abstract

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.

Keywords: CNN; Naive Bayes; human activity recognition; machine learning; motion pattern; sensors.

MeSH terms

  • Algorithms*
  • Bayes Theorem
  • Cluster Analysis
  • Human Activities
  • Humans
  • Neural Networks, Computer*