Abstract:
Human Activity Recognition is a significant zone of machine learning research because of its uses
in many fields such as health care, flexible interfaces, and a smart world, human behavior detection
is gaining a lot of interest. Activities are often more abstract than words, as they're more
contextually symbolic of a human's daily life. Techniques for recognizing actions from sensorgenerated data are well-developed. However, there have been few attempts to target sensor-based
behavior detection. The present-age smartphone is exceptional with a cutting-edge processor, more
memory storage, a long-lasting battery, and highly effective underlying sensors. This gives a
chance to open up new areas of data mining for human activities recognition. The Internet of Things
is a quickly developing worldview for keen urban communities that gives a way of correspondence,
recognizable proof, and detecting capacities among actually circulated gadgets. With the
development of the Internet of Things (IoTs), client reliance on savvy frameworks and
administrations, for example, brilliant machines, cell phones, security, and medical services
applications, has been expanded. This requests secure verification instruments to save the clients'
protection while collaborating with savvy gadgets. In this paper, a framework has been proposed
for activities recognition and user authentication. We worked out with three publically available
datasets WISDM, MobiAct, and MobiAct_V2 for human activities recognition. For user
authentication, we used WISDM dataset. The various feature extraction tools have been explored
in this research. The performance of random forest and support vector machine classifiers was
outstanding on features extracted from the Matlab tool. The highest accuracy achieved for activities
recognition on WISDM, MobiAct, and MobiAct_v2 datasets was 99.50%, 98.50%, and 98.43%
respectively. For user authentication, maximum accuracy achieved by utilizing the Matlab features
was 95% on WISDM dataset. In comparison to current work for human activity identification and
user authentication, the findings showed that the proposed conspire performed better.
