Advancements in Activity Recognition Technologies

Abstract

In today's data-rich era, marked by the
widespread use of data collection tools like
smartphones and video cameras, Human
Activity Recognition (HAR) has become a
dynamic and versatile field with numerous
practical applications. The fusion of these
devices with artificial intelligence (AI) has
revolutionized our ability to detect and
understand human activities with
unparalleled precision, revealing hidden
insights.
The proliferation of electronic gadgets
equipped with sensors, cameras,
accelerometers, and gyroscopes has
significantly expanded the scope of HAR.
These devices serve as the bedrock for
collecting intricate data on human movements
and behaviours. Simultaneously, AI
advancements empower us to process and
interpret this data accurately, providing
profound insights into various human
activities.
HAR hinges on three interconnected pillars:
data acquisition devices, AI algorithms, and
diverse applications that leverage their
synergy. This paper conducts a
comprehensive exploration of these pillars,
drawing insights from extensive literature
and real-world datasets encompassing various
contexts and data sources.
Notably, neural networks play a pivotal role
in advancing HAR techniques. These AIdriven algorithms are the cornerstone of
precise activity recognition. The paper delves
into the intricacies of neural networks,
shedding light on their ability to reveal
concealed information and enable nuanced
activity interpretation.
Throughout our exploration, we confront the
challenges confronting HAR, including data
pre-processing, noise reduction, model
optimization, and generalization. Addressing
these challenges is essential for enhancing the
accuracy and applicability of HAR methods.