Machine Learning Model for Human Activity Analysis

Alia Rifat, Pratiksha Pradip Pandao and B. Shoban Babu
Additional contact information
Alia Rifat: LeenaBOT Robotics pvt Ltd, India
Pratiksha Pradip Pandao: LeenaBOT Robotics pvt Ltd, India
B. Shoban Babu: SV Engineering College Tirupati, India

European Journal of Information Technologies and Computer Science, 2022, vol. 2, issue 1, 5-9

Abstract: Human Activity Recognition is an active subject of research and scientific progress in which several models have been presented for identifying and categorizing activities using Machine Learning utilizing various methodologies. The purpose of human activity recognition is to look at activities in video or still photos. Human activity recognition systems are motivated by this fact, and their goal is to appropriately classify input data into its underlying activity category. Human activities are classified as (a) gestures, (b) atomic actions, (c) human-to-object or human-to-human interactions, (d) collective actions, (e) behaviors, and (f) events, depending on their complexity. Today, health informatics is a critical field for improving healthcare efficiency by streamlining the collecting, storage, and retrieval of critical patient health data. In this paper, an intelligent smart healthcare system is provided that uses machine learning approaches to deliver ubiquitous human activity recognition (HAR) in an automated manner. The goal is to model and recognize activities of everyday living in a precise and efficient manner. Furthermore, for HAR purposes, we focus on a dataset collecting body motion and vital sign recordings from volunteers of various profiles while performing various physical activities. This research has demonstrated that identifying human activity from sensor data is extremely difficult, even with the availability of a number of machine learning approaches. When it comes to machine learning techniques, there is no one-size-fits-all approach.

Keywords: IoT; LeenaBOT; machine learning; smart healthcare (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/compute/article/view/10042 Abstract page (text/html)
https://eu-opensci.org/index.php/compute/article/download/10042/1790 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:comput:v:2:y:2022:i:1:id:10042

DOI: 10.24018/compute.2022.2.1.42

Access Statistics for this article

More articles in European Journal of Information Technologies and Computer Science from European Open Science
Bibliographic data for series maintained by Support Team ().