REAL-TIME PATIENT ACTION DETECTION USING ML
Author(s):
Dr .Shabeen Taj G A, Shivukumar, Chandana H M, Anusha N, Surendra S
Keywords:
Human Action recognition, Classification, Radom Forest Model, Supervised Machine learning
Abstract
Action detection using machine learning (ML) is an emerging research area that involves automatically recognizing and localizing human actions in videos. In this approach, a model is trained on a large dataset of videos, which learns to identify patterns and features that are unique to each action. Once trained, the model can then be used to detect actions in new, unseen videos. The key challenge in action detection is to accurately identify the start and end times of each action, as well as distinguish between different actions that may occur simultaneously. In recent years, significant progress has been made in developing ML-based action detection algorithms, which have found applications in diverse fields, such as patient surveillance, sports analysis, and human-computer interaction. This project provides an overview of the state-of-the-art techniques in action detection using ML, including the datasets, models, and evaluation metrics commonly used in this field. Patient action detection using Machine Learning (ML) is a technique used to automatically recognize and categorize patient activities in healthcare settings. This technique utilizes sensor data from video cameras to detect activities such as Normal, headache, leg pain, and heart pain. ML algorithms are used to analyze this sensor data and recognize specific patterns associated with each activity. This approach can provide important information to healthcare providers, allowing them to monitor patient activities, assess their health status, and identify potential problems. In this abstract, we discuss the importance of patient action detection in healthcare, the challenges involved in implementing ML-based techniques, and the potential benefits of this approach.
Article Details
Unique Paper ID: 159625

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 632 - 636
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