Enhancing healthcare solutions with machine learning-driven adaptive haptic systems
(2025) p.171-184- Abstract
Adaptive haptics, an amalgamation of different haptic systems and machine learning algorithms, has the potential for intelligent feedback in healthcare. Such systems are capable of learning to suit individual user needs in real-time, thereby improving patient care, medical education, and treatment. Targeted techniques of machine learning approaches are reinforcement learning, neural networks, support vector machine, deep learning, etc. These include primary use cases like telemedicine, rehabilitation, and robotic surgery. Yet, barriers exist, including lack of data, limitations in processing in real-time and on various medical problems at scale. Federated learning and edge computing represent possible future research directions, and the... (More)
Adaptive haptics, an amalgamation of different haptic systems and machine learning algorithms, has the potential for intelligent feedback in healthcare. Such systems are capable of learning to suit individual user needs in real-time, thereby improving patient care, medical education, and treatment. Targeted techniques of machine learning approaches are reinforcement learning, neural networks, support vector machine, deep learning, etc. These include primary use cases like telemedicine, rehabilitation, and robotic surgery. Yet, barriers exist, including lack of data, limitations in processing in real-time and on various medical problems at scale. Federated learning and edge computing represent possible future research directions, and the case for AI-haptic-based delivery should motivate their inclusion in personalized solutions for healthcare. Federated learning and edge computing could lay the groundwork for the increased coupling of haptic data with ML systems.
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- author
- Kumar, Sanjeev ; Tiwari, Geeta ; Sagar, Laxmi Kant ; Tiwari, Neeraj and Kumar, Krishna LU
- organization
- publishing date
- 2025-05-09
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Integrating AI With Haptic Systems for Smarter Healthcare Solutions
- pages
- 14 pages
- publisher
- IGI Global
- external identifiers
-
- scopus:105007488976
- ISBN
- 9798337323091
- 9798337323077
- DOI
- 10.4018/979-8-3373-2307-7.ch008
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025, IGI Global Scientific Publishing. All rights reserved.
- id
- 53f7005a-6a7a-4d36-8abf-1b647bcc3e7f
- date added to LUP
- 2025-12-22 14:25:03
- date last changed
- 2026-02-16 18:18:22
@inbook{53f7005a-6a7a-4d36-8abf-1b647bcc3e7f,
abstract = {{<p>Adaptive haptics, an amalgamation of different haptic systems and machine learning algorithms, has the potential for intelligent feedback in healthcare. Such systems are capable of learning to suit individual user needs in real-time, thereby improving patient care, medical education, and treatment. Targeted techniques of machine learning approaches are reinforcement learning, neural networks, support vector machine, deep learning, etc. These include primary use cases like telemedicine, rehabilitation, and robotic surgery. Yet, barriers exist, including lack of data, limitations in processing in real-time and on various medical problems at scale. Federated learning and edge computing represent possible future research directions, and the case for AI-haptic-based delivery should motivate their inclusion in personalized solutions for healthcare. Federated learning and edge computing could lay the groundwork for the increased coupling of haptic data with ML systems.</p>}},
author = {{Kumar, Sanjeev and Tiwari, Geeta and Sagar, Laxmi Kant and Tiwari, Neeraj and Kumar, Krishna}},
booktitle = {{Integrating AI With Haptic Systems for Smarter Healthcare Solutions}},
isbn = {{9798337323091}},
language = {{eng}},
month = {{05}},
pages = {{171--184}},
publisher = {{IGI Global}},
title = {{Enhancing healthcare solutions with machine learning-driven adaptive haptic systems}},
url = {{http://dx.doi.org/10.4018/979-8-3373-2307-7.ch008}},
doi = {{10.4018/979-8-3373-2307-7.ch008}},
year = {{2025}},
}