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A segmented-mean feature extraction method for glove-based system to enhance physiotherapy for accurate and speedy recuperation of limbs

Samraj, A., Rajendran, K., Palaniappan, Ramaswamy (2016) A segmented-mean feature extraction method for glove-based system to enhance physiotherapy for accurate and speedy recuperation of limbs. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). . pp. 1983-1988. IEEE ISBN 978-1-5090-2028-7. (doi:10.1109/ICACCI.2016.7732342) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/ICACCI.2016.7732342

Abstract

It is always desirable to have an accurate system that allows fast recovery of patients undergoing physiotherapy in terms of integrated health and cost benefits. The caregivers and medical personnel too gain a lot of expertise through the innovations involved in treatment methodology. This system proposed here was developed successfully with a straightforward Segmented-Mean feature construction method that enables its portability to suit smart biomedical devices. In this work, four different exercises were completed by four different subjects in two sessions and the feedback system was generated from every single trial performance via a visual display in a smart phone. The accuracy of the system's output depends on the precise representation of two important things namely, correct gesture and timings. These two parameters have to be captured from the signals that are generated by the hand glove during the manual physiotherapy as guided by the experts during the teaching (i.e. Training) phase. Any deviation from the model should also be captured and reflected in the feedback to align the physio-movements towards perfection to minimise adverse effects. So the feature has to be constructed with complete representation and obviously, as fast as possible. The proposed Segmented-Mean method calculates the mean of data that arrives from the significant electrodes periodically, thus preserving the performance of the subject and is found suitable in estimating the enactment of exercises and required deviations (if any), accurately and as appropriate. The proposed Segmented-Mean method helps the construction of features easily than other conventional methods by reducing the computational complexity and therefore, the response time. Hence, shifting the importance to actual physiotherapy monitoring with an accurate system that works on simple feature construction made feasible.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/ICACCI.2016.7732342
Additional information: Unmapped bibliographic data: C7 - 7732342 [EPrints field already has value set] LA - English [Field not mapped to EPrints] J2 - Int. Conf. Adv. Comput., Commun. Inf., ICACCI [Field not mapped to EPrints] AD - Department of Information Technology, Mahendra Engineering College, India [Field not mapped to EPrints] AD - Enability Foundation for Rehabilitation, India [Field not mapped to EPrints] AD - University of Kent, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] A4 - [Field not mapped to EPrints] C3 - 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016 [Field not mapped to EPrints]
Uncontrolled keywords: Assistive Technology, Bionics, Data Glove, Gesture Computing, Wearable Computing, Biomedical signal processing, Biomimetics, Bionics, Feature extraction, Information science, Neonatal monitoring, Patient rehabilitation, Smartphones, Wearable technology, Assistive technology, Biomedical devices, Conventional methods, Data glove, Feature construction, Gesture Computing, Medical personnel, Wearable computing, Physical therapy
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 21:04 UTC
Last Modified: 30 May 2019 08:28 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70677 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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