Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning

K M, Vamsikrishna and Dogra, D P and Desarkar, Maunendra Sankar (2016) Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning. IEEE Transactions on Biomedical Engineering, 63 (5). pp. 991-1001. ISSN 0018-9294

Full text not available from this repository. (Request a copy)


Physical rehabilitation supported by the computer- assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Desarkar, Maunendra SankarUNSPECIFIED
Item Type: Article
Additional Information: The authors would like to thank D. D. Chandra for his tremen- dous help during data collection and experiments.
Uncontrolled Keywords: Finger tracking, gesture recognition, human-computer interface, physical rehabilitation
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Library Staff
Date Deposited: 27 May 2016 06:41
Last Modified: 01 Sep 2017 11:28
Publisher URL: 10.1109/TBME.2015.2480881
OA policy:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 2413 Statistics for this ePrint Item