Next Article in Journal
Scene-Level Geographic Image Classification Based on a Covariance Descriptor Using Supervised Collaborative Kernel Coding
Next Article in Special Issue
Visual EKF-SLAM from Heterogeneous Landmarks
Previous Article in Journal
Mechanical Characterization of Hybrid Vesicles Based on Linear Poly(Dimethylsiloxane-b-Ethylene Oxide) and Poly(Butadiene-b-Ethylene Oxide) Block Copolymers
Previous Article in Special Issue
A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(3), 395; doi:10.3390/s16030395

Fusion of Haptic and Gesture Sensors for Rehabilitation of Bimanual Coordination and Dexterous Manipulation

1
Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin 300353, China
2
Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300353, China
3
Rehabilitation Center, Tianjin Hospital, Tianjin 300211, China
*
Author to whom correspondence should be addressed.
Academic Editor: Yajing Shen
Received: 1 January 2016 / Revised: 10 March 2016 / Accepted: 11 March 2016 / Published: 18 March 2016
(This article belongs to the Special Issue Sensors for Robots)
View Full-Text   |   Download PDF [7118 KB, uploaded 18 March 2016]   |  

Abstract

Disabilities after neural injury, such as stroke, bring tremendous burden to patients, families and society. Besides the conventional constrained-induced training with a paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fitting well with the fact that both arms are needed in common activities of daily living (ADLs), and can promote good functional recovery. In this work, the fusion of a gesture sensor and a haptic sensor with force feedback capabilities has enabled a bilateral rehabilitation training therapy. The Leap Motion gesture sensor detects the motion of the healthy hand, and the omega.7 device can detect and assist the paretic hand, according to the designed cooperative task paradigm, as much as needed, with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data facilitate instantaneous visual and audio feedback, as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm recruits the sensory, motor and cognitive aspects of the patient into one loop, encourages the active involvement of the patients into rehabilitation training, strengthens the cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy of use at home or centralized institutions and, thus, promises effective potentials for rehabilitation training. View Full-Text
Keywords: haptics; rehabilitation; bimanual coordination; dexterous manipulation; sensory-motor cognitive skills haptics; rehabilitation; bimanual coordination; dexterous manipulation; sensory-motor cognitive skills
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yu, N.; Xu, C.; Li, H.; Wang, K.; Wang, L.; Liu, J. Fusion of Haptic and Gesture Sensors for Rehabilitation of Bimanual Coordination and Dexterous Manipulation. Sensors 2016, 16, 395.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top