A rehabilitation training system with double-CCD camera and automatic spatial positioning technique

Lin, Chern‐Sheng; Wei, Tzu-Chi; Lu, An-Tsung; Hung, San-Shan; Chen, Wei-Lung; Chang, Chia-Chang

doi:10.1016/j.optlaseng.2010.11.019

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…To increase the reliability of such systems, some researchers have chosen to work with a multi-camera system. For instance, Lin et al [12] employed a double CCD camera to capture the motion during rehabilitation sessions. In [13], the authors made use of a stereo-vision system in order to detect and assess the human motor reactivity under stimulation.…”

Section: Introductionmentioning

confidence: 99%

Home self-training: Visual feedback for assisting physical activity for stroke survivors

Baptista

Ghorbel

Shabayek

et al. 2019

Computer Methods and Programs in Biomedicine

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Background and Objective: With the increase in the number of stroke survivors, there is an urgent need for designing appropriate home-based rehabilitation tools to reduce health-care costs. The objective is to empower the rehabilitation of post-stroke patients at the comfort of their homes by supporting them while exercising without the physical presence of the therapist. Methods: A novel low-cost home-based training system is introduced. This system is designed as a composition of two linked applications: one for the therapist and another one for the patient. The therapist prescribes personalized exercises remotely, monitors the home-based training and re-adapts the exercises if required. On the other side, the patient loads the prescribed exercises, trains the prescribed exercise while being guided by color-based visual feedback and gets updates about the exercise performance. To achieve that, our system provides three main functionalities, namely: 1) Feedback proposals guiding a personalized exercise session, 2) Posture monitoring optimizing the effectiveness of the session, 3) Assessment of the quality of the motion. Results: The proposed system is evaluated on 10 healthy participants without any previous contact with the system. To analyze the impact of the feedback proposals, we carried out two different experimental sessions: without and with feedback proposals. The obtained results give preliminary assessments about the interest of using such feedback. Conclusions: Obtained results on 10 healthy participants are promising. This encourages to test the system in a realistic clinical context for the rehabilitation of stroke survivors.

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Section: Introductionmentioning

confidence: 99%

Home self-training: Visual feedback for assisting physical activity for stroke survivors

Baptista

Ghorbel

Shabayek

et al. 2019

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

show abstract

“…The gesture control function and command system enable users to operate the system without wearing complicated and heavy devices [2,3]. When multiple sensors and cameras were used for positioning, these cameras were set in a sequential group to reduce the complexity of the adjustments and locate the target object at any time [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

The Manipulation of Real-Time Kinect-Based Robotic Arm Using Double-Hand Gestures

et al. 2020

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This study focused on utilizing the Kinect depth sensor to track double-hand gestures and control a real-time robotic arm. The control system is mainly composed of the microprocessor, a color camera, the depth sensor, and the robotic arm. The Kinect depth sensor was used to take photos of the human body to analyze the skeleton of a human body and obtain the relevant information. Such information was used to identify the gestures of the left hand and the left palm of the user. The gesture of left hand was used as an input command device. The gesture of the right hand was used for imitation movement teaching of robotic arm. From the depth sensor, the real-time images of the human body and the deep information of each joint were collected and converted to the relative positions of the robotic arm. Combining forward kinematics and inverse kinematics and D-H link, the gesture information of the right hand was calculated, which was converted via coordinates into each angle of the motor of the robotic arm. From the color camera, when the left palm was not detected, the user could simply use the right hand to control the action and movement of the real-time robotic arm. When the left palm was detected and 5 fingertips were identified, it meant the start of recording the real-time imitation movement of the robotic arm by the right hand. When 0 fingertip was identified, it meant the stoppage of the above recording. When 2 fingertips were identified, the user could not only control the real-time robotic arm but also repeat the recorded actions.

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Ground Experiment Development for Acquiring, Tracking and Pointing of Small Space Debris

Zhang

Guan²,

Zhao

et al. 2019

J of Astronaut Sci

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A rehabilitation training system with double-CCD camera and automatic spatial positioning technique

Cited by 10 publications

References 14 publications

Home self-training: Visual feedback for assisting physical activity for stroke survivors

Home self-training: Visual feedback for assisting physical activity for stroke survivors

The Manipulation of Real-Time Kinect-Based Robotic Arm Using Double-Hand Gestures

Ground Experiment Development for Acquiring, Tracking and Pointing of Small Space Debris

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