Position tracking of a passive rehabilitation robot

Wojewoda, Kazimierz K.; Culmer, Peter; Jackson, Andrew; Levesley, Martin

doi:10.1109/cyber.2016.7574785

Cited by 2 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly to [8], the results comparing the RMSE (Figure 7) have confirmed that utilizing the proposed fusion scheme gives more accurate results than if the webcam and optical sensors were used alone. In this work, the functionality of the hybrid position tracking system was extended to orientation tracking and likewise in the case of the position, the orientation tracking also benefits from the fusion scheme providing more accurate orientation results.…”

Section: Vdiscussionsupporting

confidence: 62%

“…Rehabilitation The presented work is a continuation of the work previously shown in [8], which proved that the 2D position tracking system fusing position estimates from two sensors, an optical mouse sensor and a webcam, can estimate the position with greater accuracy than would be possible using each sensor alone. In this paper, the functionality of the hybrid tracking system [8] is extended by including orientation tracking.…”

Section: Hybrid Position and Orientation Tracking For A Passivementioning

confidence: 57%

“…Also, compared to the results in [8], the fusion trajectory RMS error average has improved from 6.8 ±3.4mm to 2.8 ±2.6mm, even though the webcam frame rate was lowered from 10 Hz to 5 Hz. This is due to a new calibration method being employed.…”

Section: Vdiscussionmentioning

confidence: 93%

“…This is due to a new calibration method being employed. It can also be noted that the average RMS error for the optical sensors-tracked trajectory has decreased compared to the optical tracked trajectory average RMS error in [8], which is difficult to explain. However, it might be caused the fact that in this study two optical sensors separated by a distance of 180mm were used together, compared to only one sensor used in [8].…”

Section: Vdiscussionmentioning

confidence: 99%

“…In this paper, the functionality of the hybrid tracking system [8] is extended by including orientation tracking. The tracking system introduced in [8] could not track rotation and could not function properly if rotation of the tracked object occurred.…”

Section: Hybrid Position and Orientation Tracking For A Passivementioning

confidence: 99%

See 4 more Smart Citations

Hybrid position and orientation tracking for a passive rehabilitation table-top robot

Wojewoda

Culmer

Gallagher

et al. 2017

2017 International Conference on Rehabilitation Robotics (ICORR)

View full text Add to dashboard Cite

Abstract-This paper presents a real time hybrid 2D position and orientation tracking system developed for an upper limb rehabilitation robot. Designed to work on a table-top, the robot is to enable home-based upper-limb rehabilitative exercise for stroke patients. Estimates of the robot's position are computed by fusing data from two tracking systems, each utilizing a different sensor type: laser optical sensors and a webcam. Two laser optical sensors are mounted on the underside of the robot and track the relative motion of the robot with respect to the surface on which it is placed. The webcam is positioned directly above the workspace, mounted on a fixed stand, and tracks the robot's position with respect to a fixed coordinate system. The optical sensors sample the position data at a higher frequency than the webcam, and a position and orientation fusion scheme is proposed to fuse the data from the two tracking systems. The proposed fusion scheme is validated through an experimental set-up whereby the rehabilitation robot is moved by a humanoid robotic arm replicating previously recorded movements of a stroke patient. The results prove that the presented hybrid position tracking system can track the position and orientation with greater accuracy than the webcam or optical sensors alone. The results also confirm that the developed system is capable of tracking recovery trends during rehabilitation therapy.

show abstract

Section: Vdiscussionsupporting

confidence: 62%

Section: Hybrid Position and Orientation Tracking For A Passivementioning

confidence: 57%

Section: Vdiscussionmentioning

confidence: 93%

Section: Vdiscussionmentioning

confidence: 99%

Section: Hybrid Position and Orientation Tracking For A Passivementioning

confidence: 99%

See 3 more Smart Citations

Hybrid position and orientation tracking for a passive rehabilitation table-top robot

Wojewoda

Culmer

Gallagher

et al. 2017

2017 International Conference on Rehabilitation Robotics (ICORR)

View full text Add to dashboard Cite

show abstract

Multimodal Sensing in Stroke Motor Rehabilitation

Zhao

Wang

et al. 2023

Advanced Sensor Research

View full text Add to dashboard Cite

Applying sensors in biomedical institutions and home‐based stroke rehabilitation is now a global research focus. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is detailed explained. The review starts by interpreting how stroke influences motion abilities and then introduces broadly adopted physical training methods. After, the working principles of sensors and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed. The content of the paper aims to not only review the state‐of‐the‐art works of developing sensors for assisting in evaluating stroke patients’ status but also bridging the gap between medical staff and engineers.

show abstract

Position tracking of a passive rehabilitation robot

Cited by 2 publications

References 11 publications

Hybrid position and orientation tracking for a passive rehabilitation table-top robot

Hybrid position and orientation tracking for a passive rehabilitation table-top robot

Multimodal Sensing in Stroke Motor Rehabilitation

Contact Info

Product

Resources

About