Experimental Verification of Force Feedback for Rehabilitation Robot

More, Marcel; Liška, Ondřej; Kováč, Juraj

doi:10.4028/www.scientific.net/jera.18.123

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…As has been stated above, Data Matrix codes can be used to label logistic units, parts, warehousing positions, but also to navigate automated robots in production engineering [3,6,17,20].…”

Section: Resultsmentioning

confidence: 99%

Recognition of Data Matrix Codes in Images and their Applications in Production Processes

Karrach

Pivarčiová

2020

Management Systems in Production Engineering

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Data Matrix codes can be a significant factor in increasing productivity and efficiency in production processes. An important point in deploying Data Matrix codes is their recognition and decoding. In this paper is presented a computationally efficient algorithm for locating Data Matrix codes in the images. Image areas that may contain the Data Matrix code are to be identified firstly. To identify these areas, the thresholding, connected components labelling and examining outer bounding-box of the continuous regions is used. Subsequently, to determine the boundaries of the Data Matrix code more precisely, we work with the difference of adjacent projections around the Finder Pattern. The dimensions of the Data Matrix code are determined by analyzing the local extremes around the Timing Pattern. We verified the proposed method on a testing set of synthetic and real scene images and compared it with the results of other open-source and commercial solutions. The proposed method has achieved better results than competitive commercial solutions.

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“…As has been stated above, Data Matrix codes can be used to label logistic units, parts, warehousing positions, but also to navigate automated robots in production engineering [3,6,17,20].…”

Section: Resultsmentioning

confidence: 99%

Recognition of Data Matrix Codes in Images and their Applications in Production Processes

Karrach

Pivarčiová

2020

Management Systems in Production Engineering

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“…The third area is advertising and marketing actions. In production engineering, Data Matrix codes can be used to designate logistic units, parts, warehousing positions, but also to navigate automated robots [22,23,24,25]. Due to their low cost, accuracy, speed, reliability, flexibility and efficiency, as well as the ability to record large amounts of data on a small area, they still have a great advantage in industrial applications.…”

Section: Resultsmentioning

confidence: 99%

Options to Use Data Matrix Codes in Production Engineering

Karrach

Pivarčiová

2018

Management Systems in Production Engineering

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The paper deals with the possibilities of using Data Matrix codes in production engineering. We designed and tested the computationally efficient method for locating the Data Matrix code in the images. The location search procedure consists of identification of candidate regions using image binarization, then joining adjacent points into continuous regions and also examining outer boundaries of the regions. Afterwards we verify the presence of the Finder Pattern (as two perpendicular line segments) and Timing Pattern (as alternating sequence of black and white modules) in these candidate regions. Such procedure is invariant to shift rotation and scale change of Data Matrix codes. The method we have proposed has been verified on a set of real industrial images and compared to other commercial algorithms. We are also convinced that such technique is also suitable for real-time processing and has achieved better results than comparable commercial algorithms.

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“…Min et al [ 33 ] showed that in the rehabilitation training process, a combination of sensory stimulation, including visual and tactile stimulation, can provide timely and correct behavioural guidance and feedback for patients, which is conducive to reshaping the motor perception loop. The force-feedback hand rehabilitation robot used in this study provides force tactile stimulation and timely and effective sensorimotor information feedback to help patients better adjust their finger movements and complete grasping [ 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

Effect of task-oriented training assisted by force feedback hand rehabilitation robot on finger grasping function in stroke patients with hemiplegia: a randomised controlled trial

Li,

Lian,

Chen

et al. 2024

J NeuroEngineering Rehabil

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Background Over 80% of patients with stroke experience finger grasping dysfunction, affecting independence in activities of daily living and quality of life. In routine training, task-oriented training is usually used for functional hand training, which may improve finger grasping performance after stroke, while augmented therapy may lead to a better treatment outcome. As a new technology-supported training, the hand rehabilitation robot provides opportunities to improve the therapeutic effect by increasing the training intensity. However, most hand rehabilitation robots commonly applied in clinics are based on a passive training mode and lack the sensory feedback function of fingers, which is not conducive to patients completing more accurate grasping movements. A force feedback hand rehabilitation robot can compensate for these defects. However, its clinical efficacy in patients with stroke remains unknown. This study aimed to investigate the effectiveness and added value of a force feedback hand rehabilitation robot combined with task-oriented training in stroke patients with hemiplegia. Methods In this single-blinded randomised controlled trial, 44 stroke patients with hemiplegia were randomly divided into experimental (n = 22) and control (n = 22) groups. Both groups received 40 min/day of conventional upper limb rehabilitation training. The experimental group received 20 min/day of task-oriented training assisted by a force feedback rehabilitation robot, and the control group received 20 min/day of task-oriented training assisted by therapists. Training was provided for 4 weeks, 5 times/week. The Fugl-Meyer motor function assessment of the hand part (FMA-Hand), Action Research Arm Test (ARAT), grip strength, Modified Ashworth scale (MAS), range of motion (ROM), Brunnstrom recovery stages of the hand (BRS-H), and Barthel index (BI) were used to evaluate the effect of two groups before and after treatment. Results Intra-group comparison: In both groups, the FMA-Hand, ARAT, grip strength, AROM, BRS-H, and BI scores after 4 weeks of treatment were significantly higher than those before treatment (p < 0.05), whereas there was no significant difference in finger flexor MAS scores before and after treatment (p > 0.05). Inter-group comparison: After 4 weeks of treatment, the experimental group’s FMA-Hand total score, ARAT, grip strength, and AROM were significantly better than those of the control group (p < 0.05). However, there were no statistically significant differences in the scores of each sub-item of the FMA-Hand after Bonferroni correction (p > 0.007). In addition, there were no statistically significant differences in MAS, BRS-H, and BI scores (p > 0.05). Conclusion Hand performance improved in patients with stroke after 4 weeks of task-oriented training. The use of a force feedback hand rehabilitation robot to support task-oriented training showed additional value over conventional task-oriented training in stroke patients with hand dysfunction. Clinical trial registration information NCT05841108

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Experimental Verification of Force Feedback for Rehabilitation Robot

Cited by 4 publications

References 19 publications

Recognition of Data Matrix Codes in Images and their Applications in Production Processes

Recognition of Data Matrix Codes in Images and their Applications in Production Processes

Options to Use Data Matrix Codes in Production Engineering

Effect of task-oriented training assisted by force feedback hand rehabilitation robot on finger grasping function in stroke patients with hemiplegia: a randomised controlled trial

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