Experimental Performance Evaluation of Computer Vision for an Upper Limbs Rehabilitation Robot

Rijanto, Estiko; Adiwiguna, Erik; Rozaqi, Latif; Sadono, Aryo Putro; Nugraha, M. Hafil

doi:10.1109/ic3ina48034.2019.8949599

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…The authors also stated that there are robotic systems developed to assist the combination of the previously mentioned movements [97]. There is also research that utilizes different approaches with respect to upper limb rehabilitation robots, such as implementing visual and auditory feedback [98] and computer vision [99] along with robotics. The upper-limb rehabilitation robots are divided into primarily two classes, exoskeletons and end-effector-based rehabilitation robots [100][101][102].…”

Section: Upper Limb Rehabilitationmentioning

confidence: 99%

A Survey of Robots in Healthcare

et al. 2021

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In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society.

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Section: Upper Limb Rehabilitationmentioning

confidence: 99%

A Survey of Robots in Healthcare

et al. 2021

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“…On computer device controls, such as cursor controls, mouse, keyboards, and monitors [17], [18]. In the field of robotics [19]- [21] and so on.…”

Section: Introductionmentioning

confidence: 99%

Application of Computer Vision for Real-Time Detection of Fruit Color and Size in Fruit Sorter

Aryeni,

Maulidiah,

Toar

et al. 2023

JAEE

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Computer vision aims to build a computer that can see like humans. Humans can immediately recognize and define an object after seeing and recording an object. This is different from computer visual systems, where camera recordings cannot be directly translated, defined, and recognized by computers, therefore digital image processing is needed first. In this study, computer vision technology was used to detect fruit based on color, size, and shape in real time. The fruit is placed on a conveyor belt, then the fruit object is captured by a webcam using object tracking. Computer vision algorithms and programs can detect fruit objects and recognize ripe and unripe fruits by converting RGB (Red, Green, Blue) colors into HSV (Hue, Saturation, Value) for the color segmentation process. After the detection process, a selector is placed at the end of the conveyor which is used to sort the fruit into 2 categories, namely ripe and unripe. In addition, this study also determines the size and shape of the fruit. From design, realization, and testing, it was found that the success rate of detecting ripe fruit was 97.33% and unripe fruit was 93.33%. To get maximum results, it needs to be supported by room lighting settings that are kept constant.

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Enabling Contact-free Acoustic Sensing under Device Motion

Liu

Wang

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Recent years have witnessed increasing attention from both academia and industry on contact-free acoustic sensing. Due to the pervasiveness of audio devices and fine granularity of acoustic sensing, it has been applied in numerous fields, including human-computer interaction and contact-free health sensing. Though promising, the limited working range hinders the wide adoption of acoustic sensing in real life. To break the sensing range limit, we propose to deploy the acoustic device on a moving platform (i.e., a robot) to support applications that require larger coverage and continuous sensing. In this paper, we propose SonicBot, a system that enables contact-free acoustic sensing under device motion. We propose a sequence of signal processing schemes to eliminate the impact of device motion and then obtain clean target movement information that is previously overwhelmed by device movement. We implement SonicBot using commercial audio devices and conduct extensive experiments to evaluate the performance of the proposed system. Experiment results show that our system can achieve a median error of 1.11 cm and 1.31 mm for coarse-grained and fine-grained tracking, respectively. To showcase the applicability of our proposed system in real-world settings, we perform two field studies, including coarse-grained gesture sensing and fine-grained respiration monitoring when the acoustic device moves along with a robot.

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Experimental Performance Evaluation of Computer Vision for an Upper Limbs Rehabilitation Robot

Cited by 3 publications

References 12 publications

A Survey of Robots in Healthcare

A Survey of Robots in Healthcare

Application of Computer Vision for Real-Time Detection of Fruit Color and Size in Fruit Sorter

Enabling Contact-free Acoustic Sensing under Device Motion

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