Development of computer vision based obstacle detection and human tracking on smart wheelchair for disabled patient

Utaminingrum, Fitri; Fauzi, Muhammad Ali; Wihandika, Randy Cahya; Adinugroho, Sigit; Kurniawan, Tri Astoto; Syauqy, Dahnial; Sari, Yuita Arum; Adikara, Putra Pandu

doi:10.1109/iscbi.2017.8053533

Cited by 17 publications

(3 citation statements)

References 19 publications

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“…Özcelikörs [ 17 ] and Mittal [ 18 ] used the Kinect camera system in order to obtain a 3D map of the frontal obstacles, as well as their size and position. Other approaches use video processing, whether in mono- or stereovision, in order to detect particular objects or dangerous situations [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a New Negative Obstacle Sensor for Augmented Electric Wheelchair

Favey

Farcy

Donnez

et al. 2021

Sensors

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Due to pathologies or age-related problems, in some disabled people, motor impairment is associated with cognitive and/or visual impairments. This combination of limitations unfortunately leads to an inability to move around independently. Indeed, their situation does not allow them to use a conventional electric wheelchair, for safety reasons, and for the moment there is no other technological solution providing safe movement capacity. This lack of access to an autonomous travel solution has the consequence of weakening the intellectual, personal, social, cultural and moral development, as well as the life expectancy, of the people concerned. In this context, our team is working on the development of an optoelectronic system that secures the displacement of electric wheelchairs. This is a large project that requires the development of several functionalities such as: the anti-collision of the wheelchair with its environment, the prevention of falls from the wheelchair on uneven levels, and the adaptation of the system mechanically and electronically to the majority of commercially available electric wheelchair models, among others. In this article, we introduce our solution for detecting dangerous height differences, also called “negative obstacles”, through the creation of a dedicated sensor. This sensor works by optical triangulation and can embed several laser beams in order to extend its detection zone. It has the particularity of being robust in direct sunlight and rain and has a sufficiently high measurement rate to be suitable for the displacement of electric wheelchairs. We develop an adapted algorithm, and point out compromises, in particular between the orientation of the laser beams and the maximal speed of the wheelchair.

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Section: Introductionmentioning

confidence: 99%

Development of a New Negative Obstacle Sensor for Augmented Electric Wheelchair

Favey

Farcy

Donnez

et al. 2021

Sensors

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“…But attempts to build smart wheelchairs mostly lack robustness in motion control, perception and control techniques. This research work describes the development of a smart wheelchair integrated with a HMI and its interaction between sensory feedback and the computer control system [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Human-Machine Interface for a Smart Wheelchair

Hartman

Nandikolla

2019

Journal of Robotics

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The paper describes the integration of hardware and software with sensor technology and computer processing to develop the next generation intelligent wheelchair. The focus is a computer cluster design to test high performance computing for smart wheelchair operation and human interaction. The LabVIEW cluster is developed for real-time autonomous path planning and sensor data processing. Four small form factor computers are connected over a Gigabit Ethernet local area network to form the computer cluster. Autonomous programs are distributed across the cluster for increased task parallelism to improve processing time performance. The distributed programs operating frequency for path planning and motion control is 50Hz and 12.3Hz for 0.3 megapixel robot vision system. To monitor the operation and control of the distributed LabVIEW code, network automation is integrated into the cluster software along with a performance monitor. A link between the computer motion control program and the wheelchair joystick control of the drive train is developed for the computer control interface. A perception sensor array and control circuitry is integrated with the computer system to detect and respond to the wheelchair environment. Multiple cameras are used for image processing and scanning laser rangefinder sensors for obstacle avoidance in the cluster program. A centralized power system is integrated to power the smart wheelchair along with the cluster and sensor feedback system. The on board computer system is evaluated for cluster processing performance for the smart wheelchair, incorporating camera machine vision and LiDAR perception for terrain obstacle detection, operating in urban scenarios.

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“…Even more the worst case when the disabilities user have no arm anymore. Research by Utaminingrum et al [7] also develop an automatic smart wheelchair that can run automatically by detecting obstacles and tracking people in front of it. But their research did not accommodate control freely for disability user.…”

Section: Introductionmentioning

confidence: 99%

Eye State Recognition Using Multiple Methods for Applied to Control Smart Wheelchair

Pangestu¹,

Utaminingrum²,

Bachtiar³

2019

IJIES

Self Cite

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People with multi-disability order need more handling than normal people. Smart wheelchair also developed with conventional mechanism that has more improvement in any aspect such as automatic system until control system. We develop a way for controlling smart wheelchair with movement of eyelid. In other ways, our system have capability to recognize state of the eye whether the eyes in opened or closed state. Opened and closed state in the eye can made a benchmark to control a wheelchair. In our research, we develop a system to recognize whether eye in the opened or closed state by using Multiple Method consisting of Haar cascade to detect eye location, Vertical Image Projection (VIP) to extract the feature and Learning Vector Quantization (LVQ) Neural Networks to recognize state of the eye. Our method produce an accuracy until 90.32% to recognize eye state. This approach can be used as an alternate way to control movement such go forward or stop by using their eyelid movement in smart wheelchair for disabled people.

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Development of computer vision based obstacle detection and human tracking on smart wheelchair for disabled patient

Cited by 17 publications

References 19 publications

Development of a New Negative Obstacle Sensor for Augmented Electric Wheelchair

Development of a New Negative Obstacle Sensor for Augmented Electric Wheelchair

Human-Machine Interface for a Smart Wheelchair

Eye State Recognition Using Multiple Methods for Applied to Control Smart Wheelchair

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