A facial expression controlled wheelchair for people with disabilities

Rabhi, Yassine; Mrabet, Makrem; Fnaiech, Farhat

doi:10.1016/j.cmpb.2018.08.013

Cited by 62 publications

(29 citation statements)

References 35 publications

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“…Fernandes et al [16] present an interesting review of recent technologies for the special assistance and orientation of people who are visually impaired. Rabhi et al [17] propose performing recognition of facial expressions and using them as command modes for wheelchair control, where they use neural networks programmed into Raspberry Pi and an application installed on a Smartphone to capture the image and human-machine interaction, the results obtained range from 95% to 97.1% effective. Nandini and Seeja [18] present a new algorithm for easy navigation in a supermarket for visually impaired people, the results show that the algorithm was able to find the optimal path avoiding obstacles and minimizing distance and turns in the route.…”

Section: Introductionmentioning

confidence: 99%

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Márquez‐Olivera

Juárez-Gracia

Hernández-Herrera

et al. 2019

Sensors

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Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence which learns the face of the people with whom the user interacts daily. During the study we propose a new hybrid model of Alpha-Beta Associative memories (Amαβ) with Correlation Matrix (CM) and K-Nearest Neighbors (KNN), where the Amαβ-CMKNN was trained with characteristic biometric vectors generated from images of faces from people who present different facial expressions such as happiness, surprise, anger and sadness. To test the performance of the hybrid model, two experiments that differ in the selection of parameters that characterize the face are conducted. The performance of the proposed model was tested in the databases CK+, CAS-PEAL-R1 and Face-MECS (own), which test the Amαβ-CMKNN with faces of subjects of both sexes, different races, facial expressions, poses and environmental conditions. The hybrid model was able to remember 100% of all the faces learned during their training, while in the test in which faces are presented that have variations with respect to those learned the results range from 95.05% in controlled environments and 86.48% in real environments using the proposed integrated system.

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Section: Introductionmentioning

confidence: 99%

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Márquez‐Olivera

Juárez-Gracia

Hernández-Herrera

et al. 2019

Sensors

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show abstract

“…Some other recent approaches incorporated voice commands and vocal feedbacks to share control decisions with the user such as obstacle avoidance, safe approach to objects, navigation through a specific path, and learn from these decisions [30]. For patients who cannot manipulate a standard joystick, many developments have been carried out to translate users' face and body gestures and eye movements to control commands for the wheelchair through visual feedbacks [31,32]. The same approach of gesture classification and recognition is carried out through gathering surface electromyography (EMG) [33,34] and electroencephalography (EEG) [35•] bio-signals.…”

Section: Assistive Mobile Robotsmentioning

confidence: 99%

Human-Robot Interaction in Rehabilitation and Assistance: a Review

Mohebbi

2020

Curr Robot Rep

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Purpose of Review Research in assistive and rehabilitation robotics is a growing, promising, and challenging field emerged due to various social and medical needs such as aging populations, neuromuscular, and musculoskeletal disorders. Such robots can be used in various day-today scenarios or to support motor functionality, training, and rehabilitation. This paper reflects on the human-robot interaction perspective in rehabilitation and assistive robotics and reports on current issues and developments in the field. Recent Findings The survey on the literature reveals that new efforts are put on utilizing machine learning approaches alongside novel developments in sensing technology to adapt the systems with user routines in terms of activities for assistive systems and exercises for rehabilitation devices to fit each user's need and maximize their effectiveness. Summary A review of recent research and development efforts on human-robot interaction in assistive and rehabilitation robotics is presented in this paper. First, different subdomains in assistive and rehabilitation robotic research are identified, and accordingly, a survey on the background and trends of such developments is provided.

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“…User’s voice [ 30 ] and facial expression [ 31 ]-based wheelchair control systems were explored by different groups. However, voice control is laborious for the user and sound waves interference or noisy environment distractions can be introduced to the system establishing undesired commands.…”

Section: Background and Related Workmentioning

confidence: 99%

An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control

Dahmani

Chowdhury

Khandakar

et al. 2020

Sensors

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In the 34 developed and 156 developing countries, there are ~132 million disabled people who need a wheelchair, constituting 1.86% of the world population. Moreover, there are millions of people suffering from diseases related to motor disabilities, which cause inability to produce controlled movement in any of the limbs or even head. This paper proposes a system to aid people with motor disabilities by restoring their ability to move effectively and effortlessly without having to rely on others utilizing an eye-controlled electric wheelchair. The system input is images of the user’s eye that are processed to estimate the gaze direction and the wheelchair was moved accordingly. To accomplish such a feat, four user-specific methods were developed, implemented, and tested; all of which were based on a benchmark database created by the authors. The first three techniques were automatic, employ correlation, and were variants of template matching, whereas the last one uses convolutional neural networks (CNNs). Different metrics to quantitatively evaluate the performance of each algorithm in terms of accuracy and latency were computed and overall comparison is presented. CNN exhibited the best performance (i.e., 99.3% classification accuracy), and thus it was the model of choice for the gaze estimator, which commands the wheelchair motion. The system was evaluated carefully on eight subjects achieving 99% accuracy in changing illumination conditions outdoor and indoor. This required modifying a motorized wheelchair to adapt it to the predictions output by the gaze estimation algorithm. The wheelchair control can bypass any decision made by the gaze estimator and immediately halt its motion with the help of an array of proximity sensors, if the measured distance goes below a well-defined safety margin. This work not only empowers any immobile wheelchair user, but also provides low-cost tools for the organization assisting wheelchair users.

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A facial expression controlled wheelchair for people with disabilities

Cited by 62 publications

References 35 publications

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Human-Robot Interaction in Rehabilitation and Assistance: a Review

An Intelligent and Low-Cost Eye-Tracking System for Motorized Wheelchair Control

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