A facial expression controlled wheelchair for people with disabilities.

BACKGROUND AND OBJECTIVES: In order to improve assistive technologies for people with reduced mobility, this paper develops a new intelligent real-time emotion detection system to control equipment, such as electric wheelchairs (EWC) or robotic assistance vehicles. Every year, degenerative diseases and traumas prohibit thousands of people to easily control the joystick of their wheelchairs with their hands. Most current technologies are considered invasive and uncomfortable such as those requiring the user to wear some body sensor to control the wheelchair. METHODS: In this work, the proposed Human Machine Interface (HMI) provides an efficient hands-free option that does not require sensors or objects attached to the user's body. It allows the user to drive the wheelchair using its facial expressions which can be flexibly updated. This intelligent solution is based on a combination of neural networks (NN) and specific image preprocessing steps. First, the Viola-Jones combination is used to detect the face of the disability from a video. Subsequently, a neural network is used to classify the emotions displayed on the face. This solution called "The Mathematics Behind Emotion" is capable of classifying many facial expressions in real time, such as smiles and raised eyebrows, which are translated into signals for wheelchair control. On the hardware side, this solution only requires a smartphone and a Raspberry Pi card that can be easily mounted on the wheelchair. RESULTS: Many experiments have been conducted to evaluate the efficiency of the control acquisition process and the user experience in driving a wheelchair through facial expressions. The classification accuracy can expect 98.6% and it can offer an average recall rate of 97.1%. Thus, all these experiments have proven that the proposed system is able of accurately recognizing user commands in real time. Indeed, the obtained results indicate that the suggested system is more comfortable and better adapted to severely disabled people in their daily lives, than conventional methods. Among the advantages of this system, we cite its real time ability to identify facial emotions from different angles. CONCLUSIONS: The proposed system takes into account the patient's pathology. It is intuitive, modern, doesn't require physical effort and can be integrated into a smartphone or tablet. The results obtained highlight the efficiency and reliability of this system, which ensures safe navigation for the disabled patient.

Development of a New Intelligent Joystick for People with Reduced Mobility.

Despite the diversity of electric wheelchairs, many people with physical limitations and seniors have difficulty using their standard joystick. As a result, they cannot meet their needs or ensure safe travel. Recent assistive technologies can help to give them autonomy and independence. This work deals with the real-time implementation of an artificial intelligence device to overcome these problems. Following a review of the literature from previous work, we present the methodology and process for implementing our intelligent control system on an electric wheelchair. The system is based on a neural algorithm that overcomes problems with standard joystick maneuvers such as the inability to move correctly in one direction. However, this implies the need for an appropriate methodology to map the position of the joystick handle. Experiments on a real wheelchair are carried out with real patients of the Mohamed Kassab National Institute Orthopedic, Physical and Functional Rehabilitation Hospital of Tunis. The proposed intelligent system gives good results compared to the use of a standard joystick.

Intelligent Control Wheelchair Using a New Visual Joystick.

A new control system of a hand gesture-controlled wheelchair (EWC) is proposed. This smart control device is suitable for a large number of patients who cannot manipulate a standard joystick wheelchair. The movement control system uses a camera fixed on the wheelchair. The patient's hand movements are recognized using a visual recognition algorithm and artificial intelligence software; the derived corresponding signals are thus used to control the EWC in real time. One of the main features of this control technique is that it allows the patient to drive the wheelchair with a variable speed similar to that of a standard joystick. The designed device "hand gesture-controlled wheelchair" is performed at low cost and has been tested on real patients and exhibits good results. Before testing the proposed control device, we have created a three-dimensional environment simulator to test its performances with extreme security. These tests were performed on real patients with diverse hand pathologies in Mohamed Kassab National Institute of Orthopedics, Physical and Functional Rehabilitation Hospital of Tunis, and the validity of this intelligent control system had been proved.