Assessment of a navigation assistance system for power wheelchair

Babel, Marie; Pasteau, François; Fraudet, Bastien; Achille, S.; Colin, A.; Nicolas, B.; Duruflé, A.; Pape, L. Le; Gallien, P.

doi:10.1016/j.rehab.2015.07.241

Cited by 2 publications

(4 citation statements)

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“…Figure 10 shows wheelchair positions (green icons) and landmarks are assigned to be yellow icons in two different experiments: the wheelchair at position (3,3) in the grid map as shown in Figure 10(a) and the position (3,2) as shown in Figure 10(b). Table 3 shows the results of the wheelchair positioning on a 2D grid map made by the wheelchair positioning method to determine the accuracy [30].…”

Section: Determination Of the Wheelchair Position Based On Landmarkmentioning

confidence: 99%

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Positioning an electric wheelchair in 2D grid map based on natural landmarks for navigation using Q-learning

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Hải

2023

IJEECS

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Self-mobility electric wheelchairs are very useful for people with disabilities, so they can move without help in indoor environments. To create one selfmobility electric wheelchair, modern methods for control such as computer vision and machine learning can be applied. In particular, this electric wheelchair can move from any position in the indoor environment to the desired destination. For accuracy, natural landmarks are used and the navigation of the wheelchair is determined using a Q-learning reinforcement learning algorithm. In particular, this algorithm is applied to find the best path for the wheelchair to reach the destination. The article proposes a method to build one 2D grid map for wheelchair movement based on natural landmarks in the indoor environment. The new point of this method is that the position of the wheelchair can be accurately determined from a certain landmark instead of many landmarks applied in traditional methods. Some practical experiments were performed to illustrate the effectiveness of the proposed method in the indoor environment. This proposed method can be developed in more complex environments with natural landmarks.

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Section: Determination Of the Wheelchair Position Based On Landmarkmentioning

confidence: 99%

“…In recent years, smart wheelchair systems have been developed to assist people with disabilities [1]- [3]. Researches often focuses on three main areas: i) improvement of assistive technology; ii) improving physical user interface; and iii) improving control sharing between user and machine.…”

Section: Introductionmentioning

confidence: 99%

Positioning an electric wheelchair in 2D grid map based on natural landmarks for navigation using Q-learning

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Hải

2023

IJEECS

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“…Over the last 20 years, the electric powered wheelchair (EPW) has been the most common mobility aid for those with reduced mobility and, recently, the intelligent EPW, which is also referred to as an intelligent wheelchair (IW), has garnered considerable attention as an emerging device to meet users’ various demands [1,2,3,4,5,6,7,8]. Essentially, IWs are EPWs with an embedded computer and sensors, which gives them “intelligence”.…”

Section: Introductionmentioning

confidence: 99%

“…During the past decade, a number of navigation algorithms have been explored for IWs, and most of them have used various range sensors for obstacle detection and avoidance [4,5,6,7,8,9,10,11,12]. These sensor-based navigation systems consider objects that protrude more than a given distance from the ground as obstacles.…”

Section: Introductionmentioning

confidence: 99%

Wheelchair Navigation System for Disabled and Elderly People

Kim

2016

Sensors

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An intelligent wheelchair (IW) system is developed in order to support safe mobility for disabled or elderly people with various impairments. The proposed IW offers two main functions: obstacle detection and avoidance, and situation recognition. First, through a combination of a vision sensor and eight ultrasonic ones, it detects diverse obstacles and produces occupancy grid maps (OGMs) that describe environmental information, including the positions and sizes of obstacles, which is then given to the learning-based algorithm. By learning the common patterns among OGMs assigned to the same directions, the IW can automatically find paths to prevent collisions with obstacles. Second, it distinguishes a situation whereby the user is standing on a sidewalk, traffic intersection, or roadway through analyzing the texture and shape of the images, which aids in preventing any accidents that would result in fatal injuries to the user, such as collisions with vehicles. From the experiments that were performed in various environments, we can prove the following: (1) the proposed system can recognize different types of outdoor places with 98.3% accuracy; and (2) it can produce paths that avoid obstacles with 92.0% accuracy.

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Assessment of a navigation assistance system for power wheelchair

Cited by 2 publications

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Positioning an electric wheelchair in 2D grid map based on natural landmarks for navigation using Q-learning

Positioning an electric wheelchair in 2D grid map based on natural landmarks for navigation using Q-learning

Wheelchair Navigation System for Disabled and Elderly People

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