Control Systems and Electronic Instrumentation Applied to Autonomy in Wheelchair Mobility: The State of the Art

Abstract

Automatic wheelchairs have evolved in terms of instrumentation and control, solving the mobility problems of people with physical disabilities. With this work it is intended to establish the background of the instrumentation and control methods of automatic wheelchairs and prototypes, as well as a classification in each category. To this end a search of specialised databases was carried out for articles published between 2012 and 2019. Out of these, 97 documents were selected based on the inclusion and exclusion criteria. The following categories were proposed for these articles: (a) wheelchair instrumentation and control methods, among which there are systems that implement micro-electromechanical sensors (MEMS), surface electromyography (sEMG), electrooculography (EOG), electroencephalography (EEG), and voice recognition systems; (b) wheelchair instrumentation, among which are found obstacle detection systems, artificial vision (image and video), as well as navigation systems (GPS and GSM). The results found in this review tend towards the use of EEG signals, head movements, voice commands, and algorithms to avoid obstacles. The most used techniques involve the use of a classic control and thresholding to move the wheelchair. In addition, the discussion was mainly based on the characteristics of the user and the types of control. To conclude, the articles exhibited the existing limitations and possible solutions in their designs, as well as informing the physically disabled community about the technological developments in this field.

Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort

“…EEG signals are mainly formed by the sum of potential synchronization after a large number of neurons in the cortex protrude [16]. EEG signals directly reflect the neuronal activity of the brain, so they can be used as a basis for analyzing people's mental activities, emotions, thinking, and so on [17]. EEG signals have basic characteristics such as frequency, amplitude, and phase and also have the characteristics of weak signal, easy interference, and obvious frequency domain characteristics.…”

Design and Implementation of a Position, Speed and Orientation Fuzzy Controller Using a Motion Capture System to Operate a Wheelchair Prototype

“…Wheelchair control systems use different types of instrumentation and control techniques, including head motion controllers that operate a prototype or wheelchair, as indicated by the state of the art that was carried out in [ 27 ]. Designs and implementations of controllers were also defined based on the research development of different universities as mentioned by [ 28 ].…”

“…This work is part of an ongoing investigation. It began with a systematic review developed in [ 27 ], in addition to the development of the proposed system in a wheelchair prototype, the upcoming implementation in an electric-powered wheelchair, and experimental tests with users with disabilities in lower and upper limbs.…”

Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot

“…In recent years, HMI has been implemented using EOG since its acquisition is less invasive compared to electroencephalography (EEG) [ 4 , 5 , 6 ]. In addition, artificial intelligence algorithms have been used which allow the classification of EOG signals for the control of wheelchairs, orthotics, assistance robots and HMI [ 7 , 8 , 9 ]. In [ 10 ], for example, the horizontal EOG channel is used to generate control commands for a lower limb orthosis, these commands are detected in a three-second sampling window to avoid false activations of the system and the processing is done in machine language.…”