Real time control of a wheelchair based on EMG and Kinect for the disabled people

Küçükyıldız, Gürkan; Ocak, Hasan; Şayli, Ömer; Karakaya, Suat

doi:10.1109/tiptekno.2015.7374606

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…sEMG signals control the wheelchair using hand movements and the Kinect sensor is a security system. [71] IMU, computer.…”

Section: Control Systems Referencesmentioning

confidence: 99%

“…Furthermore, Jang et al [70] is the predecessor of Jang et al [69]; as it describes the kinematic model and control method for the wheelchair through the use of a myoelectric computer interface (MCI) that controls the speed and direction of the chair. On the other hand, Küçükyildiz et al [71] describes the control of a wheelchair using sEMG. The processing was done in MATLAB ® and has a Kinect device for the navigation of the chair; depth samples are processed and obstacles are avoided.…”

Section: Controllers Which Implement Semgmentioning

confidence: 99%

“…To this end, ref. [6,10,11,14,16,18,20,23,32,33,36,38,40,[42][43][44]50,[58][59][60]64,67,71,[74][75][76][77]81,87,88] include safety systems to avoid obstacles. This indicates that approximately 40% of the articles mention the use of algorithms with ultrasonic sensors, lasers, infrared, cameras, among others, in order to provide security in the movement of wheelchairs and avoid crashes.…”

Section: Wheelchair Instrumentation and Hmimentioning

confidence: 99%

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

Callejas-Cuervo

Gonźalez-Cely

Bastos

2020

Sensors

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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.

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“…sEMG signals control the wheelchair using hand movements and the Kinect sensor is a security system. [71] IMU, computer.…”

Section: Control Systems Referencesmentioning

confidence: 99%

Section: Controllers Which Implement Semgmentioning

confidence: 99%

Section: Wheelchair Instrumentation and Hmimentioning

confidence: 99%

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

Callejas-Cuervo

Gonźalez-Cely

Bastos

2020

Sensors

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“…Automatic wheelchair digital controllers depend on the type of instrumentation used. There are non-invasive instrumentation systems placed on the user, such as those based on cerebral activity, like Brain Computer Interfaces (BCIs) [ 12 , 13 , 14 ], those based on inertial and magnetic sensors that measure head or hand movements [ 15 , 16 , 17 ], and those that implement Electrooculography (EOG) as well as Electromyography (EMG) [ 18 , 19 ], [ 20 ]. On the other hand, there are controller systems placed on the wheelchair like those that involve the use of distance sensors to detect obstacles or operate the wheelchair in closed environments [ 21 , 22 , 23 ], besides those that use vision artificial techniques [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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

Callejas-Cuervo

Gonźalez-Cely

Bastos

2021

Sensors

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The design and implementation of an electronic system that involves head movements to operate a prototype that can simulate future movements of a wheelchair was developed here. The controller design collects head-movements data through a MEMS sensor-based motion capture system. The research was divided into four stages: First, the instrumentation of the system using hardware and software; second, the mathematical modeling using the theory of dynamic systems; third, the automatic control of position, speed, and orientation with constant and variable speed; finally, system verification using both an electronic controller test protocol and user experience. The system involved a graphical interface for the user to interact with it by executing all the controllers in real time. Through the System Usability Scale (SUS), a score of 78 out of 100 points was obtained from the qualification of 10 users who validated the system, giving a connotation of “very good”. Users accepted the system with the recommendation to improve safety by using laser sensors instead of ultrasonic range modules to enhance obstacle detection.

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Birbirine Benzer El Hareketlerinin EMG Sinyalleri Kullanılarak Sınıflandırılması

Derdiyok¹,

Ay²,

Yıldız³

2021

acperpro

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Rehabilitasyon cihazlarını destekleyen robotik sistemler ve el fonksiyonlarını karşılayan robotik protezlerin de iyileşme arzusu, iskelet cihazlarının geliştirilmesinde büyük rol oynamıştır. Yüzey elektromiyografi (sEMG) sinyallerinin kullanıldığı bu tür cihazlarda protez kontrolünün gerçekleşebilmesi için sinyallerin özelliklerinin bilinmesi ve bu özellikler sayesinde el hareketlerinin sınıflandırılması mümkün hale gelir. Bu çalışmasında Myo TM Armbandı (Akıllı Kol Bant) tarafından elde edilmiş olan, yüzey elektromiyografik sinyallerinin benzer hareketleri ayırt etmede sınıflandırılması amaçlanmaktadır. Bu çalışmada kullandığım 225 katılımcıdan alınan ve benzer el hareketi yapılmış olan açık kaynak EMG veri setini, öncelikle ön işleme adımlarından geçirildikten sonra, özellik çıkarımı adımları gerçekleştirilmiştir. Çıkarılan on özellik sınıflandırıcıya girdi olarak verilmiştir. Sınıflandırıcı olarak sırasıyla Karar ağaçları (Decision Trees), Destek vektör makineleri (SVM), Toplu Öğrenme (Ensemble learning), yakın K- en komşu algoritmaları (k-NN) denenmiş olup; bu algoritmalar içinde doğruluk başarısı diğer algoritmalara oranla %87.6 olarak en yüksek k- en yakın komşu algoritması ile elde edilmiştir.

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Real time control of a wheelchair based on EMG and Kinect for the disabled people

Cited by 5 publications

References 2 publications

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

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

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

Birbirine Benzer El Hareketlerinin EMG Sinyalleri Kullanılarak Sınıflandırılması

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