Ahmet B. TATAR, Beyda TAŞAR, Özgür NAZLI, Osman KALKAN

Myo-Elektriksel Sinyaller İle İnsansız Kara Aracının Uzaktan Kontrolü

Bu çalışma kapsamında insansız bir kara aracının kişinin el ve parmak hareketleri ile uzaktan kontrolü gerçekleştirilmiştir. Beyinden kol kaslarına iletilen ve kişinin el hareketlerini gerçekleştirmesini sağlayan Elektromiyografi (EMG) sinyalleri, kişinin koluna giydiği sekiz EMG sensör içeren bileklik vasıtası ile gerçek zamanlı olarak alınmıştır. Raspberry pi 3 gömülü sistem kartı üzerinde geliştirilen sinyal işleme, öznitelik çıkarımı ve sınıflandırma algoritmaları kullanılarak anlamlandırılmıştır. Başka bir deyişle el hareketin örüntüsü (el kapama, parmak açma, serçe parmak temas, bilek dışa bükme, vs.) ile EMG sinyal grubu arasındaki ilişkiler tanımlanmıştır. Anlamlandırılan her bir el hareketi araç için bir hareketi kontrol komutu (el kapama: araç ileri, parmak açma: araç dur, serçe parmağa temas: sola dönüş, bilek dışa bükme: sağa dönüş, vs.) olarak kullanılmıştır. Böylece insan – mobil araç etkileşim ağı kurulmuştur. Kurulan insan- mobil araç etkileşim ağı sayesinde el hareketleri ile mobil aracın gerçek zamanlı hareket kontrolü ortalama % 92 başarı ile gerçekleştirilmiştir.

Anahtar Kelimeler:

Myo kol bandı, EMG sinyali, Hareket sınıflandırılması, Mobil araç kontrolü

Remote Control of Unmanned Ground Vehicle via Myo-Electrical Signals

In this study, remote control of an unmanned land vehicle by hand and finger movements was performed. Electromyography (EMG) signals, which are transmitted from the brain to the arm muscles and enable the person to perform hand movements, were received in real-time by a wristband containing eight EMG sensors worn on the arm. The signal processing, developed on the Raspberry pi 3 embedded system board, was recognized by using feature extraction and classification algorithms. In other words, the relationship between the pattern of hand movement (hand closure,hand opening, thumb-pinky finger touch, wrist bending, etc.) and the EMG signal group is defined. For each recognized hand gesture was used as a motion control command for the vehicle (hand closure: vehicle forward, hand opening: vehicle stop, thumb-little finger touch: left turn, wrist bend: right turn, etc.). Thus, a human - mobile vehicle interaction network was established. Thanks to the established human-mobile vehicle interaction network, real-time motion control of hand movements and the mobile vehicle were achieved with an average success rate of 92%.

Keywords:

Myo armband, EMG signal, Motion classification, Mobile vehicle control,

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