The electric wheelchair control using electromyography sensor of arm muscle

Hardiansyah, Rudi; Ainurrohmah, Arista; Aniroh, Yunafi’atul; Tyas, Farida Herning

doi:10.1109/icts.2016.7910286

Cited by 14 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Denotes, is the input image, and is the index of rows and column respectively, and ℎ is the filter or kernel generated using Eq. (7).…”

Section: Left and Right Corner Detectionmentioning

confidence: 99%

“…A solution also proposed by using Electromyograms, Electrooculargrams, and Electroenchepalograms that utilize several electrodes that stick to the skin user to control the electricwheelchair [7][8][9]. The idea of these proposed solutions is to read the amount of electricity produced by muscles especially when people with disabilities try to move their arms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

Pangestu¹,

Utaminingrum²

2020

IJIES

View full text Add to dashboard Cite

People with deficiency hands and feet, especially people with multiple disabilities has difficulty for navigation. Therefore, this research proposed an approach to help them using an electric wheelchair and control it using their eyeball movements. However, eyeball movement detection is still a problem due to the lack of standardized methods such as low accuracy for several gazes especially for downward and forward movements that caused by failure tracking mechanisms. Failure tracking mechanism mostly caused by failure pupil detection and tracking processes. According to the conducted research, there is a relation between eyeball movement and eyelid movement. Due to the relation, it can be concluded that the movement of the eyeball can be detected by utilize the eyelid movement without using the tracking mechanism. Hence, in this research, we propose an approach to detect the five movements of the eyeball using Eye-mark based on contour and edge detection. Our proposed method produces a brisk result with 91,2% of accuracy and better computational time compared with other marking methods. Accordingly, it is interesting on how to relate between eyeball movements and electric wheelchair navigation mechanism.

show abstract

“…Denotes, is the input image, and is the index of rows and column respectively, and ℎ is the filter or kernel generated using Eq. (7).…”

Section: Left and Right Corner Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

Pangestu¹,

Utaminingrum²

2020

IJIES

View full text Add to dashboard Cite

show abstract

“…Kemajuan teknologi memungkinkan pembuatan kursi roda elektrik yang lebih memudahkan para penderita kelumpuhan dalam mengoperasikannya, berbagai macan tipe kursi roda elektrik yang sedang diteliti dan dikembangkan, tujuannya adalah untuk membantu penderita kelumpuhan dalam melakukan aktifitas kesehariannya. Pada penelitian yang menggunakan sinyal elektromiograf (EMG) sebagai kontrol kusi roda terdapat dua arah, yang pertama bersifat power assist [1], dan yang kedua bersifat kontrol penuh untuk kursi roda elektrik [2], kedua penelitian tersebut berbeda kegunaannya, kursi roda elektrik yang bersifat power assist lebih diperuntukkan bagi penderta kelumpuhan dengan kekuatan tangan yang masih normal, sedangkan kursi roda yang bersifat kontrol penuh lebih diperuntukkan bagi penderita kelumpuhan dengan kekuatan tangan yang tidak lagi mampu memutar roda dari kursi roda elektrik.…”

Section: Pendahuluanunclassified

Perintah Kontrol Gerak Kursi Roda Elektrik Menggunakan Sensor Elektromiograf

Imron

Muldayani

Sumardi

2019

JRE

View full text Add to dashboard Cite

Kelumpuhan merupakan suatu penyakit yang dapat membatasi mobilitas penderitanya. Salah satu solusi yang dapat membantu penderita kelumpuhan dalam melakukan mobilitasnya yaitu penggunaan kursi roda elektrik. Pada penelitian ini, kursi roda elektrik dengan spesifikasi dimana kontrol gerak kursi roda menggunakan otot pada kedua lengan, sehingga kursi roda elektrik ini sangat cocok digunakan bagi pasien kelumpuhan pada kaki dan lemahnya kekuatan tangan dalam memutar roda dari kursi roda. Input perintah kontrol gerak dilakukan melalui sensor elektromiograf yang terpasang pada otot fleksor dikedua lengan pasien. Output dari masing masing sensor tersebut diberikan threshold sebesar 2 volt untuk membedakan perintah kontrol atau bukan. Ketika output sensor lebih dari sama dengan threshold maka dianggap logika satu dan yang lain dianggap logika nol. Metode yang dilakukan untuk mengartikan output tersebut sebagai perintah kontrol dengan cara deteksi impuls. Gerakan kursi roda elektrik yang dapat dilakukan yaitu maju, belok kanan, dan belok kiri.

show abstract

“…EEG technique was deployed for capturing brain or blink signals and translating them into wheelchair direction commands. Another well-established approach for controlling EPWs is Electromyography (EMG) by utilizing signals acquired from arm muscles [7] and facial movements [8]. Furthermore, a hand gesture EPW control system that uses a camera which captures hand movements, and a visual recognition algorithm was presented [9].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Force Sensor Arrays Integration into Textile for a Novel Head-Foot Wheelchair Steering System

Gakopoulos

Nica

Bekteshi

et al. 2020

8th European Medical and Biological Engineering Conference

View full text Add to dashboard Cite

In this paper a novel head-foot wheelchair steering system based on force sensor arrays (FSAs) for people diagnosed with dyskinetic cerebral palsy (DCP) is introduced. The user applies pressure on FSAs placed on the head, and foot supports of the electrically powered wheelchair (EPW), based on his/her intention to accelerate, brake, steer right or left. The microcontroller-based electronic system acquires and translates the mean voltage generated by the applied force into wheelchair control signals. In such a system, FSAs are integrated into the head support of the wheelchair using support materials and textiles for the comfort of the user, having an effect on the sensor readings. This work aims to explore the effect of integrating FSAs into the head support of the wheelchair using support material and textiles. Four different sensor integration approaches were examined and compared to baseline readings of a non-integrated FSA. It was found that when a maximum force of 80 N is applied to a single sensing element (sensel) the support material decreases the mean voltage by approximately 70%, and the sensor integration into textile has shown increases between 5.2 and 14.9% compared to the support material. Furthermore, when force is applied over multiple sensels, the support material accounts for a reduce in the mean voltage ranging from approximately 3 to 32%. The addition of textile has exhibited peak decrease in the mean voltage up to roughly 41% for the tested integrations.

show abstract

The electric wheelchair control using electromyography sensor of arm muscle

Cited by 14 publications

References 3 publications

Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

Electric Wheelchair Control Mechanism Using Eye-mark Key Point Detection

Perintah Kontrol Gerak Kursi Roda Elektrik Menggunakan Sensor Elektromiograf

The Effect of Force Sensor Arrays Integration into Textile for a Novel Head-Foot Wheelchair Steering System

Contact Info

Product

Resources

About