Research Journal of Pharmacy and Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

EMG Sensor based Wheel Chair Control and Safety System


Affiliations
1 School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab- 144411, India
     

   Subscribe/Renew Journal


This work presents the development of a smart wheelchair, controlled and guided by EMG signals for those facing the problems of physical disabilities. Conventional wheelchairs were not effective for people with disabilities, as it requires a great pedalling power and gets affected by users’ limitations such as defects of the fingers. This project proposes a solution in the form of a 4-channel EMG controlled Electric wheelchair. The EMG signals are acquired using EMG sensors attached to the muscles of arms and are read in Arduino Uno board working with an AVR microcontroller. The battery powered wheelchair has 3 different modules- Signal acquisition Module, Signal Analysis Module and finally the Wheelchair controlled module. Unlike its previous versions, this wheelchair will move auto-forward whenever powered ON, moves right and left with the required muscle movements, and given further more delay on either of the directions it will turn backwards. Apart from this, an additional feature of designated stop switch is also included for ceasing wheelchair’s motion. Hence this project ensures the wheelchair's motion to be smoother.

Keywords

Wheelchair, EMG, Arduino, AVR, Battery.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Mazumder O, Kundu A.S, Chattaraj R, Bhaumik S. Holonomic wheelchair control using EMG signal and joystick interface. Recent Advancement in Engineering and Computer Science. 2014; 1-6.

  • Desmond R, Dickerman M, Fleming J, Sinyukov D, Schaufeld J, Padir T. Development of modular sensors for semi-autonomous wheelchairs. IEEE International Conference on Technology for Practical Robot Applications. 2013; 1–6.

  • Sinyukov D, Desmond R, Dickerman M, Fleming J, Schaufeld J, Padir T. Multi-modal control framework for a semi-autonomous wheelchair using modular sensor designs. Intelligent Service Robot. 2014; 7(3):145–155.

  • Millan J d R. BMI: Lessons from tests with impaired users. International Winter Workshop Brain-Computer Interface. 2014; 1.

  • Pasteau F, Krupa A, Babel M. Vision-based assistance for wheelchair navigation along corridors. IEEE International Conference on Robotics and Automation. 2014; 4430–4435.

  • Rathore D K, Srivastava P, Pandey S, Jaiswal S. A novel multipurpose smart wheelchair. IEEE Students’ Conference Electrical, Electronics and Computer Science. 2014; 1-4.

  • Yayan U, Akar B, Inan F, Yazici A. Development of indoor navigation software for intelligent wheelchair. IEEE International Symposium on Innovations in Intelligent System and Application Proceedings. 2014; 325-329.

  • Leishman F, Monfort V, Horn O, Bourhis G. Driving assistance by deictic control for a smart wheelchair: The assessment issue. IEEE Transaction on Human-Machine System. 2014; 44(1):66–77.

  • Jain S, Argall B. Automated perception of safe docking locations with alignment information for assistive wheelchairs. In IEEE/RSJ International Conference on Intelligent Robots and System. 2014; 4997-5002.

  • Venkateshwarla Rama Raju, Sreeniva B. Latent Variate Factorial and Clustering Analysis of EMG Writer`s Cramp Neuromuscular Signals. Research J. Engineering and Tech. 2018; 9(2): 167-173.

  • V Rama Raju, M Rukmini, R Borgohain, NV Thakor, RNSK Kartheek. Coherence Technique in Multisited EMG Writer’s Cramp signals. Research J. Engineering and Tech. 5(4): Oct.-Dec., 2014 page 190-194.

  • Hyeon-Su Kim, Keon-Cheol Lee, Won-Sik Bae. The Effects of Stepping Exercises on a Box or Stairs on Stroke Patients Lower Limb Muscle Activity and Balance. Research J. Pharm. and Tech 2018; 11(4):1289-1292.

  • Ki-Hong Kim, Byung-Kwan Kim, Sung-SikKo, Chun-Ho Yang. Effect of Water Depth through Water Walk Exercise on the Strength of Trunk Muscle and the EMG of the Erector Spinae and External Oblique. Research J. Pharm. and Tech. 2017; 10(7): 2329-2334.

  • Marshiana. D, Elizabeth Sherine. M, Sunitha. N, Vinothkumar. C. Footstep Power production using Piezoelectric Sensors. Research J. Pharm. and Tech. 2016; 9(7):831-834.

  • S. Krishnakumar, K. Monika, Brite Jose John, Nevin Samuel. Automatic Ingestion Monitoring Device for Diet Control. Research J. Pharm. and Tech. 2017; 10(7): 2173-2178.

  • Simpson R. Smart Wheelchair Component System. Journal of Rehabilation Research and Develoment. 2004; 41(3B) :429–442.

  • Ahmed, Faiz Syed. Mobility assistance robot for disabled persons using electromyography (EMG) sensor. IEEE International Conference on Innovative Research and Development (ICIRD). 2018.

  • Phinyomark, Angkoon, Phukpattaranont Pornchai, Limsakul Chusak. A review of control methods for electric power wheelchairs based on electromyography signals with special emphasis on pattern recognition. IETE Technical Review. 2011; 28(4) : 316-326.

  • Oonishi, Yuusuke, Oh Sehoon, Hori Yoichi. A new control method for power-assisted wheelchair based on the surface myoelectric signal. IEEE Transactions on Industrial Electronics. 2010; 57(9) : 3191-3196.

  • Taher, Ben Fatma, Amor Ben Nader, Jallouli Mohamed. A multimodal wheelchair control system based on EEG signals and Eye tracking fusion. International Symposium on. IEEE Innovations in Intelligent Systems and Applications (INISTA). 2015.

  • Kundu A S, Sankar Ananda. Hand gesture recognition based omnidirectional wheelchair control using IMU and EMG sensors. Journal of Intelligent & Robotic Systems. 2018; 91(3): 529-541.


Abstract Views: 166

PDF Views: 0




  • EMG Sensor based Wheel Chair Control and Safety System

Abstract Views: 166  |  PDF Views: 0

Authors

Himani Jerath
School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab- 144411, India
Kavala Kotesh Phani Rohith
School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab- 144411, India

Abstract


This work presents the development of a smart wheelchair, controlled and guided by EMG signals for those facing the problems of physical disabilities. Conventional wheelchairs were not effective for people with disabilities, as it requires a great pedalling power and gets affected by users’ limitations such as defects of the fingers. This project proposes a solution in the form of a 4-channel EMG controlled Electric wheelchair. The EMG signals are acquired using EMG sensors attached to the muscles of arms and are read in Arduino Uno board working with an AVR microcontroller. The battery powered wheelchair has 3 different modules- Signal acquisition Module, Signal Analysis Module and finally the Wheelchair controlled module. Unlike its previous versions, this wheelchair will move auto-forward whenever powered ON, moves right and left with the required muscle movements, and given further more delay on either of the directions it will turn backwards. Apart from this, an additional feature of designated stop switch is also included for ceasing wheelchair’s motion. Hence this project ensures the wheelchair's motion to be smoother.

Keywords


Wheelchair, EMG, Arduino, AVR, Battery.

References