GYRO LIFT-A INNOVATIVE ANTI-TREMOR STABILIZING HANDLE

  • Ponmudi P
  • Radhika J
Keywords: Flash memory, Joy stick, Bluetooth transceiver ,Graph ics LCD.

Abstract

Gyro-lift is an innovative anti-tremor stabilizing handle to help people with handle tremor eat , lift and carry utensils easily .The device compensates for the shaking and allows the food to stay on the spoon or fork without spilling . The device senses the hand tremor motion and automatically stabilizies the attached utensil. The utensil can be a spoon, fork or larger dining and kitchen vessels. To help the people affected with hand tremor to live independently .It prevents them for being hospitalized for years .It brings them out of depression. This device helpful for doctor to identify the hand tremor level of patient. Animated movement feature helpful for user needed activity design purpose.

Downloads

Download data is not yet available.

Author Biographies

Ponmudi P

Electronics and Communication Engineering, Sri Muthukumaran Institute of Technology, Chikkarayapuram, Near Mangadu, Chennai-69, TamilNadu.

Radhika J

Electronics and Communication Engineering, Sri Muthukumaran Institute of Technology, Chikkarayapuram, Near Mangadu, Chennai-69, TamilNadu.

References

[1] Kan,Y.-C. Chen, C.-K. (2012) ‘A Wearable Inertial Sensor Node for Body Motion Analysis’, IEEE Sensors Journal,Vol.12,No.3,pp.651–657.

[2] King,R.C. Atallah,L. Lo,B. Yang,G. (2007) ‘Development of a wireless sensor glove for surgical skills assessment’, IEEE Trans Inf Technol Biomed, 13(5),Vol.6,No.3, pp.673–679 .

[3] Lu,Z. Chen,X. Li,Q. Zhang,X. Zhou,P. (2014) ‘A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices’, IEEE Trans. Human-Machine Systems, Vol.44, No.2, pp.293–299.

[4] Mukhopadhyay,S.C. (2015) ‘Wearable Sensors for Human Activity Monitoring: A Review’, IEEE Sensors Journal,Vol.15,No.3, pp.1321–1330.

[5] Ong,S.C.W. Ranganath,S. (2005) ‘Automatic Sign Language Analysis-a survey and the future beyond lexical meaning’, IEEE Trans. Pattern Anal. Mach. Intell,Vol.3,No.5, pp.873–891.

[6] Sturman, D. and Zeltzer, D. (2003) ‘A survey of glove-based input’ in IEEE Computer graphics and Applications, Vol. 3, No. 2, pp. 415-420.

[7] Teleb,H. and Chang,G. (2012) ‘Data Glove Integration with 3D Virtual Environments’, ICSAI 2012,Vol.4,No.1,pp.214-480.

[8] Wu,Y. and Huang,T.S. (2010) ‘Vision-based gesture recognition: A review’, Lecture Notes Comput. Sci.,Vol.1739,No.3,pp.103–115.

[9] Zhou,H. Hu,H. Harris,N.D. Hammerton,J. (2006) ‘Applications of wearable inertial sensors in estimation of upper limb movements’, Biomedical Signal Processing and Control 1 22–32,Vol.5,No.2, pp.108-420.

[10] Zhou,S. Fei,F. Zhang,G. Mai,J. Liu,Y. Liou,J. Li,W. (2014) ‘2D human gesture tracking and recognition by the fusion of MEMS inertial and vision Sensors’, IEEE Sensors Journal,Vol.14,No.4,pp.1160–1170.
Published
2017-03-27
Section
Articles