Wearable band for hand gesture recognition based on strain sensors

A novel fully wearable system based on a smart wristband equipped with stretchable strain gauge sensors and readout electronics have been assembled and tested to detect a set of movements of a hand crucial in rehabilitation procedures. The high sensitivity of the active devices embedded on the wristband do not need a direct contact with the skin, thus maximizing the comfort on the arm of the tester. The gestures done with the device have been auto-labeled by comparing the signals detected in real-time by the sensors with a commercial infrared device (Leap motion). Finally, the system has been evaluated with two machine-learning algorithms Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), reaching a reproducibility of 98% and 94%, respectively.

[1]  Carlo Menon,et al.  A wearable sensor system for rehabilitation apllications , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).

[2]  Terri M. Skirven,et al.  Rehabilitation of the hand and upper extremity , 2011 .

[3]  Ken Perlin,et al.  Mechanical force redistribution: enabling seamless, large-format, high-accuracy surface interaction , 2014, CHI.

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Masamichi Shimosaka,et al.  Hand shape classification with a wrist contour sensor: development of a prototype device , 2011, UbiComp '11.

[6]  Rachel Nuwer Armband adds a twitch to gesture control , 2013 .

[7]  Xinjun Sheng,et al.  Preliminary Testing of a Hand Gesture Recognition Wristband Based on EMG and Inertial Sensor Fusion , 2015, ICIRA.

[8]  Yang Zhang,et al.  Tomo: Wearable, Low-Cost Electrical Impedance Tomography for Hand Gesture Recognition , 2015, UIST.

[9]  H Poizner,et al.  Virtual reality-based post-stroke hand rehabilitation. , 2002, Studies in health technology and informatics.

[10]  Carlo Menon,et al.  Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities , 2014, Journal of NeuroEngineering and Rehabilitation.

[11]  Joseph A. Paradiso,et al.  WristFlex: low-power gesture input with wrist-worn pressure sensors , 2014, UIST.

[12]  Patrick Olivier,et al.  Digits: freehand 3D interactions anywhere using a wrist-worn gloveless sensor , 2012, UIST.