'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
This paper presents an innovative wrist-worn device
with machine learning capabilities and a wearable pressure sensor
array. The device is used for monitoring different hand gestures
by tracking tendon movements around the wrist. Thus, an array
of PDMS-encapsulated capacitive pressure sensors is attached to
the user to capture wrist movement. The sensors are embedded on
a flexible substrate and their readout requires a reliable approach
for measuring small changes in capacitance. This challenge was
addressed by measuring the capacitance via the switched capacitor
method. The values were processed using a programme on
LabVIEW to visually reconstruct the gestures on a computer.
Additionally, to overcome limitations of tendon’s uncertainty
when the wristband is re-worn, or the user is changed, a
calibration step based on the Support Vector Machine (SVM)
learning technique is implemented. Sequential Minimal
Optimization (SMO) algorithm is also applied in the system to
generate SVM classifiers efficiently in real-time. The working
principle and the performance of the SVM algorithms
demonstrate through experiments. Three discriminated gestures
have been clearly separated by SVM hyperplane and correctly
classified with high accuracy (>90%) during real-time gesture
recognition
