Abstract
Exploiting hand gestures for non-verbal communication has extraordinary potential in HCI. A data glove is an apparatus widely used to recognize hand gestures. To improve the functionality of the data glove, a highly stretchable and reliable signal-to-noise ratio sensor is indispensable. To do this, the study focused on the development of soft silicone microchannel sensors using a Eutectic Gallium-Indium (EGaIn) liquid metal alloy and a hand gesture recognition system via the proposed data glove using the soft sensor. The EGaIn-silicone sensor was uniquely designed to include two sensing channels to monitor the finger joint movements and to facilitate the EGaIn alloy injection into the meander-type microchannels. We recruited 15 participants to collect hand gesture dataset investigating 12 static hand gestures. The dataset was exploited to estimate the performance of the proposed data glove in hand gesture recognition. Additionally, six traditional classification algorithms were studied. From the results, a random forest shows the highest classification accuracy of 97.3% and a linear discriminant analysis shows the lowest accuracy of 87.4%. The non-linearity of the proposed sensor deteriorated the accuracy of LDA, however, the other classifiers adequately overcame it and performed high accuracies (>90%).
Highlights
Nobody knows the exact origin of gestures in human society, when/how/why we started to use gestures [1]
To estimate the performance in the hand gesture recognition via using the proposed soft sensors, we investigated the accuracy, receiver operating characteristic curve, confusion matrix, recall, precision and F1 score
The best classification accuracy was established by Random Forest (RF) and the smallest standard deviation (SD) was by Naïve Bayes (NB)
Summary
Nobody knows the exact origin of gestures in human society, when/how/why we started to use gestures [1]. In the studies using data gloves for the hand gesture recognition, a key topic is manufacturing a high stretchable and reliable signal-to-noise ratio (SNR) sensor which is embedded into a glove to capture hand motions [10,15,16,17,18,19,20,21,22]. The EGaIn alloy was utilized for various engineering applications in soft robotics, wearable electronics, stretchable electronics and sensors, and tunable antennae [18,24,25,26]. Other liquid conductors such as carbon greases, ionic liquids, and biocompatible conductive liquids [15,16,19,21] have been investigated to develop soft strain and force sensors. Since the conductivity of the liquid materials is very low compared to the liquid metal alloy, the associated sensing system requires additional signal conditioning circuitries to acquire a reasonable SNR signal and reliable measurements
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