Dr. NAWAZ MehmoodChan, Russell W.Russell W.ChanMalik, AnjuAnjuMalikKhan, TariqTariqKhanCao, PengPengCao2024-09-072024-09-072022IEEE Sensors Journal, 2022, vol. 22(19), pp. 18922-18932.1530-437X1558-1748http://hdl.handle.net/20.500.11861/10462Human–computer communication using hand gestures has always been difficult. More than half a century ago, people used differentways of interactionwith computers from the early mediums such as perforated game cards. Nowadays, if a richer lexicon of gestures is given, people can communicate more effectively with computers. Machine learning is now used to recognize and classify hand gestures in amore preciseway. In order to increase the communication between computers and humans, we proposed a technique, which uses a wearable low-cost device to generate the electrical impedance tomography (EIT) images to recover the inner impedance structure of a user’s wrist. This is done by measuring the transverse impedance between all the 16 pairs of electrodesofwrist band that lie on the skin of the user hand. The proposedtechnique is enough to integrate the technology into the prototypewrist band tomonitor and classify gestures in real time. We have conducted a study of 16 gestures with a focus on gross hand and pinch finger gestures. The results evaluation shows that the gross hand gestures achieved 90% accuracy inwrist position, while pinch gestures achieved 93% accuracy.enHand GesturesElectrical Impedance TomographyGesture ClassificationElectrical Impedance Tomography ImagesWearable DevicesLow CostConductiveTraining DatasetLow ResolutionHigh-Resolution ImagesConvolutional LayersWhite NoiseImage ReconstructionResidual BlockLow Computational CostSurface ElectromyographyBayesian OptimizationNumber of ElectrodesGesture RecognitionDistribution of ResistanceHand Gesture RecognitionMaximum Pooling LayerWrist PositionHand SkinResNet-18 ModelSign LanguageMulti-frequency Bioelectrical Impedance AnalysisFinite Element MethodDeep LearningMeasurement NoisePeer Reviewed Journal Article10.1109/JSEN.2022.3193718