Ghannam, Rami

Heidari, Hadi

Liu, Yuchi

English

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www.gla.ac.uk/melabUniversity of Glasgow, charity number SC004401Smart Multi-Sensor Wristband for Gesture ClassificationACKNOWLEDGMENTI would like to thank all meLab members, for their participation, contributions and suggestions at various stages of this project.We present a smart wristband device with high sensitivity,which could accurately recognize hand gestures.Piezoelectric sensors were used to measure the wristmuscle and tendon movements. Machine learningalgorithms were also used for analysing and classifying thecaptured data from the wristband. The bracelet device willbe powered using energy that will be harvested andscavenged from flexible piezoelectric materials and PVcells. This self-sustainable smart wristband could be usedfor health monitoring applications, especially for patientswith Parkinson’s disease who suffer from tremors andmovement disorders.OVERVIEWYuchi Liu，Rami Ghannam and Hadi HeidariMagnetoelectronics Lab (meLAB), School of Engineering, University of Glasgow, UK2109504L@student.gla.ac.ukrami.ghannam@glasgow.ac.ukHadi.Heidari@glasgow.ac.ukIMPLEMENTATION RESULTSREFERENCES[1] X. Liang et al., "Fusion of Wearable and Contactless Sensors for Intelligent Gesture Recognition," Advanced Intelligent Systems, vol. n/a, no. n/a, p. 1900088, 2019/08/22 2019.[2] H. Li et al., "Hierarchical Sensor Fusion for Micro-Gestures Recognition with Pressure Sensor Array and Radar," IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, pp. 1-1, 2019.CONCLUSION AND FUTURE WORKFig3: Results of hand gesture recognition with different algorithmsFig2:The fabrication process of homemade piezoelectric sensorsPDMS+KNLNMixingVacuumMouldHigh voltageHigh temperatureCoating conductivePolingFlexible piezo sensorThree algorithms SVM, KNN and BDT are made acomparison in this project to find a high accuracy and fastoperation algorithm which is suitable for hand gesturerecognition. Meantime, we involved feature extraction todistinguish five fingers tapping gestures and compared withthe original signal to find a better type of data for higherclassification accuracy. 15 different time domain featuresare considered including the signal mean, maximum,minimal, peak-to-peak, median absolute deviation andautoregression coefficients.Algorithm KNN BDT SVM Accuracy with row data 96.49% 92.86% 98.25%Accuracy with features extraction73.68% 70.18% 85.95%Time(s) 24.53 4.25 485Wireless MCUEnergy HarvesterFlexible solar cellLi-ion BatteryFlexible Piezo sensorBluetoothTransceiverUser InterfaceMachine Learning AlgorithmFlexible Piezo sensorFig1. The block diagram of smart bracelet. Four flexible piezoelectric sensors were fabricated. Theflexible piezoelectric sensors generate voltage differenceswhen applying force on it. The voltage difference betweensensor surface is the valuable information for gestureclassification. Meanwhile, like the flexible solar cell, thepiezoelectric sensors also provide electric energy to powerthe whole system.A self-sustainable hand gesture recognition system has been implemented by collecting pressure signals with flexiblepiezoelectric sensors. Power harvesting techniques are used to convert mechanical energy of human motions and solarenergy into electrical energy for powering the wearable electronics. In the future, various sensors will be involved in the smartwristband to develop more functions.

Smart Multi-Sensor Wristband for Gesture Classification

http://eprints.gla.ac.uk/204515/1/204515.pdf

Smart Multi-Sensor Wristband for Gesture Classification

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