Gesture Recognition Via Flexible Capacitive Touch Electrodes

A novel wearable device for gesture recognition was developed and tested on five subjects. The low-cost, wireless wearable device was engineered with a set of seven flexible capacitive touch electrodes sewn into an armband to be worn on the forearm between the wrist and elbow. These capacitive touch electrodes were interfaced with a microcontroller and bluetooth transceiver for measurement and transmission. As different gestures are made, flexing muscles beneath the skin affect the capacitance measured on these seven electrodes. A set of 32 gestures were tested including the 16 grasps in the Cutkosky Grasp Taxonomy and 16 basic finger and wrist motions. Several classification algorithms were tested on this data. Using a Random Forest (RF) algorithm to classify the training data, an average gesture recognition accuracy of $95 . 6 \pm 0 .06$% was achieved across all five subjects individually.

[1]  Theresa Dankowski,et al.  Calibrating random forests for probability estimation , 2016, Statistics in medicine.

[2]  Claudio Castellini,et al.  A Comparative Analysis of Three Non-Invasive Human-Machine Interfaces for the Disabled , 2014, Front. Neurorobot..

[3]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[4]  Sahin Albayrak,et al.  eRing: multiple finger gesture recognition with one ring using an electric field , 2015, iWOAR.

[5]  Chintan Patel,et al.  Gait analysis for fall prediction using hierarchical textile-based capacitive sensor arrays , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[6]  Paul Lukowicz,et al.  Active Capacitive Sensing: Exploring a New Wearable Sensing Modality for Activity Recognition , 2010, Pervasive.

[7]  Carlo Menon,et al.  Exploration of Force Myography and surface Electromyography in hand gesture classification. , 2017, Medical engineering & physics.

[8]  Elvira Pirondini,et al.  EMG-based decoding of grasp gestures in reaching-to-grasping motions , 2017, Robotics Auton. Syst..

[9]  Alex Alves Freitas,et al.  A new approach for interpreting Random Forest models and its application to the biology of ageing , 2018, Bioinform..

[10]  Long Wang,et al.  A wearable capacitive sensing system with phase-dependent classifier for locomotion mode recognition , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[11]  Carlo Menon,et al.  Continuous Prediction of Finger Movements Using Force Myography , 2016 .

[12]  Long Wang,et al.  Non-contact capacitance sensing for continuous locomotion mode recognition: Design specifications and experiments with an amputee , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[13]  Tingfang Yan,et al.  Review of assistive strategies in powered lower-limb orthoses and exoskeletons , 2015, Robotics Auton. Syst..

[14]  Agnes Roby-Brami,et al.  Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies , 2016, IEEE Reviews in Biomedical Engineering.

[15]  Matthew S. Reynolds,et al.  Finding Common Ground: A Survey of Capacitive Sensing in Human-Computer Interaction , 2017, CHI.

[16]  Mark R. Cutkosky,et al.  On grasp choice, grasp models, and the design of hands for manufacturing tasks , 1989, IEEE Trans. Robotics Autom..

[17]  Sunil Agrawal,et al.  Capture, learning, and classification of upper extremity movement primitives in healthy controls and stroke patients , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

[18]  Richard H. Bayford,et al.  Towards a High Accuracy Wearable Hand Gesture Recognition System Using EIT , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[19]  Sheng Quan Xie,et al.  Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. , 2012, Medical engineering & physics.

[20]  C. J. Luca,et al.  SURFACE ELECTROMYOGRAPHY : DETECTION AND RECORDING , 2022 .

[21]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[22]  Robert Riener,et al.  A survey of sensor fusion methods in wearable robotics , 2015, Robotics Auton. Syst..