A wearable hand gesture recognition device based on acoustic measurements at wrist

This paper investigates hand gesture recognition from acoustic measurements at wrist for the development of a low-cost wearable human-computer interaction (HCI) device. A prototype with 5 microphone sensors on human wrist is benchmarked in hand gesture recognition performance by identifying 36 gestures in American Sign Language (ASL). Three subjects were recruited to perform over 20 trials for each set of hand gestures, including 26 ASL alphabets and 10 ASL numbers. Ten features were extracted from the signal recorded by each sensor. Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (kNN), and Linear Discriminant Analysis (LDA) were compared in classification performance. Among which, LDA offered the highest average classification accuracy above 80%. Based on these preliminary results, our proposed technique has exhibited a promising means for developing a low-cost HCI.

[1]  Omar M. Al-Jarrah,et al.  Recognition of gestures in Arabic sign language using neuro-fuzzy systems , 2001, Artif. Intell..

[2]  Anupam Agrawal,et al.  Vision based hand gesture recognition for human computer interaction: a survey , 2012, Artificial Intelligence Review.

[3]  Ling Shao,et al.  Enhanced Computer Vision With Microsoft Kinect Sensor: A Review , 2013, IEEE Transactions on Cybernetics.

[4]  Adrian D. C. Chan,et al.  Myoelectric Control Development Toolbox , 2007 .

[5]  Pornchai Phukpattaranont,et al.  Feature reduction and selection for EMG signal classification , 2012, Expert Syst. Appl..

[6]  Ferat Sahin,et al.  Real-Time American Sign Language Recognition System Using Surface EMG Signal , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[7]  Nurhazimah Nazmi,et al.  A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions , 2016, Sensors.

[8]  Paolo Dario,et al.  A Survey of Glove-Based Systems and Their Applications , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Stefano Stramigioli,et al.  Myoelectric forearm prostheses: state of the art from a user-centered perspective. , 2011, Journal of rehabilitation research and development.

[10]  Ivan Poupyrev,et al.  Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum , 2016, UIST.

[11]  Yi-Zeng Liang,et al.  Monte Carlo cross validation , 2001 .

[12]  S. Hesse,et al.  Upper and lower extremity robotic devices for rehabilitation and for studying motor control , 2003, Current opinion in neurology.

[13]  C. Orizio.  Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies. , 1993, Critical reviews in biomedical engineering.

[14]  J A Hokanson,et al.  Wrist anatomy: incidence, distribution, and correlation of anatomic variations, tears, and arthrosis. , 1993, The Journal of hand surgery.

[15]  Jakub Galka,et al.  Inertial Motion Sensing Glove for Sign Language Gesture Acquisition and Recognition , 2016, IEEE Sensors Journal.