Six Sensors Bracelet for Force Myography based American Sign Language Recognition

In recent years, force myography (FMG) is considered a non-invasive method used for the recognition of different gestures such as the American sign language (ASL) by measuring presser changes due to different variations in muscle volume while performing different signs. In this work, only six FSR commercial sensors were inserted to the inner side of a bracelet to be in direct contact with the skin was used to collect the raw FMG signal from 10 healthy subjects with 10 trails. In this paper, an extreme learning machine (ELM) with cross-validation ($\text{Kfold}=5$) was applied to test the accuracy of using raw FMG signal in comparison with only six time domain extracted features. The results show that the accuracy based on extracting six features was equal to 91.11%, which outperforms the raw FMG signal for gesture recognition where it reached only a testing accuracy of 85.56%.

[1]  C. Verma,et al.  Dimension reduction methods for microarray data: a review , 2017 .

[2]  Mehrdad Saif,et al.  Hand gesture recognition using force myography of the forearm activities and optimized features , 2018, 2018 IEEE International Conference on Industrial Technology (ICIT).

[3]  Carlo Menon,et al.  A preliminary investigation on the utility of temporal features of Force Myography in the two-class problem of grasp vs. no-grasp in the presence of upper-extremity movements , 2017, Biomedical engineering online.

[4]  Olfa Kanoun,et al.  Four Sensors Bracelet for American Sign Language Recognition based on Wrist Force Myography , 2020, 2020 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA).

[5]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[6]  Guang-Bin Huang,et al.  An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels , 2014, Cognitive Computation.

[7]  Carlo Menon,et al.  Force Exertion Affects Grasp Classification Using Force Myography , 2018, IEEE Transactions on Human-Machine Systems.

[8]  Zhen Gang Xiao,et al.  A Review of Force Myography Research and Development , 2019, Sensors.

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

[10]  Yimesker Yihun,et al.  Performance of Forearm FMG for Estimating Hand Gestures and Prosthetic Hand Control , 2019, Journal of Bionic Engineering.

[11]  Carlo Menon,et al.  Investigation of the Feasibility of Strain Gages as Pressure Sensors for Force Myography , 2017, IWBBIO.

[12]  Eric Fujiwara,et al.  Optical Fiber Force Myography Sensor for Identification of Hand Postures , 2018, J. Sensors.

[13]  Mosiuoa M. Sole,et al.  Sign language recognition using the Extreme Learning Machine , 2011, IEEE Africon '11.

[14]  Feng Duan,et al.  A Multi-Gestures Recognition System Based on Less sEMG Sensors , 2019, 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM).

[15]  Carlo Menon,et al.  Exploration of Gait Parameters Affecting the Accuracy of Force Myography-Based Gait Phase Detection* , 2018, 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob).

[16]  Nguon Ha,et al.  Force myography signal based hand gesture classification for the implementation of real- time prosthetic hand control system , 2017 .

[17]  Carlo Menon,et al.  Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study , 2016, Front. Bioeng. Biotechnol..