2017
DOI: 10.25147/ijcsr.2017.001.1.16
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Presentation-Aid Armband with IMU, EMG Sensor and Bluetooth for Free-Hand Writing and Hand Gesture Recognition

Abstract: Purpose -The study aimed to improve the presenter's capability to give a presentation in a hands-free manner. It covered the design of a wearable armband that uses electromyography (EMG), Inertial Measurement Unit (IMU), and Bluetooth wireless technology. Also, it covered the development of presentation software for Windows operating system.Method -The study employed the common elements of engineering design process which includes problem identification, requirement analysis, design solution, implementation, a… Show more

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Cited by 4 publications
(4 citation statements)
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“…An ANN-based hand gesture recognition system is proposed to process depth information applying a self co-articulated set of features [47]. A real-time hand gesture recognition model, using the ANN network to train EMG signals, is proposed in [28]. In this research, EMG and IMU signals are combined to recognize hand gestures in order to implement hands-free navigation and free-hand writing in the air.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…An ANN-based hand gesture recognition system is proposed to process depth information applying a self co-articulated set of features [47]. A real-time hand gesture recognition model, using the ANN network to train EMG signals, is proposed in [28]. In this research, EMG and IMU signals are combined to recognize hand gestures in order to implement hands-free navigation and free-hand writing in the air.…”
Section: Related Workmentioning
confidence: 99%
“…A real-time hand gesture recognition model, using the artificial neural feedforward (ANN) network to train EMG signals, is proposed. EMG and IMU signals are combined to recognize hand gestures in order to implement hands-free navigation and free-hand writing in the air [28]. Nevertheless, the preprocessing of the collection and labeling of large manual data imposes a heavy work burden and results in time-consuming implementations.…”
Section: Introductionmentioning
confidence: 99%
“…The latest theoretical results show that in order to learn complex functions that can represent high-level abstract information, such as visual and linguistic abstract information, a deep structure is needed and includes multi-layer nonlinear elements such as multi-layer neural networks. We hope that the input raw data will gradually be transformed into higher-level abstract information [30][31][32]. In this section, we mainly use four kinds of neural networks to recognize and classify gestures under different backgrounds, namely, deep belief nets, a deep neural network, convolutional neural network and CNN and RBM jointly networks [33][34].…”
Section: Simulation Comparison Experimentsmentioning
confidence: 99%
“…1 sEMG signals are extensively used in various fields, such as sports medicine, rehabilitation training, and mechanical control. [2][3][4] In addition, sEMG signals are extremely weak, with magnitudes limited within the µV range. Moreover, sEMG signals are nonlinear, nonstationary, and distributed between 10 Hz and 500 Hz.…”
Section: Introductionmentioning
confidence: 99%