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, and testing. Prototyping approach was used to design the armband to ensure that it can properly gather raw EMG and IMU signals. Object-Oriented Programming (OOP) was used to develop the presentation software so it could interpret the received signals into common hand gestures and facilitate free handwriting on air.Results -Raw EMG and IMU signals could be transmitted from the armband to the attached computer via Bluetooth successfully. The developed software was able to interpret the received signals from the armband and perform the corresponding computer navigation commands. It is able to recognize and translate dynamic gestures into an equivalent handwriting. Furthermore, it is able to read different file types such as Portable Document File (PDF), PowerPoint, Word document and audio-video media files.Conclusion -The angular velocity and linear acceleration data from arm movement along with the electromyographic data from forearm muscle contraction could be successfully used to implement hands-free navigation and free-hand writing on air. Thus, the integration of IMU with the EMG signals for dynamic hand gestures and free-handwriting work accordingly.Recommendations -To increase the number of recognizable gestures, additional EMG sensors must be placed in more strategic positions. Handwriting functionality could be improved with the use of additional handwriting recognition algorithm.Research Implications -Modern presentation tools like interactive whiteboard offers a lot of promising functionality but it comes with a price. The use of an add-on wearable device for lecture and presentation purposes will benefit various organizations and academic institutions since it will give them the advantage of modern presentation tools without having to abandon the currently usable laptop and LCD projectors.

A novel autonomous learning framework to enhance sEMG-based hand gesture recognition using depth information

“…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.…”

“…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.…”

Jointly network: a network based on CNN and RBM for gesture recognition

“…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].…”

Denoising of surface electromyogram based on complementary ensemble empirical mode decomposition and improved interval thresholding

“…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.…”