1,117 research outputs found
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier
EMG-based gesture recognition shows promise for human-machine interaction.
Systems are often afflicted by signal and electrode variability which degrades
performance over time. We present an end-to-end system combating this
variability using a large-area, high-density sensor array and a robust
classification algorithm. EMG electrodes are fabricated on a flexible substrate
and interfaced to a custom wireless device for 64-channel signal acquisition
and streaming. We use brain-inspired high-dimensional (HD) computing for
processing EMG features in one-shot learning. The HD algorithm is tolerant to
noise and electrode misplacement and can quickly learn from few gestures
without gradient descent or back-propagation. We achieve an average
classification accuracy of 96.64% for five gestures, with only 7% degradation
when training and testing across different days. Our system maintains this
accuracy when trained with only three trials of gestures; it also demonstrates
comparable accuracy with the state-of-the-art when trained with one trial
Intimate interfaces in action: assessing the usability and subtlety of emg-based motionless gestures
Mobile communication devices, such as mobile phones and networked personal digital assistants (PDAs), allow users to be constantly connected and communicate anywhere and at any time, often resulting in personal and private communication taking place in public spaces. This private -- public contrast can be problematic. As a remedy, we promote intimate interfaces: interfaces that allow subtle and minimal mobile interaction, without disruption of the surrounding environment. In particular, motionless gestures sensed through the electromyographic (EMG) signal have been proposed as a solution to allow subtle input in a mobile context. In this paper we present an expansion of the work on EMG-based motionless gestures including (1) a novel study of their usability in a mobile context for controlling a realistic, multimodal interface and (2) a formal assessment of how noticeable they are to informed observers. Experimental results confirm that subtle gestures can be profitably used within a multimodal interface and that it is difficult for observers to guess when someone is performing a gesture, confirming the hypothesis of subtlety
Low-cost wearable multichannel surface EMG acquisition for prosthetic hand control
Prosthetic hand control based on the acquisition
and processing of surface electromyography signals (sEMG) is a
well-established method that makes use of the electric potentials
evoked by the physiological contraction processes of one or more
muscles. Furthermore intelligent mobile medical devices are on
the brink of introducing safe and highly sophisticated systems to
help a broad patient community to regain a considerable amount
of life quality. The major challenges which are inherent in such
integrated system’s design are mainly to be found in obtaining a
compact system with a long mobile autonomy, capable of
delivering the required signal requirements for EMG based
prosthetic control with up to 32 simultaneous acquisition
channels and – with an eye on a possible future exploitation as a
medical device – a proper perspective on a low priced system.
Therefore, according to these requirements we present a wireless,
mobile platform for acquisition and communication of sEMG
signals embedded into a complete mobile control system
structure. This environment further includes a portable device
such as a laptop providing the necessary computational power
for the control and a commercially available robotic handprosthesis.
Means of communication among those devices are
based on the Bluetooth standard. We show, that the developed
low cost mobile device can be used for proper prosthesis control
and that the device can rely on a continuous operation for the
usual daily life usage of a patient
Hand Pattern Recognition Using Smart Band
The Importance of gesture recognition has widely spread around the world. Many research strategies have been proposed to study and recognize gestures, especially facial and hand gestures. Distinguishing and recognizing hand gestures is vital in hotspot fields such as bionic parts, powered exoskeleton, diagnosing muscle disorders, etc. Recognizing such gesture patterns can also create a stress-free and fancy user interface for mobile phones, gaming consoles and other such devices.
The objective is to design a simple yet efficient wearable hand gesture recognizing system. This thesis also shows that by taking both EMG and accelerometer data into account, can improve the system to recognize more patterns with higher accuracy levels. For this, a hand band embedded with a triple axis accelerometer and three surface EMG electrodes is employed to source the system. The non-invasive surface EMG electrodes senses muscle action while the accelerometer senses the hand motions. The EMG signal is passed through analog front-end module for noise filtering and signal amplification. An ARM Cortex processor converts the analog EMG and accelerometer signal into digital and transmits to a PC via Bluetooth protocol. On the receiver section, the raw EMG and acceleration data is further processed and decomposed offline using MATLAB tools to extract features such as root mean square, waveform length, threshold crossing, variance and mean. Extracted features are then fed through multi-class SVM (Support Vector Machine) process for pattern recognition. The chapters below discuss in greater detail on pattern recognition technique and other modules involved
Novel Muscle Monitoring by Radiomyography(RMG) and Application to Hand Gesture Recognition
Conventional electromyography (EMG) measures the continuous neural activity
during muscle contraction, but lacks explicit quantification of the actual
contraction. Mechanomyography (MMG) and accelerometers only measure body
surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic
snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle
actuation sensing that can be wearable and touchless, capturing both
superficial and deep muscle groups. We verified RMG experimentally by a forearm
wearable sensor for detailed hand gesture recognition. We first converted the
radio sensing outputs to the time-frequency spectrogram, and then employed the
vision transformer (ViT) deep learning network as the classification model,
which can recognize 23 gestures with an average accuracy up to 99% on 8
subjects. By transfer learning, high adaptivity to user difference and sensor
variation were achieved at an average accuracy up to 97%. We further
demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for
eye movement and body postures tracking. RMG can be used with synchronous EMG
to derive stimulation-actuation waveforms for many future applications in
kinesiology, physiotherapy, rehabilitation, and human-machine interface
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