HD-sEMG for Human Interfacing Devices

Beauchamp, Brendan P.; Kandalaft, Nabeeh

doi:10.1109/iemcon.2019.8936298

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…While the act recognizes the benefits of health data collection, it emphasizes the control of the user in ownership of PHI by requiring the deletion of the data after the health crisis is over. The mandate of reports on civil rights infringement is very important in increasing human interfacing bandwidth [20]- [25]. If the public is not accurately informed on a technology, civil rights, and ownership can slip through the cracks of lexicon and protocol black boxing.…”

Section: Privacy Of Personal Health Informationmentioning

confidence: 99%

Privacy of biofeedback human interfacing devices

Beauchamp

Corneal

Kandalaft

2022

IJAAS

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<span>The public and governmental focus have been shifted increasingly onto data and privacy due to Facebook’s standoff with Apple and several Nations’ Governments. An imperative for the discussion of data handling has been made, while Biofeedback human interfacing device (HID) companies such as CTRL-labs and Neuralink introduce new risks in the access, use, and control of personal health information. This paper combines the lexicon of digital self-extension with line drawing analysis to help visualize the industry use of personally identifiable information (PII). Through this analysis encapsulation methods of users' PII have been identified such that discussion of encrypting personal health information (PHI) can be facilitated through analogy. To reduce the likelihood that companies generating biometric hardware are subject to future legal action through laws analogous to European Privacy Law, it is in their best interest to be transparent with their users about data sharing, and educate them on how the companies encrypt their PHI. Should users consent to the use of biometric hardware, this process validates the user control of their own PHI.</span>

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Section: Privacy Of Personal Health Informationmentioning

confidence: 99%

Privacy of biofeedback human interfacing devices

Beauchamp

Corneal

Kandalaft

2022

IJAAS

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“…The accurate decoding of sEMG signals is essential to distinguish these aspects. Moreover, applications like sign language recognition [ 7 ] and human–computer interaction [ 8 ] also rely on precise decoding of sEMG signals [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals

Moslhi,

Aly,

ElMessiery

2024

Sensors

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Interest in developing techniques for acquiring and decoding biological signals is on the rise in the research community. This interest spans various applications, with a particular focus on prosthetic control and rehabilitation, where achieving precise hand gesture recognition using surface electromyography signals is crucial due to the complexity and variability of surface electromyography data. Advanced signal processing and data analysis techniques are required to effectively extract meaningful information from these signals. In our study, we utilized three datasets: NinaPro Database 1, CapgMyo Database A, and CapgMyo Database B. These datasets were chosen for their open-source availability and established role in evaluating surface electromyography classifiers. Hand gesture recognition using surface electromyography signals draws inspiration from image classification algorithms, leading to the introduction and development of the Novel Signal Transformer. We systematically investigated two feature extraction techniques for surface electromyography signals: the Fast Fourier Transform and wavelet-based feature extraction. Our study demonstrated significant advancements in surface electromyography signal classification, particularly in the Ninapro database 1 and CapgMyo dataset A, surpassing existing results in the literature. The newly introduced Signal Transformer outperformed traditional Convolutional Neural Networks by excelling in capturing structural details and incorporating global information from image-like signals through robust basis functions. Additionally, the inclusion of an attention mechanism within the Signal Transformer highlighted the significance of electrode readings, improving classification accuracy. These findings underscore the potential of the Signal Transformer as a powerful tool for precise and effective surface electromyography signal classification, promising applications in prosthetic control and rehabilitation.

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