A new approach for rehabilitation and upper-limb prosthesis control using optomyography (OMG)

Muhammed, Hamed Hamid; Raghavendra, Jammalamadaka

doi:10.1109/ibiomed.2016.7869814

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…The glasses contain seven OCO TM optomyographic sensors [15], a 9-axis IMU, altimeter and dual microphones. The sensors are built into a glasses frame to overlap key facial muscle groups associated with the affective changes (see OCO TM Sensors section) [13].…”

Section: Ocosense Tm Glassesmentioning

confidence: 99%

“…The OCO TM sensors are a proprietary sensor developed and patented by Emteq Labs (UK patent No.US11,003,899), and use an optical non-contact approach -Optomyography [15], that has advantages over EMG-based systems. EMG electrodes require firm and constant contact with skin to achieve an acceptable signal to noise ratio; the OCO TM sensors are optically based, therefore they do not require skin contact, and can function accurately from 4mm to 30mm from the skin.…”

Section: The Oco Tm Sensormentioning

confidence: 99%

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OCOsense™ smart glasses for analyzing facial expressions using optomyographic sensors

Archer

Mavridou

Broulidakis

et al. 2023

Preprint

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This article introduces the Emteq's OCOsenseTM smart glasses equipped with a novel non-contact OCOTM sensor technology for measuring facial muscle activation and expressions based on high resolution tracking of skin movement. We demonstrate that the OCOTM sensor technology based on optomyography is a sensitive and accurate approach for assessing skin movement in 3 dimensions, providing a means for measuring the facial expressions used to assess emotional valence such as smile, frown, and eyebrow raise. We propose that glasses-based optomyography sensing has the potential to herald a paradigm shift in real-world facial expression monitoring, thus enabling real-time emotional analytics with healthcare and research applications.

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Section: Ocosense Tm Glassesmentioning

confidence: 99%

Section: The Oco Tm Sensormentioning

confidence: 99%

OCOsense™ smart glasses for analyzing facial expressions using optomyographic sensors

Archer

Mavridou

Broulidakis

et al. 2023

Preprint

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“…Specifically, EMG has the inherent advantages of distinguishing between passive and active activities, predicting movements, and achieving a short calculating time [ 14 ]. Many existing technologies use EMG together with mechano-myographic sensors to acquire signals that can be used to control prosthetic devices, thus enabling people with disabilities to train and restore control over their residual muscles [ 15 ]. For example, Betthauser et al presented a sparsity-based adaptive classification method for the EMG, which aims to achieve a good performance in the offline and online contexts involving untrained upper-limb positions for amputees and able-bodied subjects [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Feature-Level Fusion of Surface Electromyography for Activity Monitoring

Tang

Luo

2018

Sensors

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Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation applications as they reflect effectively the motor intentions of users. However, real-time sEMG signals are non-stationary and vary to a large extent within the time frame of signals. Although previous studies have focused on the issues, their results have not been satisfactory. Therefore, we present a new method of conducting feature-level fusion to obtain a new feature space for sEMG signals. Eight activities of daily life (ADLs), including falls, were performed to obtain raw data from EMG signals from the lower limb. A feature set combining the time domain, time–frequency domain, and entropy domain was applied to the raw data to establish an initial feature space. A new projection method, the weighting genetic algorithm for GCCA (WGA-GCCA), was introduced to obtain the final feature space. Different tests were carried out to evaluate the performance of the new feature space. The new feature space created with the WGA-GCCA effectively reduced the dimensions and selected the best feature vectors dynamically while improving monotonicity. The Davies–Bouldin index (DBI) based on fuzzy c-means algorithms of the space obtained the lowest value compared with several fusion methods. It also achieved the highest accuracy when applied to support vector machine classifier.

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“…In this paper, we present a new multimodal dataset called Genuine Emotion and Expression Detection (MGEED) by using modalities of the optomyography (OMG [25], EEG, ECG signals and facial images, as well as depth maps. The "genuine expressions" can also be referred to as real, natural and spontaneous expressions.…”

mentioning

confidence: 99%

MGEED: A Multimodal Genuine Emotion and Expression Detection Database

Wang

Gao

et al. 2024

IEEE Trans. Affective Comput.

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Multimodal emotion recognition has attracted increasing interest from academia and industry in recent years, since it enables emotion detection using various modalities, such as facial expression images, speech and physiological signals. Although research in this field has grown rapidly, it is still challenging to create a multimodal database containing facial electrical information due to the difficulty in capturing natural and subtle facial expression signals, such as optomyography (OMG) signals. To this end, we present a newly developed Multimodal Genuine Emotion and Expression Detection (MGEED) database in this paper, which is the first publicly available database containing the facial OMG signals. MGEED consists of 17 subjects with over 150K facial images, 140K depth maps and different modalities of physiological signals including OMG, electroencephalography (EEG) and electrocardiography (ECG) signals. The emotions of the participants are evoked by video stimuli and the data are collected by a multimodal sensing system. With the collected data, an emotion recognition method is developed based on multimodal signal synchronisation, feature extraction, fusion and emotion prediction. The results show that superior performance can be achieved by fusing the visual, EEG and OMG features. The database can be obtained from https://github.com/YMPort/MGEED.

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A new approach for rehabilitation and upper-limb prosthesis control using optomyography (OMG)

Cited by 7 publications

References 18 publications

OCOsense™ smart glasses for analyzing facial expressions using optomyographic sensors

OCOsense™ smart glasses for analyzing facial expressions using optomyographic sensors

Feature-Level Fusion of Surface Electromyography for Activity Monitoring

MGEED: A Multimodal Genuine Emotion and Expression Detection Database

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