Optomyography (OMG) : A Novel Technique for the Detection of Muscle Surface Displacement Using Photoelectric Sensors (original) (raw)
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Journal of Innovative Optical Health Sciences, 2017
Noninvasive techniques, surface electromyography (sEMG) in particular, are being increasingly employed for assessing muscle activity. In these studies, local oxygen consumption and muscle metabolism are of great interest. Measurements can be performed noninvasively using optics-based methods such as near-infrared spectroscopy (NIRS). By combining energy consumption data provided by NIRS with muscle level activation data from sEMG, we may gain an insight into the metabolic and functional characteristics of muscle tissue. However, muscle motion may induce artifacts into EMG and NIRS. Thus, the inclusion of simultaneous motion measurements using accelerometers (ACMs) enhances possibilities to perceive the effects of motion on NIRS and EMG signals. This paper reviews the current state of noninvasive EMG and NIRS-based methods used to study muscle function. In addition, we built a combined sEMG/NIRS/ACM sensor to perform simultaneous measurements for static and dynamic exercises of a bic...
On Lightmyography: A New Muscle Machine Interfacing Method for Decoding Human Intention and Motion
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
Recognising and classifying human hand gestures is important for effective communication between humans and machines in applications such as human-robot interaction, human to robot skill transfer, and control of prosthetic devices. Although there are already many interfaces that enable decoding of the intention and action of humans, they are either bulky or they rely on techniques that need careful positioning of the sensors, causing inconvenience when the system needs to be used in real-life scenarios and environments. Moreover, electromyography (EMG), which is the most commonly used technique, captures EMG signals that have a nonlinear relationship with the human intention and motion. In this work, we present lightmyography (LMG) a new muscle machine interfacing method for decoding human intention and motion. Lightmyography utilizes light propagation through elastic media and the change of light luminosity to detect silicone deformation. Lightmyography is similar to forcemyography in the sense that they both record muscular contractions through skin displacements. In order to experimentally validate the efficiency of the proposed method, we designed an interface consisting of five LMG sensors to perform gesture classification experiments. Using this device, we were able to accurately detect a series of different hand postures and gestures. We also compared LMG data with processed EMG data.
Journal of Biomedical Optics, 2016
Ambulatory diffuse optical tomography (aDOT) is based on near-infrared spectroscopy (NIRS) and enables three-dimensional imaging of regional hemodynamics and oxygen consumption during a person's normal activities. Although NIRS has been previously used for muscle assessment, it has been notably limited in terms of the number of channels measured, the extent to which subjects can be ambulatory, and/or the ability to simultaneously acquire synchronized auxiliary data such as electromyography (EMG) or electrocardiography (ECG). We describe the development of a prototype aDOT system, called NINscan-M, capable of ambulatory tomographic imaging as well as simultaneous auxiliary multimodal physiological monitoring. Powered by four AA size batteries and weighing 577 g, the NINscan-M prototype can synchronously record 64-channel NIRS imaging data, eight channels of EMG, ECG, or other analog signals, plus force, acceleration, rotation, and temperature for 24+ h at up to 250 Hz. We describe the system's design, characterization, and performance characteristics. We also describe examples of isometric, cycle ergometer, and free-running ambulatory exercise to demonstrate tomographic imaging at 25 Hz. NINscan-M represents a multiuse tool for muscle physiology studies as well as clinical muscle assessment.
Scientific Reports, 2023
Conventional muscle-machine interfaces like Electromyography (EMG), have significant drawbacks, such as crosstalk, a non-linear relationship between the signal and the corresponding motion, and increased signal processing requirements. In this work, we introduce a new muscle-machine interfacing technique called lightmyography (LMG), that can be used to efficiently decode human hand gestures, motion, and forces from the detected contractions of the human muscles. LMG utilizes light propagation through elastic media and human tissue, measuring changes in light luminosity to detect muscle movement. Similar to forcemyography, LMG infers muscular contractions through tissue deformation and skin displacements. In this study, we look at how different characteristics of the light source and silicone medium affect the performance of LMG and we compare LMG and EMG based gesture decoding using various machine learning techniques. To do that, we design an armband equipped with five LMG modules, and we use it to collect the required LMG data. Three different machine learning methods are employed: Random Forests, Convolutional Neural Networks, and Temporal Multi-Channel Vision Transformers. The system has also been efficiently used in decoding the forces exerted during power grasping. The results demonstrate that LMG outperforms EMG for most methods and subjects. Rapid breakthroughs in robotics have highlighted the importance of effective interaction and communication between humans and machines, with various robotic devices being introduced to a range of industries such as housing, hospitality, and medical devices 1-3. Traditionally, a user makes a decision and communicates it to the device via an interface, and the device responds only to the provided command. More advanced systems, on the other hand, only need raw data from the user to make decisions automatically using machine learning (ML) techniques 4. This makes the human-machine interface (HMI) a crucial part of the system both for data acquisition and communication. Various HMIs have been developed employing various methods and concepts based on the specific needs of the user 5-7. Handheld controllers are the most prevalent type of HMI, and they are widely used in various applications due to their ease of learning and operation 8,9. However, they occupy the user's hands, can induce fatigue with long-term use, and may not be practical and intuitive enough for devices such as prostheses. Another method of controlling a robotic device is by using vision-based systems 10,11. Such systems are relatively reliable and typically do not interfere with the user's workspace; nonetheless, they are susceptible to occlusion and changes in environment lighting. Human-machine interaction is also possible through voice commands 12,13. This is a popular interface, although, as with vision-based systems, noise from the surroundings can interfere with communication. Wearable devices such as eyeglasses, gloves, and electroencephalography (EEG) helmets are other types of HMIs that can control a machine based on the user's movements or biological signals 14-17. They also exist in the form of armbands that capture data from the user's forearm and use machine learning techniques to predict the intended gestures of the user. Recent research has applied Deep Learning approaches
Laser doppler myography (LDMi): A novel non-contact measurement method for the muscle activity
Laser therapy, 2013
Electromyography (EMG) is considered the gold-standard for the evaluation of muscle activity. Transversal and dimensional changes of the muscle, during muscle activity, generate vibrational phenomena which can be measured by Laser Doppler Vibrometry (LDVi). There is a relationship between muscle contraction and vibrational activity, therefore, some information on fundamental muscle parameters can be assessed without contact with LDVi. In this paper, we explore the possibility to relate the EMG signal causing the muscle contraction and the vibrational activity also measureable on the muscle. A novel non-contact measurement method - Laser Doppler myography (LDMi) - aiming to measure the vibrational behavior of muscle during contraction, is presented herein. Correlations with some parameters normally measured with EMG are reported. The proposed method has been compared with standard superficial EMG (sEMG). Signals produced with sEMG and laser Doppler myography have been simultaneously ...
Muscle Sensor Model Using Small Scale Optical Device for Pattern Recognitions
The Scientific World Journal, 2013
A new sensor system for measuring contraction and relaxation of muscles by using a PANDA ring resonator is proposed. The small scale optical device is designed and configured to perform the coupling effects between the changes in optical device phase shift and human facial muscle movement, which can be used to form the relationship between optical phase shift and muscle movement. By using the Optiwave and MATLAB programs, the results obtained have shown that the measurement of the contraction and relaxation of muscles can be obtained after the muscle movements, in which the unique pattern of individual muscle movement from facial expression can be established. The obtained simulation results, that is, interference signal patterns, can be used to form the various pattern recognitions, which are useful for the human machine interface and the human computer interface application and discussed in detail.
2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH), 2009
A study was conducted to investigate the effect of upper limb muscles during repetitive task consisted of three handarm directions: 30 0 , 90 0 , and 150 0 . The aims of this study were to investigate muscle activity during repetitive task, to compare muscle activity between genders and to determine the correlation between muscles and time. Biceps brachii, anterior deltoid, and upper trapezius muscles were identified as a significant muscle in the repetitive light task experiment. Surface electromyography (SEMG) measurements for each muscle mentioned above were taken from ten subjects in the duration of one hour experiment. The results indicated that anterior deltoid is the highest affected muscle by the tasks. There was a significantly increase of root mean square (RMS) between the beginning and the end of the experiment, that indicated muscle fatigue. However there were no significant differences of RMS between male and female subjects, and between 30 0 , 90 0 and 150 0 hand-arm directions.
Journal of Biomedical Optics, 2010
We introduce a method for noninvasively measuring muscle contraction in vivo, based on near-infrared diffusing-wave spectroscopy ͑DWS͒. The method exploits the information about time-dependent shear motions within the contracting muscle that are contained in the temporal autocorrelation function g ͑1͒ ͑ , t͒ of the multiply scattered light field measured as a function of lag time, , and time after stimulus, t. The analysis of g ͑1͒ ͑ , t͒ measured on the human M. biceps brachii during repetitive electrical stimulation, using optical properties measured with time-resolved reflectance spectroscopy, shows that the tissue dynamics giving rise to the speckle fluctuations can be described by a combination of diffusion and shearing. The evolution of the tissue Cauchy strain e͑t͒ shows a strong correlation with the force, indicating that a significant part of the shear observed with DWS is due to muscle contraction. The evolution of the DWS decay time shows quantitative differences between the M. biceps brachii and the M. gastrocnemius, suggesting that DWS allows to discriminate contraction of fast-and slow-twitch muscle fibers.