MMG measurement: a high-sensitivity microphone-based sensor for clinical use

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review is to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, sign language recognition, and human-computer interaction. Results showed that electrical, dynamic, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.

Control Methods for Transradial Prostheses Based on Remnant Muscle Activity and Its Relationship with Proprioceptive Feedback

The loss of a hand can significantly affect one's work and social life. For many patients, an artificial limb can improve their mobility and ability to manage everyday activities, as well as provide the means to remain independent. This paper provides an extensive review of available biosensing methods to implement the control system for transradial prostheses based on the measured activity in remnant muscles. Covered techniques include electromyography, magnetomyography, electrical impedance tomography, capacitance sensing, near-infrared spectroscopy, sonomyography, optical myography, force myography, phonomyography, myokinetic control, and modern approaches to cineplasty. The paper also covers combinations of these approaches, which, in many cases, achieve better accuracy while mitigating the weaknesses of individual methods. The work is focused on the practical applicability of the approaches, and analyses present challenges associated with each technique along with their relationship with proprioceptive feedback, which is an important factor for intuitive control over the prosthetic device, especially for high dexterity prosthetic hands.

Implementation of Hand Gesture Recognition Device Applicable to Smart Watch Based on Flexible Epidermal Tactile Sensor Array

Ever since the development of digital devices, the recognition of human gestures has played an important role in many Human-Computer interface applications. Various wearable devices have been developed, and inertial sensors, magnetic sensors, gyro sensors, electromyography, force-sensitive resistors, and other types of sensors have been used to identify gestures. However, there are different drawbacks for each sensor, which affect the detection of gestures. In this paper, we present a new gesture recognition method using a Flexible Epidermal Tactile Sensor based on strain gauges to sense deformation. Such deformations are transduced to electric signals. By measuring the electric signals, the sensor can estimate the degree of deformation, including compression, tension, and twist, caused by movements of the wrist. The proposed sensor array was demonstrated to be capable of analyzing the eight motions of the wrist, and showed robustness, stability, and repeatability throughout a range of experiments aimed at testing the sensor array. We compared the performance of the prototype device with those of previous studies, under the same experimental conditions. The result shows our recognition method significantly outperformed existing methods.

Simultaneous Measurement of Swallowing Sound and Mechanomyogram of Submental Muscle with PVDF Film

The difficulty of swallowing, called dysphagia, causes aspiration pneumonia which is particularly a big health concern in aging society. Therefore, prevention and treatment of dysphagia would contribute to extending healthy-life and QOL of elderly people and decreasing healthcare cost. Conventional reliable methods for evaluating swallowing function require special equipment and are not suitable for long-term monitoring at home or welfare facilities. Therefore, various kinds of quantitative assessment method using biological signals such as swallowing sound, electromyography, and so forth have been proposed as a non-invasive and accessible method. The goal of this study is to realize comprehensive quantitative assessment of swallowing function using multiple biological signals simultaneously measured by a single sensor device. In this study, we propose the use of PolyVinylidene DiFluoride (PVDF) film to measure both mechanomyogram (MMG) signal for evaluating muscle activity and swallowing sound for detecting swallowing sequence. In our previous study, we confirmed PVDF film can detect MMG signal of swallowing-related muscles. We conducted experiments to confirm that PVDF film can detect swallowing sound in this study. The experimental results indicated that swallowing sound can be measured in parallel with MMG signal at the same position by changing frequency band of the signal of PVDF film.

Mechanomyographic parameter extraction methods: an appraisal for clinical applications

The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity.

Laser Doppler vibrometry measurement of the mechanical myogram

Contracting muscles show complex dimensional changes that include lateral expansion. Because this expansion process is intrinsically vibrational, driven by repetitive actions of multiple motor units, it can be sensed and quantified using the method of Laser Doppler Vibrometry (LDV). LDV has a number of advantages over more traditional mechanical methods based on microphones and accelerometers. The LDV mechanical myogram from a small hand muscle (the first dorsal interosseous) was studied under conditions of elastic loading applied to the tip of the abducted index finger. The LDV signal was shown to be related systematically to the level of force production, and to compare favorably with conventional methods for sensing the mechanical and electrical aspects of muscle contraction.

Design and evaluation of a novel microphone-based mechanomyography sensor with cylindrical and conical acoustic chambers

Mechanomyography has recently been proposed as a control modality for alternative access technologies for individuals with disabilities. However, MMG recordings are highly susceptible to contamination from limb movements. Pressure-based transducers are touted to be the most robust to external movement although there is some debate about their optimal chamber geometry, in terms of low frequency gain and spectral flatness. To investigate the question of preferred geometry, transducers with cylindrical and conical chambers of varying dimensions were designed, manufactured and tested. Using a computer-controlled electrodynamic shaker, the frequency response of each chamber geometry was empirically derived. Of the cylindrical chambers, the highest gain and the flattest frequency response was exhibited by a chamber 10 mm in diameter and 5-7 mm in height. However, conical chambers offered an average rise in gain of 6.79 ± 1.06 dB/Hz over that achievable with cylindrical geometries. The highest gain and flattest response was achieved with a transducer consisting of a low-frequency MEMS microphone, a 4 μm aluminized mylar membrane and a rigid conical chamber 7 mm in diameter and 5mm in height. This design is recommended for MMG applications where limb movement is prevalent.

A review of non-invasive techniques to detect and predict localised muscle fatigue

Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results.

Acoustic detection of coronary artery disease

Coronary artery disease (CAD) occurs when the arteries to the heart (the coronary arteries) become blocked by deposition of plaque, depriving the heart of oxygen-bearing blood. This disease is arguably the most important fatal disease in industrialized countries, causing one-third to one-half of all deaths in persons between the ages of 35 and 64 in the United States. Despite the fact that early detection of CAD allows for successful and cost-effective treatment of the disease, only 20% of CAD cases are diagnosed prior to a heart attack. The development of a definitive, noninvasive test for detection of coronary blockages is one of the holy grails of diagnostic cardiology. One promising approach to detecting coronary blockages noninvasively is based on identifying acoustic signatures generated by turbulent blood flow through partially occluded coronary arteries. In fact, no other approach to the detection of CAD promises to be as inexpensive, simple to perform, and risk free as the acoustic-based approach. Although sounds associated with partially blocked arteries are easy to identify in more superficial vessels such as the carotids, sounds from coronary arteries are very faint and surrounded by noise such as the very loud valve sounds. To detect these very weak signals requires sophisticated signal processing techniques. This review describes the work that has been done in this area since the 1980s and discusses future directions that may fulfill the promise of the acoustic approach to detecting coronary artery disease.

A self-contained, mechanomyography-driven externally powered prosthesis

The measurement of the low-frequency (5-50 Hz) "sounds" or vibrations produced by contracting muscles is termed mechanomyography (MMG). As a control signal for powered prostheses, MMG offers several advantages over conventional myoelectric control, including, nonspecific sensor placement, distal signal measurement, robustness to changing skin impedance, and reduced sensor costs. The objectives of this study were to demonstrate 2-function prosthesis control based on a triplet of distally recorded, normalized root mean square MMG signals and to identify necessary future research toward full clinical implementation of MMG signals in upper-limb externally powered prostheses. A novel self-contained MMG-driven prosthesis for below-elbow amputees was designed, implemented, and preliminarily tested on 2 subjects. This prosthesis was composed of specialized software and hardware modules that emulate a 2-site electromyography sensing system. Although the use of MMG signals for prosthesis control has been shown previously, we report, for the first time, successful control within a self-contained unit in unconstrained environments. Specifically, essential requirements for practical use, such as standardized sensor attachment, basic noise elimination, and miniaturization of the system, have been achieved. Both subjects were able to voluntarily open and close the prosthesis hand with no significant delays from intention to action (approximately 120 ms). Quantitative analyses revealed 88% and 71% control accuracy for subjects 1 and 2, respectively.

Surface electromyography and mechanomyography recording: a new differential composite probe

The objective of the study was to develop a new surface probe for differential mechanomyographic (MMG) and electromyographic (EMG) recording. Differential amplification is commonly used in electromyography to improve the signal-to-noise ratio. A new composite probe was developed with two electrodes (EMG) and two identical piezo-electric membranes (MMG) to be positioned on muscle. The probe had two built-in fixed-gain differential amplifiers: one to amplify the electric signal and the other to amplify the vibration signal. A similar non-differential MMG probe was used for comparisons. Burst muscular activity was recorded using the non-differential and differential probes and was used to test the performance of the two probes in suppressing artifacts of non-muscular origin. Power spectrum analysis of signals from the two probes showed that differential amplification significantly improved the signal-to-noise ratio in MMG recordings and significantly suppressed artifacts (power difference > 90%). The composite probe allowed simultaneous differential recording of MMG and EMG signals from the same muscular site. It recorded muscular activity more efficiently than the non-differential probe and could therefore be useful in studying fatigue and neuromuscular diseases.

Systematic characterisation of silicon-embedded accelerometers for mechanomyography

Silicon soft suction sockets (roll-on sleeves) currently used in passive prostheses for below-elbow amputees could also be used in externally powered prostheses, enhancing their functionality and comfort. However, as it is extremely difficult to hold currently used electromyography (EMG) sensors in place reliably within a silicon socket, an alternative measurement of muscular activity as the control input is necessary. Mechanomyography (MMG) is the epidermal measurement of the low-frequency vibrations produced by a contracting muscle. MMG sensors do not have to be in direct contact with the skin. Moreover, the embedding of sensors in the roll-on sleeve may also solve attachment issues, making sensor placement flexible. Therefore the objective was to determine the feasibility of recording MMG signals using silicon-embedded, micro-machined accelerometers. Fifteen embedded accelerometers were excited with predefined vibration patterns. The signal-to-noise ratio (SNR) and frequency response of each sample were measured and compared with those of non-embedded accelerometers. The SNR of embedded samples (approximately equal to 19 dB) was significantly higher than that of non-embedded samples (approximately equal to 12 dB), owing to the considerable mechanical damping effect of the silicon in the 300-900 Hz bandwidth (p=0.0028). This has implications for the application of silicon-embedded accelerometers for externally powered prosthesis control.

Surface myomechanical responses recorded on a scanner galvanometer

A moving magnet galvanometer equipped with lever and indentor was evaluated for mechanomyography (MMG). First, the precision of the galvanometer was tested on a piezo-electric disc actuator. Using a 50 mm lever, synthesised micromotions with an amplitude of 1 microm could be detected (noise level < 0.2 microm) at static indentation forces ranging from 0.1 to 2 N. Then the galvanometer was mounted on an isometric ankle dynamometer to sense calf-muscle responses (N = 6). In the first protocol, twitch contractions were elicited by electrical stimulation while the indentation force was increased. Twitch amplitudes, twitch contraction times and twitch half-relaxation times were analysed from the surface and contraction responses. With indentation force (0.1-0.5 N), the amplitude of the surface responses increased (+61%), contraction and half-relaxation times, however, were not influenced. The mean twitch contraction time from the surface responses (60 +/- 11 ms) was shorter than that from the contraction responses (115 +/- 7 ms), indicating more fast-contracting fibres under the indented area. In the second protocol, voluntary target contractions were produced, and the surface responses were simultaneously recorded on an accelerometer. After double differentiation of the galvanometer signal, both acceleration MMGs showed a high coincidence in the time and frequency domains. With an indentation force of 2 N applied on the accelerometer, the signal amplitude (-10%) and the mean frequency (-19%) decreased. A specific application of this galvanometer-dynamometer test system is the assessment of regeneration processes in paraplegics with long-term denervated muscles.

Mechanical behaviour of condenser microphone in mechanomyography

Condenser microphones (MIC) have been widely used in mechanomyography, together with accelerometers and piezoelectric contact sensors. The aim of the present investigation was to clarify the mechanical variable (acceleration, velocity or displacement) indicated by the signal from a MIC transducer using a mechanical sinusoidal vibration system. In addition, the mechanomyogram (MMG) was recorded simultaneously with a MIC transducer and accelerometer (ACC) during voluntary contractions to confirm the mechanical variable reflected by the actual MMG and to examine the influence of motion artifact on the MMG. To measure the displacement-frequency response, mechanical sinusoidal vibrations of 3 to 300 Hz were applied to the MIC transducer with different sizes of air chambers (5, 10, 15 and 20 mm in diameter and 15, 20 or 25 mm long). The MIC transducer showed a linear relationship between the output amplitude and the vibration displacement, however, its frequency response declined with decreasing diameter and decreasing length of the air chamber. In fact, the cut-off frequency (-3dB) of the MIC transducer with the 5-mm-diameter chamber was 10, 8 and 4 Hz for the length 15, 20 and 25 mm, respectively. The air chamber with at least a diameter of 10 mm and a length of 15 mm is recommended for the MIC transducer. The sensitivity of this MIC transducer arrangement was 92 mV microm(-1) when excited at 100 Hz. During voluntary contraction, the amplitude spectral density function of the MMG from the MIC transducer resembled that of the double integral of the ACC transducer signal. The angle of the MIC transducer was delayed by 180 degrees in relation to the ACC transducer signal. The sensitivity of the MIC transducer was reduced to one-third because of the peculiar volume change of air chamber when the MMG was detected on the surface of the skin. In addition, the MIC transducer was contaminated by a smaller motion artifact than that from the ACC transducer. The maximal peak amplitude of the MIC and ACC transducer signal with the motion artifact was 7.7 and 12.3 times as much as the RMS amplitude of each signal without the motion artifact, respectively. These findings suggest that the MIC transducer acts as a displacement meter in the MMG. The MIC transducer seems to be a possible candidate for recording the MMG during dynamic muscle contractions as well as during sustained contractions.