Mechanical behaviour of condenser microphone in mechanomyography

A Novel Mechanomyography (MMG) Sensor Based on Piezo-Resistance Principle and with a Pyramidic Microarray

Flexible piezoresistive sensors built by printing nanoparticles onto soft substrates are crucial for continuous health monitoring and wearable devices. In this study, a mechanomyography (MMG) sensor was developed using a flexible piezoresistive MMG signal sensor based on a pyramidal polydimethylsiloxane (PDMS) microarray sprayed with carbon nanotubes (CNTs). The experiment was conducted, and the results show that the sensitivity of the sensor can reach 0.4 kPa-1 in the measurement range of 0~1.5 kPa, and the correlation reached 96%. This has further implications for the possibility that muscle activation can be converted into mechanical movement. The integrity of the sensor in terms of its MMG signal acquisition was tested based on five subjects who were performing arm bending and arm extending movements. The results of this test were promising.

Assessing Quadriceps Muscle Contraction Using a Novel Surface Mechanomyography Sensor during Two Neuromuscular Control Screening Tasks

Electromyography (EMG) is the clinical standard for capturing muscle activation data to gain insight into neuromuscular control, yet challenges surrounding data analysis limit its use during dynamic tasks. Surface mechanomyography (sMMG) sensors are novel wearable devices that measure the physical output of muscle excursion during contraction, which may offer potential easy application to assess neuromuscular control. This study aimed to investigate sMMG detection of the timing patterns of muscle contraction compared to EMG. Fifteen healthy participants (mean age = 31.7 ± 9.1 y; eight males and seven females) were donned with EMG and sMMG sensors on their right quadriceps for simultaneous data capture during bilateral deep squats, and a subset performed three sets of repeated unilateral partial squats. No significant difference in the total duration of contraction was detected by EMG and sMMG during bilateral (p = 0.822) and partial (p = 0.246) squats. sMMG and EMG timing did not differ significantly for eccentric (p = 0.414) and concentric (p = 0.462) phases of muscle contraction during bilateral squats. The sMMG magnitude of quadriceps excursion demonstrated excellent intra-session retest reliability for bilateral (ICC_3,1 = 0.962 mm) and partial (ICC_3,1 = 0.936 mm, n = 10) squats. The sMMG sensors accurately and consistently provided key quadriceps muscle performance metrics during two physical activities commonly used to assess neuromuscular control for injury prevention, rehabilitation, and exercise training.

Crosstalk in Mechanomyographic Signals From Elbow Flexor Muscles During Submaximal to Maximal Isometric Flexion, Pronation, and Supination Torque Tasks

This study analyzed the crosstalk in mechanomyographic (MMG) signals from elbow flexors during isometric muscle actions from 20% to 100% maximum voluntary isometric contraction (MVIC). Twenty-five young, healthy, male participants performed the isometric elbow flexion, forearm pronation, and supination tasks at an elbow joint angle of 90 deg. The MMG signals from the biceps brachii (BB), brachialis (BRA), and brachioradialis (BRD) muscles were recorded using accelerometers. The cross-correlation coefficient was used to quantify the crosstalk in MMG signals, recorded in a direction transverse to muscle fiber axis, among the muscle pairs (P1: BB and BRA, P2: BRA and BRD, and P3: BB and BRD). In addition, the MMG RMS and MPF were quantified. The mean normalized RMS and mean MPF exhibited increasing (r > 0.900) and decreasing (r < -0.900) trends, respectively, with increases in the effort levels in all three tasks. The magnitude of crosstalk ranged from 0.915% to 21.565% in all three muscle pairs. The crosstalk was found to exhibit high positive correlations with submaximal to maximal flexion [P1 (r = 0.970), P2 (r = 0.951), and P3 (r = 0.824)], pronation [P1 (r = 0.811), P2 (r = 0.763), and P3 (r = 0.901)] and supination [P1 (r = 0.898), P2 (r = 0.838), and P3 (r = 0.852)] torque levels (eight out of nine p-values were < 0.05). Regardless of the high positive correlation between crosstalk and level of effort, the crosstalk remained at a low range (0.915-21.565%) with increases in the torque levels.

Measurements of Transmission Characteristics Related to Bone-Conducted Speech Using Excitation Signals in the Oral Cavity

Purpose Psychoacoustical studies on transmission characteristics related to bone-conducted (BC) speech, perceived by speakers during vocalization, are important for further understanding the relationship between speech production and perception, especially auditory feedback. For exploring how the outer ear part contributes to BC speech transmission, this article aims to measure the transmission characteristics of bone conduction focusing on the vibration of the regio temporalis (RT) and sound radiation in the ear canal (EC) due to the excitation in the oral cavity (OC). Method While an excitation signal was presented through a loudspeaker located in the enclosed cavity below the hard palate, transmitted signals were measured on the RT and in the EC. The transfer functions of the RT vibration and EC sound pressure relative to OC sound pressure were determined from the measured signals using the sweep-sine method. Results Our findings obtained from the measurements of five participants are as follows: (a) the transfer function of the RT vibration relative to the OC sound pressure attenuated the frequency components above 1 kHz and (b) the transfer function of the EC relative to the OC sound pressure emphasized the frequency components between 2 and 3 kHz. Conclusions The vibration of the soft tissue or the skull bone has an effect of low-pass filtering, whereas the sound radiation in the EC has an effect of 2-3 kHz bandpass filtering. Considering the perceptual effect of low-pass filtering in BC speech, our findings suggest that the transmission to the outer ear may not be a dominant contributor to BC speech perception during vocalization.

Wearable MMG-Plus-One Armband: Evaluation of Normal Force on Mechanomyography (MMG) to Enhance Human-Machine Interfacing

In this paper, we introduce a new mode of mechanomyography (MMG) signal capture for enhancing the performance of human-machine interfaces (HMIs) through modulation of normal pressure at the sensor location. Utilizing this novel approach, increased MMG signal resolution is enabled by a tunable degree of freedom normal to the sensor-skin contact area. We detail the mechatronic design, experimental validation, and user study of an armband with embedded acoustic sensors demonstrating this capacity. The design is motivated by the nonlinear viscoelasticity of the tissue, which increases with the normal surface pressure. This, in theory, results in higher conductivity of mechanical waves and hypothetically allows to interface with deeper muscle; thus, enhancing the discriminative information context of the signal space. Ten subjects (seven able-bodied and three trans-radial amputees) participated in a study consisting of the classification of hand gestures through MMG while increasing levels of contact force were administered. Four MMG channels were positioned around the forearm and placed over the flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris muscles. A total of 852 spectrotemporal features were extracted (213 features per each channel) and passed through a Neighborhood Component Analysis (NCA) technique to select the most informative neurophysiological subspace of the features for classification. A linear support vector machine (SVM) then classified the intended motion of the user. The results indicate that increasing the normal force level between the MMG sensor and the skin can improve the discriminative power of the classifier, and the corresponding pattern can be user-specific. These results have significant implications enabling embedding MMG sensors in sockets for prosthetic limb control and HMI.

Ultrasound Measurement of Skeletal Muscle Contractile Parameters Using Flexible and Wearable Single-Element Ultrasonic Sensor

Skeletal muscle is considered as a near-constant volume system, and the contractions of the muscle are related to the changes in tissue thickness. Assessment of the skeletal muscle contractile parameters such as maximum contraction thickness (Th), contraction time (Tc), contraction velocity (Vc), sustain time (Ts), and half-relaxation (Tr) provides valuable information for various medical applications. This paper presents a single-element wearable ultrasonic sensor (WUS) and a method to measure the skeletal muscle contractile parameters in A-mode ultrasonic data acquisition. The developed WUS was made of double-layer polyvinylidene fluoride (PVDF) piezoelectric polymer films with a simple and low-cost fabrication process. A flexible, lightweight, thin, and small size WUS would provide a secure attachment to the skin surface without affecting the muscle contraction dynamics of interest. The developed WUS was employed to monitor the contractions of gastrocnemius (GC) muscle of a human subject. The GC muscle contractions were evoked by the electrical muscle stimulation (EMS) at varying EMS frequencies from 2 Hz up to 30 Hz. The tissue thickness changes due to the muscle contractions were measured by utilizing a time-of-flight method in the ultrasonic through-transmission mode. The developed WUS demonstrated the capability to monitor the tissue thickness changes during the unfused and fused tetanic contractions. The tetanic progression level was quantitatively assessed using the parameter of the fusion index (FI) obtained. In addition, the contractile parameters (Th, Tc, Vc, Ts, and Tr) were successfully extracted from the measured tissue thickness changes. In addition, the unfused and fused tetanus frequencies were estimated from the obtained FI-EMS frequency curve. The WUS and ultrasonic method proposed in this study could be a valuable tool for inexpensive, non-invasive, and continuous monitoring of the skeletal muscle contractile properties.

Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist

To facilitate hand gesture recognition, we investigated the use of acoustic signals with an accelerometer and gyroscope at the human wrist. As a proof-of-concept, the prototype consisted of 10 microphone units in contact with the skin placed around the wrist along with an inertial measurement unit (IMU). The gesture recognition performance was evaluated through the identification of 13 gestures used in daily life. The optimal area for acoustic sensor placement at the wrist was examined using the minimum redundancy and maximum relevance feature selection algorithm. We recruited 10 subjects to perform over 10 trials for each set of hand gestures. The accuracy was 75% for a general model with the top 25 features selected, and the intra-subject average classification accuracy was over 80% with the same features using one microphone unit at the mid-anterior wrist and an IMU. These results indicate that acoustic signatures from the human wrist can aid IMU sensing for hand gesture recognition, and the selection of a few common features for all subjects could help with building a general model. The proposed multimodal framework helps address the single IMU sensing bottleneck for hand gestures during arm movement and/or locomotion.

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.

An Individual Finger Gesture Recognition System Based on Motion-Intent Analysis Using Mechanomyogram Signal

Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results.

System identification of velocity mechanomyogram measured with a capacitor microphone for muscle stiffness estimation

A mechanomyogram (MMG) measured with a displacement sensor (displacement MMG) can provide a better estimation of longitudinal muscle stiffness than that measured with an acceleration sensor (acceleration MMG), but the displacement MMG cannot provide transverse muscle stiffness. We propose a method to estimate both longitudinal and transverse muscle stiffness from a velocity MMG using a system identification technique. The aims of this study are to show the advantages of the proposed method. The velocity MMG was measured using a capacitor microphone and a differential circuit, and the MMG, evoked by electrical stimulation, of the tibialis anterior muscle was measured five times in seven healthy young male volunteers. The evoked MMG system was identified using the singular value decomposition method and was approximated with a fourth-order model, which provides two undamped natural frequencies corresponding to the longitudinal and transverse muscle stiffness. The fluctuation of the undamped natural frequencies estimated from the velocity MMG was significantly smaller than that from the acceleration MMG. There was no significant difference between the fluctuations of the undamped natural frequencies estimated from the velocity MMG and that from the displacement MMG. The proposed method using the velocity MMG is thus more advantageous for muscle stiffness estimation.

Muscle stiffness estimation using a system identification technique applied to evoked mechanomyogram during cycling exercise

The aims of this study were to develop a method to extract the evoked mechanomyogram (MMG) during cycling exercise and to clarify muscle stiffness at various cadences, workloads, and power. Ten young healthy male participants were instructed to pedal a cycle ergometer at cadences of 40 and 60 rpm. The loads were 4.9, 9.8, 14.7, and 19.6 N, respectively. One electrical stimulus per two pedal rotations was applied to the vastus lateralis muscle at a knee angle of 80° in the down phase. MMGs were measured using a capacitor microphone, and the MMGs were divided into stimulated and non-stimulated sequences. Each sequence was synchronously averaged. The synchronously averaged non-stimulated MMG was subtracted from the synchronously averaged stimulated MMG to extract an evoked MMG. The evoked MMG system was identified and the poles of the transfer function were calculated. The poles and mass of the vastus lateralis muscle were used to estimate muscle stiffness. Results showed that muscle stiffness was 186-626 N /m and proportional to the workloads and power. In conclusion, our method can be used to assess muscle stiffness proportional to the workload and power.

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.

Planar microphone based on piezoelectric electrospun poly(γ-benzyl-α,L-glutamate) nanofibers

Velocity and pressure microphones composed of piezoelectric poly(γ-benzyl-α,L-glutamate) (PBLG) nanofibers were produced by adhering a single layer of PBLG film to a Mylar diaphragm. The device exhibited a sensitivity of -60 dBV/Pa in air, and both pressure and velocity response showed a broad frequency response that was primarily controlled by the stiffness of the supporting diaphragm. The pressure microphone response was ±3 dB between 200 Hz and 4 kHz when measured in a semi-anechoic chamber. Thermal stability, easy fabrication, and simple design make this single element transducer ideal for various applications including those for underwater and high temperature use.

Mechanomyography and muscle function assessment: a review of current state and prospects

Previous studies have explored to saturation the efficacy of the conventional signal (such as electromyogram) for muscle function assessment and found its clinical impact limited. Increasing demand for reliable muscle function assessment modalities continues to prompt further investigation into other complementary alternatives. Application of mechanomyographic signal to quantify muscle performance has been proposed due to its inherent mechanical nature and ability to assess muscle function non-invasively while preserving muscular neurophysiologic information. Mechanomyogram is gaining accelerated applications in evaluating the properties of muscle under voluntary and evoked muscle contraction with prospects in clinical practices. As a complementary modality and the mechanical counterpart to electromyogram; mechanomyogram has gained significant acceptance in analysis of isometric and dynamic muscle actions. Substantial studies have also documented the effectiveness of mechanomyographic signal to assess muscle performance but none involved comprehensive appraisal of the state of the art applications with highlights on the future prospect and potential integration into the clinical practices. Motivated by the dearth of such critical review, we assessed the literature to investigate its principle of acquisition, current applications, challenges and future directions. Based on our findings, the importance of rigorous scientific and clinical validation of the signal is highlighted. It is also evident that as a robust complement to electromyogram, mechanomyographic signal may possess unprecedented potentials and further investigation will be enlightening.

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.

System identification of evoked mechanomyogram from abductor pollicis brevis muscle in isometric contraction

The purpose of this study is to verify the applicability of a sixth-order model to the mechanomyogram (MMG) system of the parallel-fibered muscle, which was identified from the MMG of the pennation muscle. The median nerve was stimulated, and an MMG and torque of the abductor pollicis brevis muscle were measured. The MMGs were detected with either a capacitor microphone or an acceleration sensor. The transfer functions between stimulation and the MMG and between stimulation and torque were identified by the singular value decomposition method. The torque and the MMG, which were detected with a capacitor microphone, DMMG, were approximated with a second- and a third-order model, respectively. The natural frequency of the torque, reflecting longitudinal mechanical characteristics, did not show a significant difference from that of the DMMG. The MMG detected with an acceleration sensor was approximated with a fourth-order model. The natural frequencies of the AMMG reflecting the muscle and subcutaneous tissue in the transverse direction were obtained. Both DMMG and AMMG have to be measured to investigate the model of the MMG system for parallel-fibered muscle. The MMG system of parallel-fibered muscle was also modeled with a sixth-order model.

Mechanomyographic responses for the biceps brachii are unable to track the declines in peak torque during 25, 50, 75, and 100 fatiguing isokinetic muscle actions

This study examined the peak torque and mechanomyographic (MMG) amplitude and mean frequency (MNF) responses during fatiguing isokinetic muscle actions. On four separate occasions, twenty men (mean ± SD age = 23 ± 3 years) performed 25, 50, 75, and 100 repeated maximal concentric isokinetic muscle actions of the dominant forearm flexors. During each muscle action, the MMG signal was detected from the biceps brachii with an accelerometer. The data were examined with linear regression and one-way repeated measures analyses of variance. The results indicated that the mean percent decline in peak torque value for the 25 repetition trial (25.6%) was significantly less than that for the 50 repetition trial (45.2%). Furthermore, the mean linear slope coefficient for the peak torque versus repetition number relationship for the 50 repetition trial was significantly less than that for the 100 repetition trial. There were no mean differences among the trials for the linear slope coefficients and y-intercepts for the MMG amplitude and MNF versus repetition number relationships. When detected with an accelerometer, the linear slope coefficients and y-intercepts for the MMG amplitude and MNF versus repetition number relationships were not sensitive enough to track the decline in muscle function during fatigue.

Comparison of displacement and acceleration transducers for the characterization of mechanics of muscle and subcutaneous tissues by system identification of a mechanomyogram

The purpose of this study was to clarify the performance of transducers for the mechanical characterization of muscle and subcutaneous tissue with the aid of a system identification technique. The common peroneal nerve was stimulated, and a mechanomyogram (MMG) of the anterior tibialis muscle was detected with a laser displacement meter or an acceleration sensor. The transfer function between stimulation and the MMG was identified by the singular value decomposition method. The MMG detected with a laser displacement meter, DMMG, was approximated with a second-order model, but that detected with an acceleration sensor, AMMG, was approximated with a sixth-order model. The natural frequency of the DMMG coincided with that in the literature and was close to the lowest natural frequency of the AMMG. The highest natural frequency of the AMMG was within the range of the resonance frequencies of human soft tissue. The laser displacement meter is suitable for the precise identification of the MMG, which has a natural frequency of around 3 Hz. The acceleration transducer is suitable for the identification of the MMG with natural frequencies of tens of hertz.

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.

System identification of mechanomyograms detected with an acceleration sensor and a laser displacement meter

The purpose of this study is to investigate the transfer functions of mechanomyograms (MMGs) detected with an acceleration sensor and a laser displacement meter. The MMGs evoked by electrical stimulation to the peroneal nerve were recorded on the skin of the tibial anterior muscle. The displacement MMG (DMMG) and the acceleration MMG (AMMG) systems were identified using a singular value decomposition method. The appropriate order of the AMMG system was six and that of the DMMG system was four. The undamped natural frequencies of the systems were compared to resonance frequencies of human soft tissue. Some of the undamped natural frequencies estimated from the AMMG systems agreed with the resonance frequencies in the literature but others were lower than the resonance frequencies. The undamped natural frequencies estimated from the DMMG systems were lower than the resonance frequencies.

The effect of accelerometer location on the classification of single-site forearm mechanomyograms

Background

Recently, pattern recognition methods have been deployed in the classification of multiple activation states from mechanomyogram (MMG) signals for the purpose of controlling switching interfaces. Given the propagative properties of MMG signals, it has been suggested that MMG classification should be robust to changes in sensor placement. Nonetheless, this purported robustness remains speculative to date. This study sought to quantify the change in classification accuracy, if any, when a classifier trained with MMG signals from the muscle belly, is subsequently tested with MMG signals from a nearby location.

Methods

An arrangement of 5 accelerometers was attached to the flexor carpi radialis muscle of 12 able-bodied participants; a reference accelerometer was located over the muscle belly, two peripheral accelerometers were positioned along the muscle's transverse axis and two more were aligned to the muscle's longitudinal axis. Participants performed three classes of muscle activity: wrist flexion, wrist extension and semi-pronation. A collection of time, frequency and time-frequency features were considered and reduced by genetic feature selection. The classifier, trained using features from the reference accelerometer, was tested with signals from the longitudinally and transversally displaced accelerometers.

Results

Classification degradation due to accelerometer displacement was significant for all participants, and showed no consistent trend with the direction of displacement. Further, the displaced accelerometer signals showed task-dependent de-correlations with respect to the reference accelerometer.

Conclusions

These results indicate that MMG signal features vary with spatial location and that accelerometer displacements of only 1-2 cm cause sufficient feature drift to significantly diminish classification accuracy. This finding emphasizes the importance of consistent sensor placement between MMG classifier training and deployment for accurate control of switching interfaces.

On functional motor adaptations: from the quantification of motor strategies to the prevention of musculoskeletal disorders in the neck-shoulder region

BACKGROUND

Occupations characterized by a static low load and by repetitive actions show a high prevalence of work-related musculoskeletal disorders (WMSD) in the neck-shoulder region. Moreover, muscle fatigue and discomfort are reported to play a relevant initiating role in WMSD.

AIMS

To investigate relationships between altered sensory information, i.e. localized muscle fatigue, discomfort and pain and their associations to changes in motor control patterns.

MATERIALS & METHODS

In total 101 subjects participated. Questionnaires, subjective assessments of perceived exertion and pain intensity as well as surface electromyography (SEMG), mechanomyography (MMG), force and kinematics recordings were performed.

RESULTS

Multi-channel SEMG and MMG revealed that the degree of heterogeneity of the trapezius muscle activation increased with fatigue. Further, the spatial organization of trapezius muscle activity changed in a dynamic manner during sustained contraction with acute experimental pain. A graduation of the motor changes in relation to the pain stage (acute, subchronic and chronic) and work experience were also found. The duration of the work task was shorter in presence of acute and chronic pain. Acute pain resulted in decreased activity of the painful muscle while in subchronic and chronic pain, a more static muscle activation was found. Posture and movement changed in the presence of neck-shoulder pain. Larger and smaller sizes of arm and trunk movement variability were respectively found in acute pain and subchronic/chronic pain. The size and structure of kinematics variability decreased also in the region of discomfort. Motor variability was higher in workers with high experience. Moreover, the pattern of activation of the upper trapezius muscle changed when receiving SEMG/MMG biofeedback during computer work.

DISCUSSION

SEMG and MMG changes underlie functional mechanisms for the maintenance of force during fatiguing contraction and acute pain that may lead to the widespread pain seen in WMSD. A lack of harmonious muscle recruitment/derecruitment may play a role in pain transition. Motor behavior changed in shoulder pain conditions underlining that motor variability may play a role in the WMSD development as corroborated by the changes in kinematics variability seen with discomfort. This prognostic hypothesis was further, supported by the increased motor variability among workers with high experience.

CONCLUSION

Quantitative assessments of the functional motor adaptations can be a way to benchmark the pain status and help to indentify signs indicating WMSD development. Motor variability is an important characteristic in ergonomic situations. Future studies will investigate the potential benefit of inducing motor variability in occupational settings.

The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements

Background

Individuals with severe physical disabilities and minimal motor behaviour may be unable to use conventional mechanical switches for access. These persons may benefit from access technologies that harness the volitional activity of muscles. In this study, we describe the design and demonstrate the performance of a binary switch controlled by mechanomyogram (MMG) signals recorded from the frontalis muscle during eyebrow movements.

Methods

Muscle contractions, detected in real-time with a continuous wavelet transform algorithm, were used to control a binary switch for computer access. The automatic selection of scale-specific thresholds reduced the effect of artefact, such as eye blinks and head movement, on the performance of the switch. Switch performance was estimated by cued response-tests performed by eleven participants (one with severe physical disabilities).

Results

The average sensitivity and specificity of the switch was 99.7 ± 0.4% and 99.9 ± 0.1%, respectively. The algorithm performance was robust against typical participant movement.

Conclusions

The results suggest that the frontalis muscle is a suitable site for controlling the MMG-driven switch. The high accuracies combined with the minimal requisite effort and training show that MMG is a promising binary control signal. Further investigation of the potential benefits of MMG-control for the target population is warranted.

Recognition of forearm muscle activity by continuous classification of multi-site mechanomyogram signals

Recent studies on identifying multiple activation states from mechanomyogram (MMG) signals for the purpose of controlling switching interfaces have employed pattern recognition methods where MMG signal features from multiple muscle sites are extracted and classified. The purpose of this study is to determine if MMG signal features retain enough discriminatory information to allow reliable continuous classification, and to determine if there is a decline in classification accuracy over short time periods. MMG signals were recorded from two accelerometers attached to the flexor carpi radialis and extensor carpi radialis muscles of 12 able-bodied participants as participants performed three classes of forearm muscle activity. The data were collected over five recording sessions, with a ten-minute interval between each session. The data were spliced into 256 ms epochs, and a comprehensive set of signal features was extracted. A pattern classifier, trained with continuously acquired signal features from the first recording session, was tested with signals recorded from the other sessions. The average classification accuracy over the five sessions was 89 ± 2%. There was no obvious declining trend in classification accuracy with time. These results show that MMG signals recorded at the forearm retain enough discriminatory information to allow continuous recognition of hand motion across multiple (>90) repetitions, and the MMG-classifier does not show short-term degradation. These results indicate the potential of MMG as a multifunction control signal for muscle-machine interfaces.

Uncovering patterns of forearm muscle activity using multi-channel mechanomyography

A coordinated activation of distal forearm muscles allows the hand and fingers to be shaped during movement and grasp. However, little is known about how the muscle activation patterns are reflected in multi-channel mechanomyogram (MMG) signals. The purpose of this study is to determine if multi-site MMG signals exhibit distinctive patterns of forearm muscle activity. MMG signals were recorded from forearm muscle sites of nine able-bodied participants during hand movement. By using 14 features selected by a genetic algorithm and classified by a linear discriminant analysis classifier (LDA), we show that MMG patterns are specific and consistent enough to identify 7+/-1 hand movements with an accuracy of 90+/-4%. MMG-based movement recognition required a minimum of three recording sites. Further, by classifying five classes of contraction patterns with 98+/-3% accuracy from MMG signals recorded from the residual limb of an amputee participant, we demonstrate that MMG shows pattern-specificity even in the absence of typical musculature. Multi-site monitoring of the RMS of MMG signals is suggested as a method of estimating the relative contributions of muscles to motor tasks. The patterns in MMG facilitate our understanding of the mechanical activity of muscles during movement.

Laser-detected lateral muscle displacement is correlated with force fluctuations during voluntary contractions in humans

Fluctuations in muscle force during steady voluntary contractions result from the summation of twitch forces produced by asynchronous activation of multiple motor units. We hypothesized that oscillatory lateral muscle displacement, measured with a non-contact high-resolution laser displacement sensor, is correlated with force fluctuations during steady, voluntary contractions with a human muscle. Eight healthy young adults (20-33 yrs) performed steady isometric contractions with the first dorsal interosseus muscle. Contraction intensity ranged from 2.5% to 60% of the maximal voluntary contraction force. Oscillatory lateral displacement of the muscle surface was measured with a high-resolution laser displacement sensor (0.5 microm resolution), concurrently with abduction force of the index finger. In the time-domain analysis, there was a significant positive peak in the cross-correlation function between lateral muscle displacement and force fluctuations. In addition, the amplitude increased linearly with contraction intensity in both signals. In the frequency-domain analysis, frequency content was similar in both signals, and there was significant coherence between signals for the major frequency range of the signals (<5 Hz). In conclusion, laser-detected lateral displacement of a hand muscle is correlated with force fluctuations across a wide range of contraction intensity during steady voluntary contractions in humans.

Influence of force tremor on mechanomyographic signals recorded with an accelerometer and a condenser microphone during measurement of agonist and antagonist muscles in voluntary submaximal isometric contractions

The purpose of this study was to investigate the influence of force tremor (FT) on the mechanomyogram (MMG) recorded by a condenser microphone (MIC) and an accelerometer (ACC) for the measurement of agonist and antagonist muscles during submaximal isometric contractions. Following determination of the isometric maximum voluntary contraction (MVC), 10 male subjects were asked to perform elbow flexion and extension at 20%, 40%, 60%, and 80% MVC. Surface electromyogram (EMG) and MMG of the biceps brachii (BB) and triceps brachii (TB) were recorded simultaneously using a MIC (MMG-(MIC)) and an ACC (MMG-(ACC)). We analyzed the root mean square (RMS) for all signals and compared the sum of the power spectrum amplitude (SPA) at 3-6 Hz and 8-12 Hz between the MMG-(MIC) and the MMG-(ACC). During elbow flexion and extension, the RMS of the EMG and the MMG-(MIC) of the agonist were significantly (p<0.05) higher than those of the antagonist in each contraction level. The RMS of the MMG-(ACC) of the antagonist showed no significant (p>0.05) difference from that of the agonist, or tended to be higher than the agonist. The SPA of the MMG-(MIC) of the agonist at 3-6 Hz and 8-12 Hz tended to be higher than the antagonist in elbow flexion and extension at each contraction level. The SPA of the MMG-(ACC) of the agonist and that of the antagonist showed no significant (p>0.05) difference, or the antagonist MMG-(ACC) tended to be higher than that of the agonist. These results suggest the MMG detected by a MIC appears to be less affected by FT than is the ACC because of its inherent characteristic to reduce FT in simultaneously evaluated agonist and antagonist muscles by means of MMG during submaximal isometric contraction.

A comparison between mechanomyographic condenser microphone and accelerometer measurements during submaximal isometric, concentric and eccentric contractions

The aim of this study was to compare mechanomyogram (MMG) recorded by a condenser microphone (MIC) and an accelerometer (ACC) during submaximal isometric, concentric and eccentric contractions in 14 males. The maximal voluntary force (MVC) of the biceps brachii was measured. The subjects were asked to do short duration isometric, concentric and eccentric contraction at 10%, 30%, 50%, 70% MVC twice. For the concentric and eccentric contraction, the subject bent his arm for 3s (concentric) then held it for 3s and extended (eccentric) during 3s. The normalized root mean square (RMS) and mean power frequency (MPF) increased linearly with increased force for both transducers. There was a correlation between MIC MPF and ACC MPF at 10%, 30%, 50% MVC, and between MIC RMS and ACC RMS at 30% MVC during isometric contractions. There was significantly higher MPF for the ACC than for the MIC in concentric and eccentric modes, while the RMS did not differ among transducers in the three contraction modes. The RMS and MPF values coefficient of variations were significantly larger during anisometric contractions compared with isometric contractions and were lower for the accelerometer than for the microphone. The present results obtained during isometric, concentric and eccentric contractions of increased intensity showed that the information contained in microphone- and accelerometer-based MMG signals is different despite similar trends. It can be concluded that at low-moderate movement velocity, concentric contractions can be investigated by means of accelerometer and microphone.

Two-dimensional spatial distribution of surface mechanomyographical response to single motor unit activity

In order to better understand the mechanisms of generation of mechanomyography (MMG) signals, the two-dimensional distribution of surface MMG produced by the activity of single motor units was analyzed by a novel two-dimensional recording method. Motor unit action potentials were identified from intramuscular electromyographic (EMG) signals and used to trigger the averaging of MMG signals detected over the tibialis anterior muscle of 11 volunteers with a grid of 5x3 accelerometers (20-mm inter-accelerometer distance). The intramuscular wires were inserted between the first and second accelerometer in the middle column of the grid, proximal to the innervation zone. The subjects performed three contractions with visual feedback of the intramuscular EMG signals. In each contraction, a new motor unit was recruited at the minimum stable discharge rate (mean+/-S.D., N = 11 subjects, 7.3+/-2.3 pulse/s), resulting in torque of 2.4+/-2.8% of the maximal voluntary contraction (MVC), 4.6+/-2.7% MVC, and 6.3+/-3.1% MVC (all different, P < 0.01). For 23 out of 33 detected motor units, it was possible to extract the motor unit surface acceleration map (MUAM). A negative MUAM peak (-2.7+/-2.2 mm/s2) was detected laterally and a positive MUAM peak (4.1+/-2.4 mm/s2) medially (P < 0.001). The time-to-peak was shorter in the medial part of the muscle (2.9+/-0.4 ms) than in the other locations (3.4+/-0.5 ms, P < 0.001). The double integrated signals (muscle displacement) indicated negative deflection in the lateral part and inflation close to the tibia bone. The maps of acceleration showed spatial dependency in single motor unit MMG activities. The technique provides a new insight into motor unit contractile properties.

Spectral moments of mechanomyographic signals recorded with accelerometer and microphone during sustained fatiguing contractions

The aim of this study was to analyse the trends of the first three power spectral moments of the mechanomyogram (MMG) signal recorded by a microphone (MMG(MIC)) and an accelerometer (MMG(ACC)) during sustained contractions. MMG signals were recorded from the biceps brachii muscle in 14 healthy male subjects during a 3 min isometric elbow flexion at 30% of the maximal voluntary contraction. MMG absolute and normalised root mean square (RMS), mean power frequency (MNF), power spectral variance (Mc2), and skewness (mu3) were computed. For both MMG(MIC) and MMG(ACC), absolute and normalised RMS and Mc2 increased while MNF and mu3 decreased with contraction time (P<0.001). The rates of change of RMS over time were significantly correlated (P<0.001) for MMG(MIC) and MMG(ACC) but not correlated for spectral moments. The coefficient of variation of RMS was higher for MMG(MIC) than for MMG(ACC), while the opposite was observed for mu3 (P<0.05). It was concluded that higher order spectral moments of the MMG signal change during sustained contraction, indicating a complex modification of the shape of the power spectrum and not just scaling of the bandwidth. This is most likely due to the additional motor unit recruitment with fatigue and to the non-linear summation of motor unit contributions to the signal. Moreover, the characteristics of MMG signals recorded with microphones and accelerometers have important differences, which should be taken into account when comparing results from different studies.

Validation of an accelerometer for determination of muscle belly radial displacement

A commercial variable-capacitance micromachined accelerometer was validated for muscle belly radial displacement measurement. The displacement was calculated by the acceleration data being integrated twice and was compared with the results obtained simultaneously by an accurate mechanical displacement sensor based on an optical encoder. The aim of the investigation was to evaluate the accuracy and precision of an accelerometer for tensiomyography, which is a method for the detection of skeletal muscle contractile properties on the basis of muscle belly radial displacement. A hundred measurements at a bandwidth of 2300 Hz were performed. It was shown that the accuracy and precision in determination of the maximum displacement and the time of the maximum displacement from the calculated curve were satisfactory, in spite of the standard deviation of the twice-integrated acceleration growing approximately linearly with time. The results were accurate enough since the elapsed time from the beginning of the integration was small (less than 75 ms). The measured maximum displacement ranges were between 9.2 and 10.2 mm. The mean relative error was less than 1% (SD = 0.02mm) for the maximum displacement and about 1% (SD = 0.6 ms) for the time to maximum displacement. The accuracy of the half-relaxation time determination was more uncertain because of the relatively high relative error of -2.4% (SD = 3 ms). Results showed that a commercial micromachined accelerometer could be suitable for the measurement of muscle belly radial displacement and used for development of a future miniaturised and flexible system for the measurement of similar displacements.

Gender, muscle, and velocity comparisons of mechanomyographic and electromyographic responses during isokinetic muscle actions

The purpose of this study was to examine the responses of peak torque (PT), mean power output (MP), mechanomyographic (MMG) and electromyographic (EMG) amplitude and mean power frequency (MPF) of the vastus lateralis (VL), rectus femoris (RF), and vastus medialis (VM) in males and females during maximal, concentric isokinetic muscle actions. Subjects performed maximal leg extensions at 60 degrees s(-1), 120 degrees s(-1), 180 degrees s(-1), 240 degrees s(-1), 300 degrees s(-1), 360 degrees s(-1), 420 degrees s(-1), and 480 degrees s(-1). No gender differences were observed, but there were muscle-specific differences for the patterns of MMG MPF, EMG amplitude, and EMG MPF. The MP and MMG amplitude increased to 180-240 degrees s(-1), plateaued, and then decreased to 480 degrees s(-1). MMG MPF for the VL and VM remained unchanged to 300 degrees s(-1), but then increased to 480 degrees s(-1). The EMG amplitude for the RF and EMG MPF for the VL decreased across velocity. Overall, these findings indicated that there were muscle-specific, velocity-related differences in the associations among motor control strategies (EMG amplitude and MPF) and the mechanical aspects of isokinetic muscular activity (MMG amplitude and MPF).

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.

Reliability of the mechanomyogram detected with an accelerometer during voluntary contractions

The accelerometer is used for mechanomyogram (MMG) recordings of muscle contractions. Although the mechanical characteristics of other MMG transducers have been determined with reference to the accelerometer, mechanical aspects of the accelerometer itself, including the weight of the transducer, have not been verified. This study was designed to reinvestigate the mechanical variable of the MMG signal detected with an accelerometer, with reference to a laser distance sensor (LDS), and then to clarify the influence of the accelerometer weight on the MMG recording during muscle contractions. The study was performed during mechanical sinusoidal vibrations and during voluntary contractions of the quadriceps muscle. Maximum differences in the amplitude spectral density functions between the LDS signal and the double integral of the accelerometer signal were approximately 4 microm. The results verified that the MMG signal from the accelerometer accurately reflected the acceleration of body surface vibration. However, the MMG signal was gradually distorted when weight was added to the accelerometer: the addition of 4.0 g (total 6.0 g, including 2.0 g of accelerometer) substantially attenuated the MMG signal. The results suggest that the appropriate weight for the accelerometer should be less than 5.0 g for measurements of the quadriceps muscle and indicate that the transducer weight must be taken into account for accurate measurement of muscles of different sizes.

Mechanomyography versus electromyography, in monitoring the muscular fatigue

BACKGROUND

The use of the mechanomyogram (MMG) which detects muscular vibrations generated by fused individual fiber twitches has been refined. The study addresses a comparison of the MMG and surface electromyogram (SEMG) in monitoring muscle fatigue.

METHODS

The SEMG and MMG were recorded simultaneously from the same territory of motor units in two muscles (Biceps, Brachioradialis) of the human (n = 18), during sustained contraction at 25 % MVC (maximal voluntary contraction).

RESULTS

The RMS (root mean square) of the SEMG and MMG increased with advancing fatigue; MF (median frequency) of the PSD (power density spectra) progressively decreased from the onset of the contraction. These findings (both muscles, all subjects), demonstrate both through the SEMG and MMG a central component of the fatigue. The MF regression slopes of MMG were closer to each other between men and women (Biceps 1.55%; Brachialis 13.2%) than were the SEMG MF slopes (Biceps 25.32%; Brachialis 17.72%), which shows a smaller inter-sex variability for the MMG vs. SEMG.

CONCLUSION

The study presents another quantitative comparison (MF, RMS) of MMG and SEMG, showing that MMG signal can be used for indication of the degree of muscle activation and for monitoring the muscle fatigue when the application of SEMG is not feasible (chronical implants, adverse environments contaminated by electrical noise).