Ultra-High-Frame-Rate Ultrasound Monitoring of Muscle Contractility Changes Due to Neuromuscular Electrical Stimulation

Wearable super-resolution muscle-machine interfacing

Muscles are the actuators of all human actions, from daily work and life to communication and expression of emotions. Myography records the signals from muscle activities as an interface between machine hardware and human wetware, granting direct and natural control of our electronic peripherals. Regardless of the significant progression as of late, the conventional myographic sensors are still incapable of achieving the desired high-resolution and non-invasive recording. This paper presents a critical review of state-of-the-art wearable sensing technologies that measure deeper muscle activity with high spatial resolution, so-called super-resolution. This paper classifies these myographic sensors according to the different signal types (i.e., biomechanical, biochemical, and bioelectrical) they record during measuring muscle activity. By describing the characteristics and current developments with advantages and limitations of each myographic sensor, their capabilities are investigated as a super-resolution myography technique, including: (i) non-invasive and high-density designs of the sensing units and their vulnerability to interferences, (ii) limit-of-detection to register the activity of deep muscles. Finally, this paper concludes with new opportunities in this fast-growing super-resolution myography field and proposes promising future research directions. These advances will enable next-generation muscle-machine interfaces to meet the practical design needs in real-life for healthcare technologies, assistive/rehabilitation robotics, and human augmentation with extended reality.

Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation

Functional electrical stimulation (FES) is a potential neurorehabilitative intervention to enable functional movements in persons with neurological conditions that cause mobility impairments. However, the quick onset of muscle fatigue during FES is a significant challenge for sustaining the desired functional movements for more extended periods. Therefore, a considerable interest still exists in the development of sensing techniques that reliably measure FES-induced muscle fatigue. This study proposes to use ultrasound (US) imaging-derived echogenicity signal as an indicator of FES-induced muscle fatigue. We hypothesized that the US-derived echogenicity signal is sensitive to FES-induced muscle fatigue under isometric and dynamic muscle contraction conditions. Eight non-disabled participants participated in the experiments, where FES electrodes were applied on their tibialis anterior (TA) muscles. During a fatigue protocol under either isometric and dynamic ankle dorsiflexion conditions, we synchronously collected the isometric dorsiflexion torque or dynamic dorsiflexion angle on the ankle joint, US echogenicity signals from TA muscle, and the applied stimulation intensity. The experimental results showed an exponential reduction in the US echogenicity relative change (ERC) as the fatigue progressed under the isometric (R2=0.891±0.081) and dynamic (R2=0.858±0.065) conditions. The experimental results also implied a strong linear relationship between US ERC and TA muscle fatigue benchmark (dorsiflexion torque or angle amplitude), with R2 values of 0.840±0.054 and 0.794±0.065 under isometric and dynamic conditions, respectively. The findings in this study indicate that the US echogenicity signal is a computationally efficient signal that strongly represents FES-induced muscle fatigue. Its potential real-time implementation to detect fatigue can facilitate an FES closed-loop controller design that considers the FES-induced muscle fatigue.

Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation

The rapid onset of muscle fatigue during functional electrical stimulation (FES) is a major challenge when attempting to perform long-term periodic tasks such as walking. Surface electromyography (sEMG) is frequently used to detect muscle fatigue for both volitional and FES-evoked muscle contraction. However, sEMG contamination from both FES stimulation artifacts and residual M-wave signals requires sophisticated processing to get clean signals and evaluate the muscle fatigue level. The objective of this paper is to investigate the feasibility of computationally efficient ultrasound (US) echogenicity as a candidate indicator of FES-induced muscle fatigue. We conducted isometric and dynamic ankle dorsiflexion experiments with electrically stimulated tibialis anterior (TA) muscle on three human participants. During a fatigue protocol, we synchronously recorded isometric dorsiflexion force, dynamic dorsiflexion angle, US images, and stimulation intensity. The temporal US echogenicity from US images was calculated based on a gray-scaled analysis to assess the decrease in dorsiflexion force or motion range due to FES-induced TA muscle fatigue. The results showed a monotonic reduction in US echogenicity change along with the fatigue progression for both isometric (R² =0.870±0.026) and dynamic (R² =0.803±0.048) ankle dorsiflexion. These results implied a strong linear relationship between US echogenicity and TA muscle fatigue level. The findings indicate that US echogenicity may be a promising computationally efficient indicator for assessing FES-induced muscle fatigue and may aid in the design of muscle-in-the-loop FES controllers that consider the onset of muscle fatigue.

A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study

For decades, surface electromyography (sEMG) has been a popular non-invasive bio-sensing technology for predicting human joint motion. However, cross-talk, interference from adjacent muscles, and its inability to measure deeply located muscles limit its performance in predicting joint motion. Recently, ultrasound (US) imaging has been proposed as an alternative non-invasive technology to predict joint movement due to its high signal-to-noise ratio, direct visualization of targeted tissue, and ability to access deep-seated muscles. This paper proposes a dual-modal approach that combines US imaging and sEMG for predicting volitional dynamic ankle dorsiflexion movement. Three feature sets: 1) a uni-modal set with four sEMG features, 2) a uni-modal set with four US imaging features, and 3) a dual-modal set with four dominant sEMG and US imaging features, together with measured ankle dorsiflexion angles, were used to train multiple machine learning regression models. The experimental results from a seated posture and five walking trials at different speeds, ranging from 0.50 m/s to 1.50 m/s, showed that the dual-modal set significantly reduced the prediction root mean square errors (RMSEs). Compared to the uni-modal sEMG feature set, the dual-modal set reduced RMSEs by up to 47.84% for the seated posture and up to 77.72% for the walking trials. Similarly, when compared to the US imaging feature set, the dual-modal set reduced RMSEs by up to 53.95% for the seated posture and up to 58.39% for the walking trials. The findings show that potentially the dual-modal sensing approach can be used as a superior sensing modality to predict human intent of a continuous motion and implemented for volitional control of clinical rehabilitative and assistive devices.

Sound production in piranhas is associated with modifications of the spinal locomotor pattern

In piranhas, sounds are produced through the vibration of the swim bladder wall caused by the contraction of bilateral sonic muscles. Because they are solely innervated by spinal nerves, these muscles likely evolved from the locomotor hypaxial musculature. The transition from a neuromuscular system initially shaped for slow movements (locomotion) to a system that requires a high contraction rate (sound production) was accompanied with major peripheral structural modifications, yet the associated neural adjustments remain to this date unclear. To close this gap, we investigated the activity of both the locomotor and the sonic musculature using electromyography. The comparison between the activation patterns of both systems highlighted modifications of the neural motor pathway: (1) a transition from a bilateral alternating pattern to a synchronous activation pattern, (2) a switch from a slow- to a high-frequency regime, and (3) an increase in the synchrony of motor neuron activation. Furthermore, our results demonstrate that sound features correspond to the activity of the sonic muscles, as both the variation patterns of periods and amplitudes of sounds highly correspond to those seen in the sonic muscle electromyograms (EMGsonic). Assuming that the premotor network for sound production in piranhas is of spinal origin, our results show that the neural circuit associated with spinal motor neurons transitioned from the slow alternating pattern originally used for locomotion to a much faster simultaneous activation pattern to generate vocal signals.