Objective: The study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, i.e., the muscle deformation, has not been widely studied. To address this gap, we analysed the kinematics of muscle units in natural contractions. Approach: We combined high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) recordings, at 1000 frames per second, from the tibialis anterior muscle to measure the motion of the muscular tissue caused by individual MU contractions. The MU discharge times were identified online by decomposition of the HDsEMG and provided as biofeedback to 12 subjects who were instructed to keep the MU active at the minimum discharge rate (9.8 ± 4.7 pulses per second; force less than 10% of the maximum). The series of discharge times were used to identify the velocity maps associated with 51 single muscle unit movements with high spatio-temporal precision, by a novel processing method on the concurrently recorded US images. From the individual MU velocity maps, we estimated the region of movement, the duration of the motion, the contraction time, and the excitation-contraction (E-C) coupling delay. Main results: Individual muscle unit motions could be reliably identified from the velocity maps in 10 out of 12 subjects. The duration of the motion, total contraction time, and E-C coupling were 17.9 ± 5.3 ms, 56.6 ± 8.4 ms, and 3.8 ± 3.0 ms (n = 390 across 10 participants). The experimental measures also provided the first evidence of muscle unit twisting during voluntary contractions and MU territories with distinct split regions. Significance: The proposed method allows for the study of kinematics of individual MU twitches during natural contractions. The described measurements and characterisations open new avenues for the study of neuromechanics in healthy and pathological conditions.

The study of human neuromechanical control at the motor unit (MU) level has predominantly focussed on electrical activity and force generation, whilst the link between these, the muscle deformation, has not been widely studied. Here, we describe a methodology utilising ultrafast ultrasound (US), allowing imaging of up to tens of thousands of frames per second, to measure the deformation of the muscular tissue due to individual MU twitches for a population of active MUs during voluntary contractions. We used the spiking activity of MUs decomposed from high-density surface electromyography recordings of the tibialis anterior muscle to guide the analysis of simultaneously recorded ultrafast US. With a novel analysis on the US images we identified, with high spatio-temporal precision, the velocity maps associated with single MU movements. From the individual MU profiles obtained from the velocity maps, the region of movement, the duration of the mechanical twitch, the total and active contraction times, and the activation time were computed. The latter features, the temporal features, showed high repeatability across different force levels. The former feature, the spatial feature, showed high consistency across force levels, however the complicated dynamics of the muscle motion resulted in morphing and translation of these regions. Furthermore, the experimental measures provided the first evidence of muscle unit twisting during voluntary contractions. The proposed approach allows, for the first time, non-invasive recordings of muscle deformation due to individual MU activations during voluntary contractions.Key pointsWe identified the activity of single motor units (MUs) from high-density surface electromyography (HDsEMG) and used this information in combination with ultrafast ultrasound to extract local muscle motion due to the contraction of individual muscle unitsMultiple MUs, including those with fibres overlapping in space, can be simultaneously and individually detected using this techniqueThe proposed method allows us to measure both the spiking activity of motor units and their movement within the muscles concomitantlyThe technique allows for populations of MUs to be tracked and monitored in the electrical and mechanical domains simultaneously and non-invasively during natural contractions, thus achieving a high spatio-temporal resolution in the characterization of MU behaviour

<p><em>Objective:</em> Non-invasive human machine interfaces (HMIs) have high potential in medical, entertainment, and industrial applications. Traditionally, surface electromyography (sEMG) has been used to track muscular activity and infer motor intention. As an alternative HMI approach, ultrasound (US) has been receiving increasing attention as an alternative to EMG. Here, we developed a novel portable US armband system with 24 channels and a multiple receiver approach and compared it with existing sEMG- and US-based HMIs on movement intention decoding. </p> <p><em>Methods:</em> US and motion capture data was recorded while participants performed wrist and hand movements in up to four degrees of freedom (DoFs). A linear regression model was then used to predict the hand kinematics from the US (or sEMG, for comparison) features. The model was also implemented in real-time for a 3-DoF pointer control with minimal training data. </p> <p><em>Results:</em> In offline analysis, the wearable US system achieved an average of 0.94 in the prediction of four DoFs of the wrist and hand. sEMG reached a performance of   = 0.60. </p> <p><em>Conclusion:</em> The newly proposed A-mode US bracelet setup and processing pipeline can regress hand kinematics for up to four DoFs with higher accuracies than other previously published interfaces based on sEMG or US data. The system allowed real-time control with minimal training data </p> <p><em>Significance:</em> Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The proposed US system here provided robust proportional and simultaneous control over multiple DoFs. </p>

<p><em>Objective:</em> Non-invasive human machine interfaces (HMIs) have high potential in medical, entertainment, and industrial applications. Traditionally, surface electromyography (sEMG) has been used to track muscular activity and infer motor intention. Ultrasound (US) has received increasing attention as an alternative to sEMG-based HMIs. Here, we developed a portable US armband system with 24 channels and a multiple receiver approach, and compared it with existing sEMG- and US-based HMIs on movement intention decoding. </p> <p><br></p> <p><em>Methods:</em> US and motion capture data was recorded while participants performed wrist and hand movements of four degrees of freedom (DoFs) and their combinations. A linear regression model was used to offline predict hand kinematics from the US (or sEMG, for comparison) features. The method was further validated in real-time for a 3-DoF target reaching task. </p> <p><br></p> <p><em>Results:</em> In the offline analysis, the wearable US system achieved an average  R²of 0.94 in the prediction of four DoFs of the wrist and hand while sEMG reached a performance of R²  = 0.60. In online control, the participants achieved an average 93% completion rate of the targets. </p> <p><br></p> <p><em>Conclusion: </em>When tailored for HMIs, the proposed US A-mode system and processing pipeline can successfully regress hand kinematics both in offline and online settings with performances comparable or superior to previously published interfaces<em>.</em> </p> <p><br></p> <p><em>Significance:</em> Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The wearable US system  allowed for robust proportional and simultaneous control over multiple DoFs in both offline and online settings. </p>

Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.