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Abstract Maneuverable microswimmers/microdrones that navigate in hard-to-reach spaces inside human bodies hold a great potential for various biomedical applications. Acoustically actuated microswimmers have already demonstrated feasibility. However, for eventual translation of this technology, a robust 3-D tracking strategy for the microswimmer is particularly required. This paper presents our lab-designed 3-D ultrasound tracking system for real-time tracking of an acoustically actuated 3-D swimming microdrone. The ultrasound tracking system utilizing two ultrasound probes, a step motor and a host controller, was built to track the 3-D arbitrary motion of the microdrone in real-time. The performance of tracking was evaluated in the benchtop experiments by comparing the reconstructed trajectories with synchronized camera recordings. The ultrasound tracking system showed high reliability, with an average error of less than 0.3 mm across six different trials when compared to camera tracking. The results demonstrated the capability of our lab-designed 3-D ultrasound tracking system in accurately tracking the undetermined motion of the acoustic actuated 3-D swimming microdrone in real-time. The developed tracking system holds promise as a potential approach for biomedical applications and could pave the way for future clinical translation of the microswimmer technology. 

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. 

This paper investigates an ultrasound (US) imaging-based methodology to assess the contraction levels of plantar flexors quantitatively. Echogenicity derived from US imaging at different anatomical depths, including both lateral gastrocnemius (LGS) and soleus (SOL) muscles, is used for the prediction of the volitional isometric plantar flexion moment. Synchronous measurements, including a plantar flexion torque signal, a surface electromyography (sEMG) signal, and US imaging of both LGS and SOL muscles, are collected. Four feature sets, including sole sEMG, sole LGS echogenicity, sole SOL echogenicity, and their fusion, are used to train a Gaussian process regression (GPR) model and predict plantar flexion torque. The experimental results on four non-disabled participants show that the torque prediction accuracy is improved significantly by using the LGS or SOL echogenicity signal than using the sEMG signal. However, there is no significant improvement by using the fused feature compared to sole LGS or SOL echogenicity. The findings imply that using US imaging-derived signals improves the accuracy of predicting volitional effort on human plantar flexors. Potentially, US imaging can be used as a new sensing modality to measure or predict human lower limb motion intent in clinical rehabilitation devices. 

Abstract Neuroprosthetic devices that use transcutaneous neuromuscular electrical stimulation (NMES) are potential interventions to restore skeletal muscle function in people with neurological disorders. As commonly noted, how to assess the NMES-induced muscle fatigue is a critical problem. This is because the capability of fatigue assessment is a necessary precursor for optimally modulating the NMES dosage to improve the control performance of a neuroprosthesis and ensure user’s safety. To effectively estimate the NMES-induced muscle fatigue, this paper proposes a novel state observer that combines a mathematical predictive fatigue model and intermittent feedback from ultrasound-derived strain images. The strain images quantify muscle contractility during NMES. Principal component regression (PCR) is used to derive a relationship between the strain images and instantaneous muscle force production. Lyapunov stability analysis was performed to obtain the convergence property of the designed observer. A globally uniformly ultimately bounded (GUUB) result was obtained. Simulations based on pre-recorded data from a human experiment were also conducted to demonstrate the performance of the designed observer.