IEEE Transactions on Biomedical Engineering最新文献

Objective: Powered ankle exoskeletons with biofeedback systems have proven effective at improving ankle plantar flexor muscle recruitment and push-off power in individuals with cerebral palsy (CP). However, their clinical translation and feasibility for at-home training remain limited. This study sought to design an unpowered wearable ankle device with spring resistance combined with a gamified ankle power biofeedback system. Our primary goal was to validate the device's ability to increase plantar flexor muscle recruitment and push-off power relative to baseline, and ensure that these improvements were comparable to those achieved with motorized resistance.

Methods: Seven ambulatory individuals with CP completed walking sessions with (1) a powered ankle exoskeleton with motorized resistance, (2) our novel ankle device with spring resistance, and (3) shoes only (baseline); Both devices utilized the same biofeedback system.

Results: Relative to baseline, both the motorized and spring resistance increased peak (48%, p<0.05) and mean (43-45%, p<0.05) soleus activation and mean (37-39%, p<0.05) medial gastrocnemius activation. No differences in muscle recruitment between spring and motorized devices were observed. Walking with spring resistance increased average ankle push-off positive power by 22% (p = 0.003) compared to motorized resistance and by 23% (p = 0.013) compared to baseline.

Conclusion: An ankle device providing targeted spring resistance with ankle power biofeedback can effectively improve push-off muscle recruitment and power in individuals with CP.

Significance: This supports future research studying outcomes following training with spring-based ankle resistance devices that lower barriers for clinical translation.

Objective: Cortico-muscular communication patterns are instrumental in understanding movement control. Estimating significant causal relationships between motor cortex electroencephalogram (EEG) and surface electromyogram (sEMG) from concurrently active muscles presents a formidable challenge since the relevant processes underlying muscle control are typically weak in comparison to measurement noise and background activities.

Methodology: In this paper, a novel framework is proposed to simultaneously estimate the order of the autoregressive model of cortico-muscular interactions along with the parameters while enforcing stationarity condition in a convex program to ensure global optimality. The proposed method is further extended to a non-convex program to account for the presence of measurement noise in the recorded signals by introducing a wavelet sparsity assumption on the excitation noise in the model.

Results: The proposed methodology is validated using both simulated data and neurophysiological signals. In case of simulated data, the performance of the proposed methods has been compared with the benchmark approaches in terms of order identification, computational efficiency, and goodness of fit in relation to various noise levels. In case of physiological signals our proposed methods are compared against the state-of-the-art approaches in terms of the ability to detect Granger causality.

Significance: The proposed methods are shown to be effective in handling stationarity and measurement noise assumptions, revealing significant causal interactions from brain to muscles and vice versa.

Intra-cardiac echocardiography (ICE) is a crucial imaging modality used in electrophysiology (EP) and structural heart disease (SHD) interventions, providing real-time, high-resolution views from within the heart. Despite its advantages, effective manipulation of the ICE catheter requires significant expertise, which can lead to inconsistent outcomes, especially among less experienced operators. To address this challenge, we propose an AI-driven view guidance system that operates in a continuous closed-loop with human-in-the-loop feedback, designed to assist users in navigating ICE imaging without requiring specialized knowledge. Specifically, our method models the relative position and orientation vectors between arbitrary views and clinically defined ICE views in a spatial coordinate system. It guides users on how to manipulate the ICE catheter to transition from the current view to the desired view over time. By operating in a closed-loop configuration, the system continuously predicts and updates the necessary catheter manipulations, ensuring seamless integration into existing clinical workflows. The effectiveness of the proposed system is demonstrated through a simulation-based performance evaluation using real clinical data, achieving an 89% success rate with 6,532 test cases. Additionally, a semi-simulation experiment with human-in-the-loop testing validated the feasibility of continuous yet discrete guidance. These results underscore the potential of the proposed method to enhance the accuracy and efficiency of ICE imaging procedures.

Objective: We present a 500 MHz inductive birdcage RF resonator for imaging the human brain in an 11.7 T MRI scanner. A homogenous circularly polarized transmit field (B1 +) was generated by transmitting power to the resonator through four couplers driven in differential mode and with an incremental 90-degree phase delay. A detailed mechanical and electrical model of the hardware, loaded with different phantoms, was generated and its performance simulated using a finite-difference timedomain method. The model was validated through bench and MRI measurements. This validation is important for future analysis of radiofrequency safety and performance through the prediction of SAR and B1 + profiles across different human brain models at various positions inside the coil.

Objective: Fatigue-resistant and graded muscle forces can be evoked through asynchronous intrafascicular multi-electrode stimulation (aIFMS). Prior studies on controlled force generation using aIFMS employed either a feedback controller featuring a multiple-input single-output delayed-integral (MISO-I) control law, or a feedforward controller with a non-predictive model-based policy. However, these controllers resulted in lagged responses as stimulation was coordinated via intentional time delays and lacked immediate control corrections. To address these limitations, this paper presents an adaptive feedforward model predictive controller (aF-MPC) for isometric torque control.

Methods: The aF-MPC was evaluated in experiments in anesthetized felines implanted with Utah Slanted Electrode Arrays in their sciatic nerves. This controller redesigned the existing aIFMS feedforward controller by enhancing it with a predictive policy and an online model learning algorithm to compensate for unaccounted aIFMS effects. Statistical comparisons of the aF-MPC and the (non-adaptive) F-MPC trials and observational comparisons of the aF-MPC and the MISO-I controller were performed for different desired trajectories.

Results: The aF-MPC exhibited significant performance improvements over the F-MPC across multiple metrics. Observationally, the aF-MPC showed improvements in all performance metrics over the MISO-I controller.

Conclusion: Despite unknown dynamics in the aIFMS system, this paper's aF-MPC outperformed alternate approaches as it accurately tracked desired torque profiles even under high-frequency commands.

Significance: The application of the aF-MPC in conjunction with aIFMS could provide a better avenue for developing naturalistic motor neuroprosthesis than F-MPCs or MISO-I controllers.