A computational framework for post-TAVR cardiac conduction abnormality (CCA) risk assessment in patient-specific anatomy.

BACKGROUND: Cardiac conduction abnormality (CCA)- one of the major persistent complications associated with transcatheter aortic valve replacement (TAVR) may lead to permanent pacemaker implantation. Localized stresses exerted by the device frame on the membranous septum (MS) which lies between the aortic annulus and the bundle of His, may disturb the cardiac conduction and cause the resultant CCA. We hypothesize that the area-weighted average maximum principal logarithmic strain (AMPLS) in the MS region can predict the risk of CCA following TAVR. METHODS: Rigorous finite element-based analysis was conducted in two patients (Balloon expandable TAVR recipients) to assess post-TAVR CCA risk. Following the procedure one of the patients required permanent pacemaker (PPM) implantation while the other did not (control case). Patient-specific aortic root was modeled, MS was identified from the CT image, and the TAVR deployment was simulated. Mechanical factors in the MS region such as logarithmic strain, contact force, contact pressure, contact pressure index (CPI) and their time history during the TAVR deployment; and anatomical factors such as MS length, implantation depth, were analyzed. RESULTS: Maximum AMPLS (0.47 and 0.37, respectively), contact force (0.92 N and 0.72 N, respectively), and CPI (3.99 and 2.86, respectively) in the MS region were significantly elevated in the PPM patient as compared to control patient. CONCLUSION: Elevated stresses generated by TAVR devices during deployment appear to correlate with CCA risk, with AMPLS in the MS region emerging as a strong predictor that could be used for preprocedural planning in order to minimize CCA risk.

Assessment of Paravalvular Leak Severity and Thrombogenic Potential in Transcatheter Bicuspid Aortic Valve Replacements Using Patient-Specific Computational Modeling.

Bicuspid aortic valve (BAV), the most common congenital valvular abnormality, generates asymmetric flow patterns and increased stresses on the leaflets that expedite valvular calcification and structural degeneration. Recently adapted for use in BAV patients, TAVR demonstrates promising performance, but post-TAVR complications tend to get exacerbated due to BAV anatomical complexities. Utilizing patient-specific computational modeling, we address some of these complications. The degree and location of post-TAVR PVL was assessed, and the risk of flow-induced thrombogenicity was analyzed in 3 BAV patients - using older generation TAVR devices that were implanted in these patients, and compared them to the performance of the newest generation TAVR devices using in silico patient models. Significant decrease in PVL and thrombogenic potential was observed after implantation of the newest generation device. The current work demonstrates the potential of using simulations in pre-procedural planning to assess post-TAVR complications, and compare the performance of different devices to achieve better clinical outcomes. Patient-specific computational framework to assess post-transcatheter bicuspid aortic valve replacement paravalvular leakage and flow-induced thrombogenic complications and compare device performances.

The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics.

Due to the epochal changes introduced by "Industry 4.0", it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human-robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative "direct instrumental evaluations" and "rating of standard methods", allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers' awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities.

Muscle fatigue assessment through electrodermal activity analysis during isometric contraction.

We studied the effects of muscle fatigue on the Autonomic Nervous System (ANS) dynamics. Specifically, we monitored the electrodermal activity (EDA) on 32 healthy subjects performing isometric biceps contraction. As assessed by means of an electromyography (EMG) analysis, 15 subjects showed muscle fatigue and 17 did not. EDA signals were analyzed using the recently proposed cvxEDA model in order to decompose them into their phasic and tonic components and extract effective features to study ANS dynamics. A statistical comparison between the two groups of subjects was performed. Results revealed that relevant phasic EDA features significantly increased in the fatigued group. Moreover, a pattern recognition system was applied to the EDA dataset in order to automatically discriminate between fatigued and non-fatigued subjects. The proposed leave-one-subject-out KNN classifier showed an accuracy of 75.69%. These results suggest the use of EDA as correlate of muscle fatigue, providing integrative information to the standard indices extracted from the EMG signals.

Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-Inspired Robotic Hand for Prosthetic Applications.

Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.