The Simultaneous Model-Based Estimation of Joint, Muscle, and Tendon Stiffness is Highly Sensitive to the Tendon Force-Strain Relationship

OBJECTIVE

Accurate estimation of stiffness across anatomical levels (i.e., joint, muscle, and tendon) in vivo has long been a challenge in biomechanics. Recent advances in electromyography (EMG)-driven musculoskeletal modeling have allowed the non-invasive estimation of stiffness during dynamic joint rotations. Nevertheless, validation has been limited to the joint level due to a lack of simultaneous in vivo experimental measurements of muscle and tendon stiffness.

METHODS

With a focus on the triceps surae, we employed a novel perturbation-based experimental technique informed by dynamometry and ultrasonography to derive reference stiffness at the joint, muscle, and tendon levels simultaneously. Here, we propose a new EMG-driven model-based approach that does not require external joint perturbation, nor ultrasonography, to estimate multi-level stiffness. We present a novel set of closed-form equations that enables the person-specific tuning of musculoskeletal parameters dictating biological stiffness, including passive force-length relationships in modeled muscles and tendons.

RESULTS

Calibrated EMG-driven musculoskeletal models estimated the reference data with average normalized root-mean-square error ≈ 20%. Moreover, only when calibrated tendons were approximately four times more compliant than typically modeled, our approach could estimate multi-level reference stiffness.

CONCLUSION

EMG-driven musculoskeletal models can be calibrated on a larger set of reference data to provide more realistic values for the biomechanical variables across multiple anatomical levels. Moreover, the tendon models that are typically used in musculoskeletal modeling are too stiff.

SIGNIFICANCE

Calibrated musculoskeletal models informed by experimental measurements give access to an augmented range of biomechanical variables that might not be easily measured with sensors alone.

Benchmarking commercially available soft and rigid passive back exoskeletons for an industrial workplace

Low-back pain is a common occupational hazard for industrial workers. Several studies show the advantages of using rigid and soft back-support passive exoskeletons and exosuits (exos) to reduce the low-back loading and risk of injury. However, benefits of using these exos have been shown to be task-specific. Therefore, in this study, we developed a benchmarking approach to assess exos for an industrial workplace at Hankamp Gears B.V. We assessed two rigid (Laevo Flex, Paexo back) and two soft (Auxivo Liftsuit 1.0, and Darwing Hakobelude) exos for tasks resembling the workplace. We measured the assistive moment provided by each exo and their respective influence on muscle activity as well as the user's perception of comfort and exertion. Ten participants performed four lifting tasks (Static hold, Asymmetric, Squat, and Stoop), while their electromyography and subjective measures were collected. The two rigid exos provided the largest assistance during the Dynamic tasks. Reductions in erector spinae activity were seen to be task-specific, with larger reductions for the two rigid exos. Overall, Laevo Flex offered a good balance between assistive moments, reductions in muscle activity, as well as user comfort and reductions in perceived exertion. Thus, we recommend benchmarking exos for intended use in the industrial workplace. This will hopefully result in a better adoption of the back-support exoskeletons in the workplace and help reduce low-back pain.

Electromyography-driven musculoskeletal models with time-varying fatigue dynamics improve lumbosacral joint moments during lifting

Muscle fatigue is prevalent across different aspects of daily life. Tracking muscle fatigue is useful to understand muscle overuse and possible risk of injury leading to musculoskeletal disorders. Current fatigue models are not suitable for real-world settings as they are either validated using simulations or non-functional tasks. Moreover, models that capture the changes to muscle activity due to fatigue either assume a linear relationship between muscle activity and muscle force or utilize a simple muscle model. Personalised electromygraphy (EMG)-driven musculoskeletal models (pEMS) offer person-specific approaches to model muscle and joint kinetics during a wide repertoire of daily life tasks. These models utilize EMG, thus capturing central fatigue-dependent changes in multi-muscle bio-electrical activity. However, the peripheral muscle force decay is missing in these models. Thus, we studied the influence of fatigue on a large scale pEMS of the trunk. Eleven healthy participants performed functional asymmetric lifting task. Average peak body-weight normalized lumbosacral moments (BW-LM) were estimated to be 2.55 ± 0.26 Nm/kg by reference inverse dynamics. After complete exhaustion of the lower back, the pEMS overestimated the peak BW-LM by 0.64 ± 0.37 Nm/kg. Then, we developed a time-varying muscle force decay model resulting in a time-varying pEMS (t-pEMS). This reduced the difference between BW-LM estimated by the t-pEMS and reference to 0.49 ± 0.14 Nm/kg. We also showed that five fatiguing contractions are sufficient to calibrate the t-pEMS. Thus, this study presents a person and muscle specific model to track fatigue during functional tasks.

Trunk extensor muscle endurance and its relationship to action potential conduction velocity and spectral parameters estimated using high-density electromyography

Trunk extensor muscle fatigue typically manifests as a decline in spectral content of surface electromyography. However, previous research on the relationship of this decline with trunk extensor muscle endurance have shown inconsistent results. The decline of spectral content mainly reflects the decrease in average motor unit action potential conduction velocity (CV). We evaluated whether the rate of change in CV, as well as two approaches employing the change in spectral content, are related to trunk extensor muscle endurance. Fourteen healthy male participants without a low-back pain history performed a non-strictly controlled static forward trunk bending trial until exhaustion while standing. For 13 participants, physiologically plausible CV estimates were obtained from high-density surface electromyography bilaterally from T6 to L5. Laterally between L1 and L2, the linear rate of CV change was strongly correlated to endurance time (R2 = 0.79), whereas analyses involving the linear rate of change in spectral measures showed a lower (R2 = 0.38) or no correlation. For medial electrode locations, estimating CV and its relationship with endurance time was less successful, while the linear rate of change in spectral measures correlated moderately to endurance time (R2 = 0.44; R2 = 0.56). This study provides guidance on monitoring trunk extensor muscle fatigue development using electromyography.

A Neuromechanical Model-Based Strategy to Estimate the Operator's Payload in Industrial Lifting Tasks

One of the main technological barriers hindering the development of active industrial exoskeleton is today represented by the lack of suitable payload estimation algorithms characterized by high accuracy and low calibration time. The knowledge of the payload enables exoskeletons to dynamically provide the required assistance to the user. This work proposes a payload estimation methodology based on personalized Electromyography-driven musculoskeletal models (pEMS) combined with a payload estimation method we called "delta torque" that allows the decoupling of payload dynamical properties from human dynamical properties. The contribution of this work lies in the conceptualization of such methodology and its validation considering human operators during industrial lifting tasks. With respect to existing solutions often based on machine learning, our methodology requires smaller training datasets and can better generalize across different payloads and tasks. The proposed payload estimation methodology has been validated on lifting tasks with 0kg, 5kg, 10kg and 15kg, resulting in an average MAE of about 1.4 Kg. Even if 5kg and 10Kg lifting tasks were out of the training set, the MAE related to these tasks are 1.6 kg and 1.1 kg, respectively, demonstrating the generalizing property of the proposed methodology. To the best of the authors' knowledge, this is the first time that an EMG-driven model-based approach is proposed for human payload estimation.

Characterization of Motor Unit Firing and Twitch Properties for Decoding Musculoskeletal Force in the Human Ankle Joint In Vivo

Understanding how motor units (MUs) contribute to skeletal mechanical force is crucial for unraveling the underlying mechanism of human movement. Alterations in MU firing, contractile and force-generating properties emerge in response to physical training, aging or injury. However, how changes in MU firing and twitch properties dictate skeletal muscle force generation in healthy and impaired individuals remains an open question. In this work, we present a MU-specific approach to identify firing and twitch properties of MU samples and employ them to decode musculoskeletal function in vivo. First, MU firing events were decomposed offline from high-density electromyography (HD-EMG) of six lower leg muscles involved in ankle plantar-dorsi flexion. We characterized their twitch responses based on the statistical distributions of their firing properties and employed them to compute MU-specific activation dynamics. Subsequently, we decoded ankle joint moments by linking our framework to a subject-specific musculoskeletal model. We validated our approach at different ankle positions and levels of activation and compared it with traditional EMG-driven models. Our proposed MU-specific formulation achieves higher generalization across conditions than the EMG-driven models, with significantly lower coefficients of variation in torque predictions. Furthermore, our approach shows distinct neural strategies across a large repertoire of contractile conditions in different muscles. Our proposed approach may open new avenues for characterizing the relationship between MU firing and twitch properties and their influence on force capacity. This can facilitate the development of targeted rehabilitation strategies tailored to individuals with specific neuromuscular conditions.

Automated spatial localization of ankle muscle sites and model-based estimation of joint torque post-stroke via a wearable sensorised leg garment

Assessing a patient's musculoskeletal function during over-ground walking is a primary objective in post-stroke rehabilitation, due to the importance of walking recovery for everyday life. However, the quantitative assessment of musculoskeletal function currently requires lab-constrained equipment, and labor-intensive analyses, which hampers assessment in standard clinical settings. The development of fully wearable systems for the online estimation of muscle-tendon forces and resulting joint torque would aid clinical assessment of motor recovery, it would enhance the detection of neuro-muscular anomalies and it would consequently enable highly personalized treatments. Here, we present a wearable technology that combines (1) a soft garment for the human leg sensorized with 64 flexible and dry electromyography (EMG) electrodes, (2) a generalized and automated algorithm for the localization of leg muscle sites, and (3) an EMG-driven musculoskeletal modeling framework for the estimation of ankle dorsi-plantar flexion torques. Our results showed that the automated clustering algorithm could detect muscle locations in both healthy and post-stroke individuals. The estimated muscle-specific EMG envelopes could be used to drive forward person-specific musculoskeletal models and estimate resulting joint torques accurately across all healthy and post-stroke individuals and across different walking speeds (R2  > 0.82 and RMSD  < 0.16). The technology we proposed opens new avenues for automated muscle localization and quantitative musculoskeletal function assessment during gait in both healthy and neurologically impaired individuals.

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.

Synergy-Driven Musculoskeletal Modeling to Estimate Muscle Excitations and Joint Moments at Different Walking Speeds in Individuals with Transtibial Amputation

The main requirement for an amputee is to regain the function of the lost limb. In order to fully benefit from powered prosthetic legs, the user must rely on the dynamic control of the device. Progress in high-level control for powered prosthetic legs is currently challenged by the inability of current control schemes to generalize to large repertoires of movements as well as adapting to external mechanical demands. This ultimately leads the user to adopt compensatory movements, lack of comfort, higher energy requirements during walking and standing. This study uses a feedforward model of muscle activation and force generation that applies mathematical formulations of muscle synergies to generate synthetic activation profiles underlying walking across different speeds. Estimated activation profiles are used to drive forward subject-specific numerical models of the lower extremity musculoskeletal system. The model was validated on one individual with uni-lateral transtibial amputation and its predictions were compared to experimental torques from inverse dynamic calculations. Results showed that a generic muscle synergy driven personalized musculoskeletal model can fit the ankle torques of the intact limb of a person with transtibial amputation (RMSD = 0.1329±0.02). The estimated moments might be suitable as the control signal to drive powered prostheses to ultimately improve physical interaction between the user and a powered prostheses during dynamic motor tasks.

Predictive Control of Peak Achilles Tendon Force in a Simulated System of the Human Ankle Joint with a Parallel Artificial Actuator During Hopping

Latest advances in wearable exoskeletons for the human lower extremity predominantly focus on minimising metabolic cost of walking. However, there currently is no robotic exoskeleton that gains control on the mechanics of biological tissues such as biological muscles or series-elastic tendons. Achieving robotic control of biological tissue mechanics would enable prevention of musculoskeletal injuries or the personalization of rehabilitation treatments following injury with levels of precisions not attained before. In this paper, we introduce a new framework that uses nonlinear model predictive control (NMPC) for the closed-loop control of peak tendon force in a simulated system of the human ankle joint with parallel exoskeletal actuation. We propose a computationally efficient NMPC's inner model consisting of explicit, closed-form equations of muscle-tendon dynamics along with those of the ankle joint with parallel actuation. The proposed formulation is tested and verified on movement data collected during dynamic ankle dorsiflexion/plantarflexion rotations executed on a dynamometer as well as during walking and running on a treadmill. The framework designed using the NMPC controller showed a promising performance in keeping the Achilles tendon force under a predefined threshold. Results indicated that our proposed model was generalizable to different muscles and gaits and suitable for real-time applications due to its low computational time.

Subject-Specific and COM Acceleration-Enhanced Reflex Neuromuscular Model to Predict Ankle Responses in Perturbed Gait

Subject-specific musculoskeletal models generate more accurate joint torque estimates from electromyography (EMG) inputs in relation to experimentally obtained torques. Similarly, reflex Neuromuscular Models (NMMs) that employ COM states in addition to musculotendon information generate muscle activations to musculoskeletal models that better predict ankle torques during perturbed gait. In this study, the reflex NMM of locomotion of one subject is identified by employing an EMG-calibrated musculoskeletal model in unperturbed and perturbed gait. A COM acceleration-enhanced reflex NMM is identified. Subject-specific musculoskeletal models improve torque tracking of the ankle joint in unperturbed and perturbed conditions. COM acceleration-enhanced reflex NMM improves ankle torque tracking especially in early stance and during backward perturbation. Results found herein can guide the implementation of reflex controllers in active prosthetic and orthotic devices.

Does a Soft Actuated Back Exosuit Influence Multimodal Physiological Measurements and User Perception During an Industry Inspired Task?

Back support soft exosuits are promising solutions to reduce risk of musculoskeletal injuries at workplaces resulting from physically demanding and repetitive lifting tasks. Design of novel active exosuits address the impact on the muscle activity and metabolic costs but do not consider other critical aspects such as comfort and user perception during the intended tasks. Thus, in this study, we describe a novel soft active exosuit in line with its impact on physiological and subjective measures during lifting. We tested four healthy participants who performed repetitive lifting tasks with and without this exosuit. The exosuit provided assistance proportional to the lumbar flexion angle measured using an inertial measurement unit. We measured the participant's multimodal physiological measures including surface electromyography, metabolic cost, heart rate, and skin temperature. We also measured subjective scores on user exertion, task load, and device acceptability. All participants perceived a reduction in task load when using the exosuit. Three participants showed reduction of muscle activity for the erector spinae muscles. The metabolic costs and heart rate reserve reduced for two participants, with similar trends for skin temperature. For future development of workplace exosuits, we recommend incorporating assessments of both physiological and subjective measures, considering the user-dependent response to the exosuit.

Role of bone marrow adipocytes in bone metastasis development and progression: a systematic review

Purpose

Bone marrow adipocytes (BMAs) are the most plentiful cells in the bone marrow and function as an endocrine organ by producing fatty acids, cytokines, and adipokines. Consequently, BMAs can interact with tumor cells, influencing both tumor growth and the onset and progression of bone metastasis. This review aims to systematically evaluate the role of BMAs in the development and progression of bone metastasis.

Methods

A comprehensive search was conducted on PubMed, Web of Science, and Scopus electronic databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement standards, to identify studies published from March 2013 to June 2023. Two independent reviewers assessed and screened the literature, extracted the data, and evaluated the quality of the studies. The body of evidence was evaluated and graded using the ROBINS-I tool for non-randomized studies of interventions and the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool for in vivo studies. The results were synthesized using descriptive methods.

Results

The search yielded a total of 463 studies, of which 17 studies were included in the final analysis, including 15 preclinical studies and two non-randomized clinical studies. Analysis of preclinical studies revealed that BMAs play a significant role in bone metastasis, particularly in prostate cancer followed by breast and malignant melanoma cancers. BMAs primarily influence cancer cells by inducing a glycolytic phenotype and releasing or upregulating soluble factors, chemokines, cytokines, adipokines, tumor-derived fatty acid-binding protein (FABP), and members of the nuclear receptor superfamily, such as chemokine (C-C motif) ligand 7 (CCL7), C-X-C Motif Chemokine Ligand (CXCL)1, CXCL2, interleukin (IL)-1β, IL-6, FABP4, and peroxisome proliferator-activated receptor γ (PPARγ). These factors also contribute to adipocyte lipolysis and regulate a pro-inflammatory phenotype in BMAs. However, the number of clinical studies is limited, and definitive conclusions cannot be drawn.

Conclusion

The preclinical studies reviewed indicate that BMAs may play a crucial role in bone metastasis in prostate, breast, and malignant melanoma cancers. Nevertheless, further preclinical and clinical studies are needed to better understand the complex role and relationship between BMAs and cancer cells in the bone microenvironment. Targeting BMAs in combination with standard treatments holds promise as a potential therapeutic strategy for bone metastasis.

Real-time lumbosacral joint loading estimation in exoskeleton-assisted lifting conditions via electromyography-driven musculoskeletal models

Lumbar joint compression forces have been linked to the development of chronic low back pain, which is specially present in occupational environments. Offline methodologies for lumbosacral joint compression force estimation are not commonly integrated in occupational or medical applications due to the highly time-consuming and complex post-processing procedures. Hence, applications such as real-time adjustment of assistive devices (i.e., back-support exoskeletons) for optimal modulation of compression forces remains unfeasible. Here, we present a real-time electromyography (EMG)-driven musculoskeletal model, capable of estimating accurate lumbosacral joint moments and plausible compression forces. Ten participants performed box-lifting tasks (5 and 15 kg) with and without the Laevo Flex back-support exoskeleton using squat and stoop lifting techniques. Lumbosacral kinematics and EMGs from abdominal and thoracolumbar muscles were used to drive, in real-time, subject-specific EMG-driven models, and estimate lumbosacral joint moments and compression forces. Real-time EMG-model derived moments showed high correlations (R2 = 0.76 - 0.83) and estimation errors below 30% with respect to reference inverse dynamic moments. Compared to unassisted lifting conditions, exoskeleton liftings showed mean lumbosacral joint moments and compression forces reductions of 11.9 - 18.7 Nm (6 - 12% of peak moment) and 300 - 450 N (5 - 10%), respectively. Our modelling framework was capable of estimating in real-time, valid lumbosacral joint moments and compression forces in line with in vivo experimental data, as well as detecting the biomechanical effects of a passive back-support exoskeleton. Our presented technology may lead to a new class of bio-protective robots in which personalized assistance profiles are provided based on subject-specific musculoskeletal variables.

A Wearable Real-time Kinematic and Kinetic Measurement Sensor Setup for Human Locomotion

Current laboratory-based setups (optical marker cameras + force plates) for human motion measurement require participants to stay in a constrained capture region which forbids rich movement types. This study established a fully wearable system, based on commercially available sensors (inertial measurement units + pressure insoles) that can measure both kinematic and kinetic motion data simultaneously and support wireless frame-by-frame streaming. In addition, its capability and accuracy were tested against a conventional laboratory-based setup. An experiment was conducted, with 9 participants wearing the wearable measurement system and performing 13 daily motion activities, from slow walking to fast running, together with vertical jump, squat, lunge and single-leg landing, inside the capture space of the laboratory-based motion capture system. The recorded sensor data were post-processed to obtain joint angles, ground reaction forces (GRFs), and joint torques (via multi-body inverse dynamics). Compared to the laboratory-based system, the established wearable measurement system can measure accurate information of all lower limb joint angles (Pearson's r = 0.929), vertical GRFs (Pearson's r = 0.954), and ankle joint torques (Pearson's r = 0.917). Center of pressure (CoP) in the anterior-posterior direction and knee joint torques were fairly matched (Pearson's r = 0.683 and 0.612, respectively). Calculated hip joint torques and measured medial-lateral CoP did not match with the laboratory-based system (Pearson's r = 0.21 and 0.47, respectively). Furthermore, both raw and processed datasets are openly accessible (https://doi.org/10.5281/zenodo.6457662). Documentation, data processing codes, and guidelines to establish the real-time wearable kinetic measurement system are also shared (https://github.com/HuaweiWang/WearableMeasurementSystem).

Real-time myoelectric control of wrist/hand motion in Duchenne muscular dystrophy: A case study

Introduction: Duchenne muscular dystrophy (DMD) is a genetic disorder that induces progressive muscular degeneration. Currently, the increase in DMD individuals' life expectancy is not being matched by an increase in quality of life. The functioning of the hand and wrist is central for performing daily activities and for providing a higher degree of independence. Active exoskeletons can assist this functioning but require the accurate decoding of the users' motor intention. These methods have, however, never been systematically analyzed in the context of DMD.

Methods: This case study evaluated direct control (DC) and pattern recognition (PR), combined with an admittance model. This enabled customization of myoelectric controllers to one DMD individual and to a control population of ten healthy participants during a target-reaching task in 1- and 2- degrees of freedom (DOF). We quantified real-time myocontrol performance using target reaching times and compared the differences between the healthy individuals and the DMD individual.

Results and Discussion: Our findings suggest that despite the muscle tissue degeneration, the myocontrol performance of the DMD individual was comparable to that of the healthy individuals in both DOFs and with both control approaches. It was also evident that PR control performed better for the 2-DOF tasks for both DMD and healthy participants, while DC performed better for the 1-DOF tasks. The insights gained from this study can lead to further developments for the intuitive multi-DOF myoelectric control of active hand exoskeletons for individuals with DMD.

Person-specific biophysical modelling of alpha-motoneuron pools driven by in vivo decoded neural synaptic input

Interfacing with alpha-motoneurons (MNs) is key to understand and control motor impairment and neurorehabilitation technologies. Depending on the neurophysiological condition of each individual, MN pools exhibit distinct neuro-anatomical properties and firing behaviors. Hence, the ability to assess subject-specific characteristics of MN pools is essential for unravelling the neural mechanisms and adaptations underlying motor control, both in healthy and impaired individuals. However, measuring in vivo the properties of complete human MN pools remains an open challenge. Therefore, this work proposes a novel approach based on decoding neural discharges from human MNs in vivo for driving the metaheuristic optimization of biophysically realistic MN models. First, we show that this framework provides subject-specific estimates of MN pool properties from the tibialis anterior muscle on five healthy individuals. Second, we propose a methodology to create complete pools of in silico MNs for each subject. Lastly, we show that neural-data driven complete in silico MN pools reproduce in vivo MN firing characteristics and muscle activation profiles during force-tracking tasks involving isometric ankle dorsi-flexion, at different levels of amplitude. This approach can open new avenues for understanding human neuro-mechanics and, particularly, MN pool dynamics, in a person-specific way. Thereby enabling the development of personalized neurorehabilitation and motor restoring technologies.

Electromyography-driven model-based estimation of ankle torque and stiffness during dynamic joint rotations in perturbed and unperturbed conditions

The simultaneous modulation of joint torque and stiffness enables humans to perform large repertoires of movements, while versatilely adapting to external mechanical demands. Multi-muscle force control is key for joint torque and stiffness modulation. However, the inability to directly measure muscle force in the intact moving human prevents understanding how muscle force causally links to joint torque and stiffness. Joint stiffness is predominantly estimated via joint perturbation-based experiments in combination with system identification techniques. However, these techniques provide joint-level stiffness estimations with no causal link to the underlying muscle forces. Moreover, the need for joint perturbations limits the generalizability and applicability to study natural movements. Here, we present an electromyography (EMG)-driven musculoskeletal modeling framework that can be calibrated to match reference joint torque and stiffness profiles simultaneously via a multi-term objective function. EMG-driven models calibrated on <2 s of reference torque and stiffness data could blindly estimate reference profiles across 100 s of data not used for calibration. Model calibrations using an objective function comprising torque and stiffness terms always provided less feasible solutions than an objective function comprising solely a torque term, thereby reducing the space of feasible muscle-tendon parameters. Results also showed the proposed framework's ability to estimate joint stiffness in unperturbed conditions, while capturing differences against stiffness profiles derived during perturbed conditions. The proposed framework may provide new ways for studying causal relationships between muscle force and joint torque and stiffness during movements in interaction with the environment, with broad implications across biomechanics, rehabilitation and robotics.

Automated estimation of ankle muscle EMG envelopes and resulting plantar-dorsi flexion torque from 64 garment-embedded electrodes uniformly distributed around the human leg

The design of personalized movement training and rehabilitation pipelines relies on the ability of assessing the activation of individual muscles concurrently with the resulting joint torques exerted during functional movements. Despite advances in motion capturing, force sensing and bio-electrical recording technologies, the estimation of muscle activation and resulting force still relies on lengthy experimental and computational procedures that are not clinically viable. This work proposes a wearable technology for the rapid, yet quantitative, assessment of musculoskeletal function. It comprises of (1) a soft leg garment sensorized with 64 uniformly distributed electromyography (EMG) electrodes, (2) an algorithm that automatically groups electrodes into seven muscle-specific clusters, and (3) a EMG-driven musculoskeletal model that estimates the resulting force and torque produced about the ankle joint sagittal plane. Our results show the ability of the proposed technology to automatically select a sub-set of muscle-specific electrodes that enabled accurate estimation of muscle excitations and resulting joint torques across a large range of biomechanically diverse movements, underlying different excitation patterns, in a group of eight healthy individuals. This may substantially decrease time needed for localization of muscle sites and electrode placement procedures, thereby facilitating applicability of EMG-driven modelling pipelines in standard clinical protocols.

Neural data-driven model of spinal excitability changes induced by transcutaneous electrical stimulation in spinal cord injury subjects

The efficacy of trans-spinal direct current stimulation (tsDCS) as neurorehabilitation technology remains sub-optimal, partly due to the variability introduced by subject-specific neurophysiological features and stimulation conditions (e.g. electrode placement, stimulating amplitude, polarity, etc.) Hence, current therapies apply tsDCS in an open-loop fashion, resulting in a lack of standardized protocols for controlling elicited neuronal adaptations in closed-loop. Through the combination of high-density electromyogram (HD-EMG) decomposition, biophysical neuronal modelling and metaheuristic optimization, this work presents a novel neural data-driven framework for estimating subject-specific features and quantifying acute neuronal adaptations elicited by tsDCS on incomplete spinal cord injury subjects. This approach consists of calibrating the anatomical parameters (e.g. soma diameter) of in silico $\alpha-$motoneuron (MN) models for firing similarly to in vivo MNs decoded from HD-EMG. Assuming that cathodal-tsDCS elicits excitability changes in the MN pool, while preserving their anatomical parameters, optimization of an excitability gain common to the entire pool was performed to minimize discrepancies in firing rate and recruitment time between in vivo and in silico MNs after cathodal-tsDCS. This quantification of excitability changes on MN models calibrated in a person specific way enables closing the loop with neuro-modulation devices to tailor neurorehabilitation therapies. Clinical Relevance - This framework addresses a key limitation in non-invasive neuro-modulative technologies via a novel model-assisted framework that enables quantifying acute excitability changes induced on a person-specific in silico MN pool calibrated using in vivo neural data. This will enable the development of advanced controllers for modulating targeted neuronal adaptations in closed-loop.

Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.

Identification of Motor Unit Twitch Properties in the Intact Human In Vivo

Restoring natural motor function in neurologically injured individuals is challenging, largely due to the lack of personalization in current neurorehabilitation technologies. Signal-driven neuro-musculoskeletal models may offer a novel paradigm for devising novel closed-loop rehabilitation strategies according to an individual's physiology. However, current modelling techniques are constrained to bipolar electromyography (EMG), thereby lacking the resolution necessary to extract the activity of individual motor units (MUs) in vivo. In this work, we decoded MU spike trains from high-density (HD)-EMG to obtain relevant neural properties across multiple isometric plantar-dorsiflexion tasks. Then, we sampled MU statistical distributions and used them to reproduce MU specific activation profiles. Results showed bimodal distributions which may correspond to slow and fast MU populations. The estimated activation profiles showed a high degree of similarity to the reference torque (R²>0.8) across the recorded muscles. This suggests that the estimation of MU twitch properties is a crucial step for the translation of neural information into muscle force.Clinical Relevance- This work has multiple implications for understanding the underlying mechanism of motor impairment and for developing closed-loop strategies for modulating alpha motor circuitries in neurologically injured individuals.

Clusterization of multi-channel electromyograms into muscle-specific activations to drive a subject-specific musculoskeletal model: towards fast and accurate clinical decision-making

Current clinical decision-making is based on rapid and subjective functional tests such as 10 m walking. Moreover, greater accuracy can be achieved at the expense of rapidity and costs. In biomechanical laboratories, advanced technologies and musculoskeletal modeling can quantitatively describe the biomechanical reasons underlying gait disorders. Our work aims to blend clinical rapidity and biomechanical accuracy through multi-channel (MC) electromyography (EMG) clustering and real-time neuro-musculoskeletal (NMS) modeling techniques integrated into a sensorized wearable garment that is quick to set up. Here we present a unique pipeline that goes from MC EMG signals to ankle torque estimation following two steps: (1) non-negative matrix factorization (NNMF)-based EMG clustering for the extraction of muscle-specific activations and (2) subject-specific EMG-driven NMS modeling. The results show the potential of NNMF as an electrode clustering tool, as well as the ability to predict joint torque during movements that were not used for the EMG clustering.

Optimization framework for the model-based estimation of in vivo α-motoneuron properties in the intact human

The in vivo estimation of α-motoneuron (MN) properties in humans is crucial to characterize the effect that neurorehabilitation technologies may elicit over the composite neuro-musculoskeletal system. Here, we combine biophysical neuronal modelling, high-density electromyography and convolutive blind-source separation along with numerical optimization to estimate geometrical and electrophysiological properties of in vivo decoded human MNs. The proposed methodology implements multi-objective optimization to automatically tune ionic channels conductance and soma size of MN models for minimizing the error between several features of simulated and in vivo decoded MN spike trains. This approach will open new avenues for the closed-loop control of motor restorative technologies such as wearable robots and neuromodulation devices.Clinical Relevance- This work proposes a non-invasive framework for the in vivo estimation of person-specific α-motoneuron properties. This will enable predicting neuronal adaptations in response to neurorehabilitation therapies in the intact human.

Pulmonary embolism in patients with COVID-19 pneumonia: When we have to search for it?

Background

COVID-19 is still a global challenge in regard for management and therapy. Pulmonary embolism (PE) seems to have a higher prevalence in COVID-19 instead of non-COVID patients. Clinical and laboratory parameters related with PE are still unknown.

Methods

We conducted a retrospective unicentre study in Alto Vicentino Hospital between March 1st, 2020, and January 31st, 2021 in patients admitted for COVID-19 tested with a RT-PCR nasal swab. Data about patients studied with computed tomography pulmonary angiogram (CTPA) because of PE suspicion were collected, as their clinical and laboratory parameters too.

Results

2621 patients were admitted for COVID-19 in Alto Vicentino Hospital between March 1st, 2020, and January 31st, 2021 and in 267 of them a CTPA was performed finding 50 PE (18.7%). Only non-Caucasian race (OR = 5.44; 95% CI 1.22–24.35; p = 0.027) and previous VTE (OR = 5.3; 95% CI 1.09–26.17; p = 0.039) were found to be independently associated with PE.

Conclusion

PE is a frequent complication of COVID-19 and clinician need high degree of suspicion because clinical and laboratoristic parameters cannot drive diagnosis.

Mayfly taxonomy (Arthropoda: Hexapoda: Ephemeroptera) during the first two decades of the twenty-first century and the concentration of taxonomic publishing

The twentieth anniversary of the first issue of Zootaxa (De Moraes Freire, 2001) provides an appropriate opportunity to reflect on some trends in global Ephemeroptera taxonomy publishing over the last two decades, with a focus on the description of new species and the outsized role of the journals Zootaxa and ZooKeys, in particular. Detailed reviews of world Ephemeroptera knowledge up to about 2000 were collected in a series of nine papers from a symposium on the subject, published together in the proceedings of the ninth International Conference on Ephemeroptera (Domínguez 2001). Domínguez Dos Santos (2014) provided updates and analysis for South America up to the year 2012. More recent detailed accounts of regional and taxonomic diversity, and other aspects of mayfly biology and ecology, were reviewed by Jacobus et al. (2019), while Ogden et al. (2019) discussed current issues involving higher classification.

Electromyography-informed modeling for estimating muscle activation and force alterations in Parkinson's disease

Electromyography (EMG)-driven neuromusculoskeletal modeling (NMSM) enables simulating the mechanical function of multiple muscle-tendon units as controlled by nervous system in the generation of complex movements. In the context of clinical assessment this may enable understanding biomechanical factor contributing to gait disorders such as one induced by Parkinson's disease (PD). In spite of the challenges in the development of patient-specific models, this preliminary study aimed at establishing a feasible and noninvasive experimental and modeling pipeline to be adopted in clinics to detect PD-induced gait alterations. Four different NMSM have been implemented for three healthy controls using CEINMS, an OpenSim-compatible toolbox. Models differed in the EMG-normalization methods used for calibration purposes (i.e. walking trial normalization and maximum voluntary contraction normalization) and in the set of experimental EMGs used for the musculotendon-unit mapping (i.e. 4 channels vs. 15 channels). Model accuracy assessment showed no statistically significant differences between the more complete model (non-clinically viable) and the proposed reduced one (clinically viable). The clinically viable reduced model was systematically applied on a dataset including ten PD's and thirteen healthy controls. Results showed significant differences in the neuromuscular control strategy of the PD group in term of muscle forces and joint torques. Indeed, PD patients displayed a significantly lower magnitude on force production and revealed a higher amount of force variability with the respect of the healthy controls. The estimated variables could become a measurable biomechanical outcome to assess and track both disease progression and its impact on gait in PD subjects.

Conyza bonariensis as an alternative host for Colletotrichum species in Argentina

AIMS

This study investigated the diversity of Colletotrichum isolates recovered from Conyza bonariensis leaves through the use of morphological characteristics, growth rate, carbon sources utilization and phylogenetic analysis.

METHODS AND RESULTS

In all, 30 Colletotrichum isolates recovered from C. bonariensis leaves showing symptoms of disease were included in the present study. Based on the analysis of morphology and sequences, the isolates were distributed into six Colletotrichum species complexes. The concatenated alignment of GAPDH and ITS sequences showed that 20 out of 30 isolates were included in four species complexes which comprise the most important pathogens causing anthracnose in soybean or anthracnose and stalk rot in maize: C. truncatum, C. orchidearum, C. gloeosporioides and C. graminicola. The remaining 10 isolates were included in the C. boninense and C. destructivum species complexes or could not be assigned to any complex with the available information.

CONCLUSION

Weeds belonging to genus Conyza are host to soybean and maize potential pathogenic species of Colletotrichum and could have a role as inoculum reservoir for cross contamination in the agroecosystem.

SIGNIFICANCE AND IMPACT OF THE STUDY

The combined use of morphological, kinetics and physiological parameters of growth and phylogenetic analysis in Colletotrichum isolates from Conyza leaves allowed the detection of species complexes previously not identified in Argentina.

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.

Putative role of Brugada syndrome genes in familial atrial fibrillation

OBJECTIVE

Familial atrial fibrillation (FAF), a not uncommon arrhythmia of the atrium, is characterized by heritability, early onset and absence of other heart defects. The molecular and genetic basis is still not completely clear and genetic diagnosis cannot be achieved in about 90% of patients. In this study, we present the results of genetic screening by next generation sequencing in affected Russian families.

PATIENTS AND METHODS

Sixty subjects (18 probands and 42 relatives) with a clinical diagnosis of FAF were enrolled in the study. Since AF frequently associates with other cardiomyopathies, we included all genes that were known to be associated with these disorders at the time of our study. All probands were therefore systematically screened for 47 genes selected from the literature.

RESULTS

Our study revealed that seven variants co-segregated with the clinical phenotype in seven families. Interestingly, four out of six genes and three out of seven variants have already been associated with Brugada syndrome in the literature.

CONCLUSIONS

To our knowledge, this is the first report of association of the CACNA1C, CTNNA3, PKP2, ANK2 and SCN10A genes with FAF; it is also the first study in Russian families.

Interfacing With Alpha Motor Neurons in Spinal Cord Injury Patients Receiving Trans-spinal Electrical Stimulation

Trans-spinal direct current stimulation (tsDCS) provides a non-invasive, clinically viable approach to potentially restore physiological neuromuscular function after neurological impairment, e.g., spinal cord injury (SCI). Use of tsDCS has been hampered by the inability of delivering stimulation patterns based on the activity of neural targets responsible to motor function, i.e., α-motor neurons (α-MNs). State of the art modeling and experimental techniques do not provide information about how individual α-MNs respond to electrical fields. This is a major element hindering the development of neuro-modulative technologies highly tailored to an individual patient. For the first time, we propose the use of a signal-based approach to infer tsDCS effects on large α-MNs pools in four incomplete SCI individuals. We employ leg muscles spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-MNs discharges across multiple lumbosacral segments during isometric plantar flexion sub-maximal contractions. This is done before, immediately after and 30 min after sub-threshold cathodal stimulation. We deliver sham tsDCS as a control measure. First, we propose a new algorithm for removing compromised information from decomposed α-MNs spike trains, thereby enabling robust decomposition and frequency-domain analysis. Second, we propose the analysis of α-MNs spike trains coherence (i.e., frequency-domain) as an indicator of spinal response to tsDCS. Results showed that α-MNs spike trains coherence analysis sensibly varied across stimulation phases. Coherence analyses results suggested that the common synaptic input to α-MNs pools decreased immediately after cathodal tsDCS with a persistent effect after 30 min. Our proposed non-invasive decoding of individual α-MNs behavior may open up new avenues for the design of real-time closed-loop control applications including both transcutaneous and epidural spinal electrical stimulation where stimulation parameters are adjusted on-the-fly.

Characterization of Forearm Muscle Activation in Duchenne Muscular Dystrophy via High-Density Electromyography: A Case Study on the Implications for Myoelectric Control

Duchenne muscular dystrophy (DMD) is a genetic disorder that results in progressive muscular degeneration. Although medical advances increased their life expectancy, DMD individuals are still highly dependent on caregivers. Hand/wrist function is central for providing independence, and robotic exoskeletons are good candidates for effectively compensating for deteriorating functionality. Robotic hand exoskeletons require the accurate decoding of motor intention typically via surface electromyography (sEMG). Traditional low-density sEMG was used in the past to explore the muscular activations of individuals with DMD; however, it cannot provide high spatial resolution. This study characterized, for the first time, the forearm high-density (HD) electromyograms of three individuals with DMD while performing seven hand/wrist-related tasks and compared them to eight healthy individuals (all data available online). We looked into the spatial distribution of HD-sEMG patterns by using principal component analysis (PCA) and also assessed the repeatability and the amplitude distributions of muscle activity. Additionally, we used a machine learning approach to assess DMD individuals' potentials for myocontrol. Our analysis showed that although participants with DMD were able to repeat similar HD-sEMG patterns across gestures (similarly to healthy participants), a fewer number of electrodes was activated during their gestures compared to the healthy participants. Additionally, participants with DMD activated their muscles close to maximal contraction level (0.63 ± 0.23), whereas healthy participants had lower normalized activations (0.26 ± 0.2). Lastly, participants with DMD showed on average fewer PCs (3), explaining 90% of the complete gesture space than the healthy (5). However, the ability of the DMD participants to produce repeatable HD-sEMG patterns was unexpectedly comparable to that of healthy participants, and the same holds true for their offline myocontrol performance, disproving our hypothesis and suggesting a clear potential for the myocontrol of wearable exoskeletons. Our findings present evidence for the first time on how DMD leads to progressive alterations in hand/wrist motor control in DMD individuals compared to healthy. The better understanding of these alterations can lead to further developments for the intuitive and robust myoelectric control of active hand exoskeletons for individuals with DMD.

Neuro-Musculoskeletal Mapping for Man-Machine Interfacing

We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function.

Model-Based Estimation of Ankle Joint Stiffness During Dynamic Tasks: a Validation-Based Approach

Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel 'perturbation-free' stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses.

Estimation of Time-Varying Ankle Joint Stiffness Under Dynamic Conditions via System Identification Techniques

An important goal in the design of next-generation exoskeletons and limb prostheses is to replicate human limb dynamics. Joint impedance determines the dynamic relation between joint displacement and torque. Joint stiffness is the position-dependent component of joint impedance and is key in postural control and movement. However, the mechanisms to modulate joint stiffness are not fully understood yet. The goal of this study is to conduct a systematic analysis on how humans modulate ankle stiffness. Time-varying stiffness was estimated for six healthy subjects under isometric, as well as quick and slow dynamic conditions via system identification techniques; specifically, an ensemble-based algorithm using short segments of ankle torque and position recordings. Our results show that stiffness had the lowest magnitude under quick dynamic conditions. Under isometric conditions, with fixed position and varying muscle activity, stiffness exhibited a higher magnitude. Finally, under slow dynamic conditions, stiffness was found to be the highest. Our results highlight, for the first time, the variability in stiffness modulation strategies across conditions, especially across movement velocity.

A Case Study With Symbihand: An sEMG-Controlled Electrohydraulic Hand Orthosis for Individuals With Duchenne Muscular Dystrophy

With recent improvements in healthcare, individuals with Duchenne muscular dystrophy (DMD) have prolonged life expectancy, and it is therefore vital to preserve their independence. Hand function plays a central role in maintaining independence in daily living. This requires sufficient grip force and the ability to modulate it with no substantially added effort. Individuals with DMD have low residual grip force and its modulation is challenging and fatiguing. To assist their hand function, we developed a novel dynamic hand orthosis called SymbiHand, where the user's hand motor intention is decoded by means of surface electromyography, enabling the control of an electrohydraulic pump for actuation. Mechanical work is transported using hydraulic transmission and flexible structures to redirect interaction forces, enhancing comfort by minimizing shear forces. This paper outlines SymbiHand's design and control, and a case study with an individual with DMD. Results show that SymbiHand increased the participant's maximum grasping force from 2.4 to 8 N. During a grasping force-tracking task, muscular activation was decreased by more than 40% without compromising task performance. These results suggest that SymbiHand has the potential to decrease muscular activation and increase grasping force for individuals with DMD, adding to the hand a total mass of no more than 241 g. Changes in mass distributions and an active thumb support are necessary for improved usability, in addition to larger-scale studies for generalizing its assistive potential.

Use of Antibiotic Loaded Biomaterials for the Management of Bone Prosthesis Infections: Rationale and Limits

BACKGROUND

Periprosthetic joint infection still represents a challenging issue for the orthopedic community. In the United States approximately a million joint arthroplasties are performed each year, with infection rates ranging from 1 to 2%: revisions has significant implications on health care costs and appropriate resource management. The use of locally applied antibiotics as a prophylaxis measure or as a component of the therapeutic approach in primary or revision surgery is finalized at eliminating any microorganism and strengthening the effectiveness of systemic therapy.

OBJECTIVE

The present review of clinical and preclinical in vivo studies tried to identify advantages and limitations of the materials used in the clinical orthopedic practice and discuss developed biomaterials, innovative therapeutic approaches or strategies to release antibiotics in the infected environment.

METHODS

A systematic search was carried out by two independent observers in two databases (www.pubmed.com and www.scopus.com) in order to identify pre-clinical and clinical reports in the last 10 years.

RESULTS

71 papers were recognized eligible: 15 articles were clinical studies and 56 in vivo studies.

CONCLUSION

Polymethylmethacrylate was the pioneer biomaterial used to manage infections after total joint replacement. Despite its widespread use, several issues still remain debated: the methods to combine materials and antibiotics, the choice of antibiotics, releasing kinetics and antibiotics efficacy. In the last years, the interest was directed towards the selection of different antibiotics, loaded in association with more than only one class and biomaterials with special focus on delivery systems as implant surface coatings, hydrogels, ceramics, micro-carriers, microspheres or nanoparticles.

P1564Atrial fibrillation and thromboembolic prophylaxis: focus on the frail oldest patient. how net clinical benefit influences anticoagulant therapy

Background

Atrial fibrillation is highly prevalent among the elderly population, which is also frequently prone to thromboembolic complications. Anticoagulant prophylaxis is underused in the elderly due to fear of bleeding, which tends to be more frequent and severe within this group. Randomized controlled trials and several observational studies have shown the comparative effectiveness of direct oral anticoagulant (DOAC) against vitamin K antagonists (VKA), and their superior safety, at least in terms of intracranial bleeding. However, for patients aged 85 or older, there is still insufficient literature, leaving unanswered the question of which prophylaxis to use.

Purpose

The aim of the study is to compare the incidence of established outcomes and to investigate the net clinical benefit between DOAC and VKA in patients aged ≥85 years.

Methods

A cohort of 553 outpatients from the real world began treatment using DOACs at our clinic. The prospective follow-up on average lasted 1.97 years. Main endpoints were stroke and systemic thromboembolism, major hemorrhage, myocardial infarction and mortality for all causes. A sample of the 160 patients aged ≥85 years was compared with the remaining younger ones and with a second cohort of 298 outpatients aged ≥85 years. Retrospectively analyzed, with follow-ups at our center, who started VKAs; the average time was 2.03 years. The “net clinical benefit” of DOACs against VKAs was calculated as the difference between thromboembolic events with VKAs and with DOACs, minus the difference (weighted by 1.5) between spontaneous intracranial bleeding with DOACs and VKAs.

Results

In terms of thromboembolic events, DOACs and VKAs (2.43% p-y vs. 1.82% p-y, p=0.975) have shown comparable efficacy in a higher risk sample (CHA2DS2-VASc score: 5.2 vs. 4.5; p<0.001). There were no differences in spontaneous intracranial hemorrhages (0.81% p-y vs. 1.16% p-y; p=0.419). Major bleeding was more frequent in DOACs (10.11% p-y vs. 4.13% p-y, p<0.05), although they are comparable if we consider patients in VKAs achieving a time in therapeutic range (TTR) <60%. Mortality, in all cases similar (13.75% p-y vs. 9.92% p-y; p=0.778), but was reduced in patients with VKAs therapy, with a TTR ≥60%. The net clinical benefit of DOACs compared to VKAs is noticeable in patients with a previous stroke or with CHA2DS2-VASc score <6, while VKAs may be more beneficial to patients with vascular disease or with CHA2DS2-VASc score ≥6.

Conclusions

DOACs are as effective in very old patients with atrial fibrillation compared to very old patients receiving VKAs, but they are associated with increased major bleeding. The same is true when compared with younger DOACs users. TTR, representing quality achieved by the anticoagulation with a VKA, can influence the comparison with DOACs. Considering ischemic strokes and spontaneous intracranial hemorrhages, there is however a net benefit for DOACs use in specific categories of elderly patients.

P6258Direct oral anticoagulants in non-valvular atrial fibrillation: accuracy of traditional bleeding scores in the elderly

Background

Due to the fear of increased risk of bleeding, anticoagulation treatment is underutilized in the prevention of stroke in elderly patients with non-valvular atrial fibrillation (NVAF). Although direct oral anticoagulants (DOAC) are safer than VKA, still little is known about the risk factors associated with bleeding in elderly patients treated with DOAC. Furthermore, it is still uncertain whether the risk scores that are currently used can serve to effectively identify higher bleeding risk in elderly subjects.

Purpose

The aim of this study was to identify predictors of bleeding in a cohort of elderly people affected by NVAF treated with DOAC, and to evaluate the accuracy of risk scores for bleeding used at present.

Methods

Data on outpatients aged ≥75 years, naïve for DOAC therapy, who started therapy with Dabigatran, Rivaroxaban, Apixaban or Edoxaban for the prevention of thromboembolism during FANV were analyzed. HASBLED, ATRIA, OBRI and ORBIT scores were calculated for each patient. Patients had follow-up for 12 months during which deaths, therapy discontinuation and adverse events such as thromboembolism and bleeding were reported. Potential predictors of bleeding and the predictive value of each bleeding score were tested using univariate Cox regression; testing accuracy was evaluated using ROC curves.

Results

A total of 291 patients (52.9% female, mean age 82.85±5.18 years) had a median follow-up time of 11 (10–12) months. The incidence rate of major bleeding was 4.7 per 100 patient-years, the rate of intracranial bleeding was 0.4 per 100 patient-years. Patients who had major bleeding were more often affected by heart failure (63.6% vs 25%; p=0.009) and thrombocytopenia (36.4% vs 7.4%; p=0,009). However in the multivariate analysis only heart failure remained statistically associated with major bleeding (HR 3.83, 95% CI 1.06–13.85; p=0.041). None of tested bleeding risk scores was able to predict major bleeding in our cohort. HASBLED and ORBIT scores were able to predict major and non-major clinically relevant bleeding (HR 1.32; 95% CI 1.01–1.71; p=0.042 and HR 1.20; 95% CI 1.00- 1.43; p=0.046); only the ORBIT score was found to be statistically significant, but with weak discriminatory power at ROC curves (AUC 0.59; 95% CI 0.51–0.68; p=0.041).

Conclusions

In our cohort of elderly patients aged 75 or older, anticoagulated for NVAF, heart failure history was the only effective predictor of major bleeding risk during DOAC treatment. None of the bleeding risk scores used currently have demonstrated a good discriminatory power in our cohort. As predictive factors of bleedings in DOAC-treated patients may not be the same as those for VKA-treated patients and those in elderly may also be differ in younger people, it calls for more investigation on the topic.

Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling

BACKGROUND

Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery.

METHODS

We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time.

RESULTS

We demonstrated patients' control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients.

CONCLUSIONS

Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.

A Variable Stiffness Actuator Module With Favorable Mass Distribution for a Bio-inspired Biped Robot

Achieving human-like locomotion with humanoid platforms often requires the use of variable stiffness actuators (VSAs) in multi-degree-of-freedom robotic joints. VSAs possess 2 motors for the control of both stiffness and equilibrium position. Hence, they add mass and mechanical complexity to the design of humanoids. Mass distribution of the legs is an important design parameter, because it can have detrimental effects on the cost of transport. This work presents a novel VSA module, designed to be implemented in a bio-inspired humanoid robot, Binocchio, that houses all components on the same side of the actuated joint. This feature allowed to place the actuator's mass to more proximal locations with respect to the actuated joint instead of concentrating it at the joint level, creating a more favorable mass distribution in the humanoid. Besides, it also facilitated it's usage in joints with centralized multi-degree of freedom (DoF) joints instead of cascading single DoF modules. The design of the VSA module is presented, including it's integration in the multi-DoFs joints of Binocchio. Experiments validated the static characteristics of the VSA module to accurately estimate the output torque and stiffness. The dynamic responses of the driving and stiffening mechanisms are shown. Finally, experiments show the ability of the actuation system to replicate the envisioned human-like kinematic, torque and stiffness profiles for Binocchio.