Biomechanical Evaluation of Stair Ambulation Using Impedance Control on an Active Prosthesis.

Active prostheses can provide net positive work to individuals with amputation, offering more versatility across locomotion tasks than passive prostheses. However, the effect of powered joints on bilateral biomechanics has not been widely explored for ambulation modes different than level ground and treadmill walking. In this study, we present the bilateral biomechanics of stair ascent and descent with a powered knee-ankle prosthesis compared to the biomechanical profiles of able-bodied subjects at different configurations of stair height between 102 mm and 178 mm. In addition, we include reference profiles from users with passive prostheses for the nominal stair height of 152 mm to place our findings in relation to the typical solution for individuals with transfemoral amputation (TFA). We report the biomechanical profiles of kinematics, kinetics, and power, together with temporal and waveform symmetry and distribution of mechanical energy across the joints. We found that an active prosthesis provides a substantial contribution to mechanical power during stair ascent and power absorption during stair descent and gait patterns like able-bodied subjects. The active prosthesis enables step-over-step gait in stair ascent. This translates into a lower mechanical energy requirement on the intact side, with a 57% reduction of energy at the knee and 26% at the hip with respect to the passive prosthesis. For stair descent, we found a 28% reduction in the negative work done by the intact ankle. These results reflect the benefit of active prostheses, allowing the users to complete tasks more efficiently than passive legs. However, in comparison to able-bodied biomechanics, the results still differ from the ideal patterns. We discuss the limitations that explain this difference and suggest future directions for the design of impedance controllers by taking inspiration from the biological modulation of the knee moment as a function of the stair height.

User- and Speed-Independent Slope Estimation for Lower-Extremity Wearable Robots.

Wearable robots can help users traverse unstructured slopes by providing mode-specific hip, knee, and ankle joint assistance. However, generalizing the same assistance pattern across different slopes is not optimal. Control strategies that scale assistance based on slope are expected to improve the feel of the device and improve outcome measures such as decreasing metabolic cost. Prior numerical methods for slope estimation struggled to estimate slopes at variable walking speeds or were limited to a single estimation per gait cycle. This study overcomes these limitations by developing machine-learning methods that yield continuous, user- and speed-independent slope estimators for a variety of wearable robot applications using an able-bodied wearable sensor dataset. In a leave-one-subject-out cross-validation (N = 9), four-phase XGBoost regression models were trained on static-slope (fixed-slope) data and evaluated on a novel subject's static-slope and dynamic-slope (variable-slope) data. Using all available sensors, we achieved an average error of 0.88° and 1.73° mean absolute error (MAE) on static and dynamic slopes, respectively. Ankle prosthesis, knee-ankle prosthesis, and hip exoskeleton sensor suites yielded average errors under 2° MAE on static and dynamic slopes, except for the ankle prosthesis and hip exoskeleton cases on dynamic slopes which yielded an average error of 2.2° and 3.2° MAE, respectively. We found that the thigh inertial measurement unit contributed the most to a reduction in average error. Our findings suggest that reliable slope estimators can be trained using only static-slope data regardless of the type of lower-extremity wearable robot.

Neurological scoring and gait kinematics to assess functional outcome in an ovine model of ischaemic stroke.

Background: Assessment of functional impairment following ischaemic stroke is essential to determine outcome and efficacy of intervention in both clinical patients and pre-clinical models. Although paradigms are well described for rodents, comparable methods for large animals, such as sheep, remain limited. This study aimed to develop methods to assess function in an ovine model of ischaemic stroke using composite neurological scoring and gait kinematics from motion capture. Methods: Merino sheep (n = 26) were anaesthetised and subjected to 2 hours middle cerebral artery occlusion. Animals underwent functional assessment at baseline (8-, 5-, and 1-day pre-stroke), and 3 days post-stroke. Neurological scoring was carried out to determine changes in neurological status. Ten infrared cameras measured the trajectories of 42 retro-reflective markers for calculation of gait kinematics. Magnetic resonance imaging (MRI) was performed at 3 days post-stroke to determine infarct volume. Intraclass Correlation Coefficients (ICC's) were used to assess the repeatability of neurological scoring and gait kinematics across baseline trials. The average of all baselines was used to compare changes in neurological scoring and kinematics at 3 days post-stroke. A principal component analysis (PCA) was performed to determine the relationship between neurological score, gait kinematics, and infarct volume post-stroke. Results: Neurological scoring was moderately repeatable across baseline trials (ICC > 0.50) and detected marked impairment post-stroke (p < 0.05). Baseline gait measures showed moderate to good repeatability for the majority of assessed variables (ICC > 0.50). Following stroke, kinematic measures indicative of stroke deficit were detected including an increase in stance and stride duration (p < 0.05). MRI demonstrated infarction involving the cortex and/or thalamus (median 2.7 cm3, IQR 1.4 to 11.9). PCA produced two components, although association between variables was inconclusive. Conclusion: This study developed repeatable methods to assess function in sheep using composite scoring and gait kinematics, allowing for the evaluation of deficit 3 days post-stroke. Despite utility of each method independently, there was poor association observed between gait kinematics, composite scoring, and infarct volume on PCA. This suggests that each of these measures has discreet utility for the assessment of stroke deficit, and that multimodal approaches are necessary to comprehensively characterise functional impairment.

Predicting biological joint moment during multiple ambulation tasks.

Combining machine learning models with wearable sensing provides a key technique for understanding the biological effort, creating an alternative to inverse dynamics based on motion capture. In this study, we demonstrate a novel approach to not only estimate but predict the joint moment in advance for multiple ambulation modes. By combining electromyography (EMG), inertial measurement units (IMU), and electrogoniometers, we enable the prediction of the joint moment only from wearable sensors. We performed a forward feature selection to determine the best feature sets for different anticipation times of the intended moment generated at the hip, knee, and ankle, encompassing level walking on a treadmill and ascent/descent of stairs and ramps. We show that wearable sensors can predict the joint moment with an MAE of 0.06 ± 0.02 Nm/kg for direct estimation and an MAE of 0.10 ± 0.04 Nm/kg when predicting 150 ms in advance, corresponding to an MAE within 9.2% of the joint moment range. We found that the hip moment had a significantly lower error than the knee and ankle when anticipating the joint moment (Bonferroni test, p < 0.05). The accurate estimation of the joint moment could monitor user activity to reduce risk factors and inform the control of exoskeletons.

Subject-Independent Continuous Locomotion Mode Classification for Robotic Hip Exoskeleton Applications.

Autonomous lower-limb exoskeletons must modulate assistance based on locomotion mode (e.g., ramp or stair ascent) to adapt to the corresponding changes in human biological joint dynamics. However, current mode classification strategies for exoskeletons often require user-specific tuning, have a slow update rate, and rely on additional sensors outside of the exoskeleton sensor suite. In this study, we introduce a deep convolutional neural network-based locomotion mode classifier for hip exoskeleton applications using an open-source gait biomechanics dataset with various wearable sensors. Our approach removed the limitations of previous systems as it is 1) subject-independent (i.e., no user-specific data), 2) capable of continuously classifying for smooth and seamless mode transitions, and 3) only utilizes minimal wearable sensors native to a conventional hip exoskeleton. We optimized our model, based on several important factors contributing to overall performance, such as transition label timing, model architecture, and sensor placement, which provides a holistic understanding of mode classifier design. Our optimized DL model showed a 3.13% classification error (steady-state: 0.80 ± 0.38% and transitional: 6.49 ± 1.42%), outperforming other machine learning-based benchmarks commonly practiced in the field (p<0.05). Furthermore, our multi-modal analysis indicated that our model can maintain high performance in different settings such as unseen slopes on stairs or ramps. Thus, our study presents a novel locomotion mode framework, capable of advancing robotic exoskeleton applications toward assisting community ambulation.

A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions.

We introduce a novel dataset containing 3-dimensional biomechanical and wearable sensor data from 22 able-bodied adults for multiple locomotion modes (level-ground/treadmill walking, stair ascent/descent, and ramp ascent/descent) and multiple terrain conditions of each mode (walking speed, stair height, and ramp inclination). In this paper, we present the data collection methods, explain the structure of the open dataset, and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics as a function of the gait phase. This dataset offers a comprehensive source of locomotion information for the same set of subjects to motivate applications in locomotion recognition, developments in robotic assistive devices, and improvement of biomimetic controllers that better adapt to terrain conditions. With such a dataset, models for these applications can be either subject-dependent or subject-independent, allowing greater flexibility for researchers to advance the field.

A Machine Learning Strategy for Locomotion Classification and Parameter Estimation Using Fusion of Wearable Sensors.

The accurate classification of ambulation modes and estimation of walking parameters is a challenging problem that is key to many applications. Knowledge of the user's state can enable rehabilitative devices to adapt to changing conditions, while in a clinical setting it can provide physicians with more detailed patient activity information. This study describes the development and optimization process of a combined locomotion mode classifier and environmental parameter estimator using machine learning and wearable sensors. A detailed analysis of the best sensor types and placements for each problem is also presented to provide device designers with information on which sensors to prioritize for their application. For this study, 15 able-bodied subjects were unilaterally instrumented with inertial measurement unit, goniometer, and electromyography sensors and data were collected for extensive ranges of level-ground, ramp, and stair walking conditions. The proposed system classifies steady state ambulation modes with 99% accuracy and ambulation mode transitions with 96% accuracy, along with estimating ramp incline within 1.25 degrees, stair height within 1.29 centimeters, and walking speed within 0.04 meters per second. Mechanical sensors (inertial measurement units, goniometers) are found to be most important for classification, while goniometers dominate ramp incline and stair height estimation, and speed estimation is performed largely with a single inertial measurement unit. The feature tables and Matlab code to replicate the study are published as supplemental materials.

Automated gap-filling for marker-based biomechanical motion capture data.

Marker-based motion capture presents the problem of gaps, which are traditionally processed using motion capture software, requiring intensive manual input. We propose and study an automated method of gap-filling that uses inverse kinematics (IK) to close the loop of an iterative process to minimize error, while nearly eliminating user input. Comparing our method to manual gap-filling, we observe a 21% reduction in the worst-case gap-filling error (p < 0.05), and an 80% reduction in completion time (p < 0.01). Our contribution encompasses the release of an open-source repository of the method and interaction with OpenSim.

Biological Hip Torque Estimation using a Robotic Hip Exoskeleton.

Machine learning (ML) algorithms present an opportunity to estimate joint kinetics using a limited set of mechanical sensors. These estimates could be used as a continuous reference signal for exoskeleton control, able to modulate exoskeleton assistance in real-world environments. In this study, sagittal plane biological hip torque during level ground, incline and decline walking was calculated using inverse dynamics of human subject data. Subsequently, this torque was estimated using neural network (NN) and XGBoost ML models. Model inputs consisted solely of mechanical sensor data onboard a robotic hip exoskeleton. These results were compared to a baseline method of estimating hip torque as the mean torque profile during ambulation. On average across conditions, the NN and XGBoost models estimated biological hip torque with an RMSE of 0.116±0.015 and 0.108±0.011 Nm/kg, respectively, which was significantly less than the baseline estimation that had an RMSE of 0.300±0.145 Nm/kg (p<0.05). Fitting the baseline method to ambulation mode specific data significantly reduced overall RMSE by 59.3%; however, the ML models were still significantly better than the baseline method (p<0.05). These results show that machine learning algorithms can estimate biological hip torque using only mechanical sensors onboard a hip exoskeleton better than simply using an average torque profile. This suggests that these estimation models could be suitable for modulating exoskeleton assistance. Additionally, no evidence suggested the need to train separate ML models for each ambulation mode as estimation RMSE was not significantly different across unified and separated ML models.

Impedance Control Strategies for Enhancing Sloped and Level Walking Capabilities for Individuals with Transfemoral Amputation Using a Powered Multi-Joint Prosthesis.

INTRODUCTION: Powered prostheses are a promising new technology that may help people with lower-limb loss improve their ability to perform locomotion tasks. Developing active prostheses requires robust design methodologies and intelligent controllers to appropriately provide assistance to the user for varied tasks in different environments. The purpose of this study was to validate an impedance control strategy for a powered knee and ankle prosthesis using an embedded sensor suite of encoders and a six-axis load cell that would aid an individual in performing common locomotion tasks, such as level walking and ascending/descending slopes. MATERIALS AND METHODS: Three amputees walked on a treadmill and four amputees walked on a ramp circuit to test whether a dual powered knee and ankle prosthesis could generate appropriate device joint kinematics across users. RESULTS: Investigators found that tuning 2-3 subject-specific parameters per ambulation mode was necessary to render individualized assistance. Furthermore, the kinematic profiles demonstrate invariance to walking speeds ranging from 0.63 to 1.07 m/s and incline/decline angles ranging from 7.8° to 14°. CONCLUSION: This work presents a strategy that requires minimal tuning for a powered knee & ankle prosthesis that scales across a nominal range of both walking speeds and ramp slopes.

Feature Selection and Non-Linear Classifiers: Effects on Simultaneous Motion Recognition in Upper Limb.

Myoelectric signals are a standard input for volitional control of prosthetic devices. As an information-rich signal, feature selection plays a decisive role in the performance of motion classification. In this paper, we evaluate feature selection in the classification of simultaneous motions produced from combinations of wrist and elbow flexion/extension, radio-ulnar pronation/supination, and hand opening/closing aiming to determine a common set of recommendations for the implementation of motion classification from EMG signals for prosthetic control. Chow-Liu trees and forward feature selection are used as the methods for selecting features, and six different classification algorithms are evaluated as the wrapping component. We analyzed the performance of different linear and non-linear kernel algorithms in terms of the accuracy with respect to the feature selection, observing that feature selection was critical for improving accuracy levels to above 95%. Chow-Liu trees demonstrated to be a strategy that enables a combination of a low number of iterations with comparable accuracy to what is achieved with a forward selection search. In addition, we found a trend for waveform length and entropy as the most relevant types of features to consider and found evidence that simultaneous motion classification should be handled using non-linear classification approaches. Our study serves to improve the feature and algorithm selection for surface EMG signals in the classification of simultaneous motions generating a viable approach in the recognition of combinations of actions from the upper limb.

Experiencia con colgajos toracoabdominales en reconstrucción mamaria. Clasificación personal y variantes / Experience with thoracoabdominal flaps in breast reconstruction. Personal classification and variants

INTRODUCCIÓN Y OBJETIVO: Los colgajos toracoabdominales permiten el cierre de defectos torácicos con una menor morbilidad y dificultad técnica respecto a los colgajos a distancia. En cirugía recontructiva oncológica mamaria se utilizaron primariamente como colgajos fasciocutáneos para pacientes con cáncer de mama localmente avanzado (T3 y T4) que requieren grandes resecciones de piel y necesitan una cubierta rápida y eficaz del defecto torácico. En los últimos años han resurgido como opción terapéutica para solucionar complicaciones postquirúrgicas por isquemia y necrosis cutánea con exposición de expansores o implantes mamarios. El aumento de la mastectomía reductora de riesgo ha llevado a zonas de necrosis cutáneas pequeñas que requieren colgajos de vecindad para solucionar la complicación. Es en esta última indicación donde estos colgajos tienen una aplicación óptima. MATERIAL Y MÉTODO: Describimos el colgajo toracoabdominal como una técnica reconstructiva de gran utilidad para cubrir defectos cutáneos en cirugía mamaria y presentamos una clasificación útil para clarificar sus indicaciones, extraída de nuestra experiencia con dichos colgajos. RESULTADOS: Elaboramos una clasificación didáctica de los colgajos toracoabdominales, presentamos un caso clínico de cada modelo de colgajo, y recopilamos nuestra casuística. CONCLUSIONES: Destacamos el uso específico en complicaciones de cirugías de reconstrucción mamaria de los colgajos toracoabdominales como alternativa que aporta tejidos de igual coloración, fácil de realizar, con cierre directo de la zona donante por lo general y con un pedículo vascular fiable. Además, aportamos una clasificación propia

BACKGROUND AND OBJECTIVE: The thoracoabdominal flap allows the closure of thoracic defects with a lower morbidity and technical difficulty compared to remote flaps. In mammary oncological reconstructive surgery, they were primarily used as fasciocutaneous flaps for patients with locally advanced breast cancer (T3 and T4) that require large skin resections and a fast and effective coverage of the thoracic defect. In recent years they have resurged as a therapeutic option to solve postoperative complications due to ischemia and skin necrosis with exposure of expanders or breast implants.The increase in the practice of risk-reducing mastectomy has led to areas of small skin necrosis that require local flaps to solve the complication. It is in this last indication where these flaps have an optimal application. METHODS: In this study, the thoracoabdominal flap is presented as a very useful reconstructive technique to cover skin defects in breast surgery. A classification is presented in order to clarify these fasciocutaneous flaps as well as our experience with theme. RESULTS: We elaborate a didactic classification of thoracoabdominal flaps, presenting a clinical case of each flap model, and compiling our casuistry. CONCLUSIONS: We highlight the specific use in complications of breast reconstruction surgeries of thoracoabdominal flaps as an alternative that provides tissues of the same color, easy to perform, with direct closure of the donor area and with a reliable vascular pedicle. In addition, we provide our own classification

Colgajo doble banderín de región retroauricular para reconstrucción del hélix / Double flag flap in the retroauricular region for helix reconstruction

El objetivo del presente artículo es mostrar los resultados de la reconstrucción de los defectos de la superficie del borde del hélix utilizando el colgajo doble banderín de región retroauricular. Se describen los pedículos vasculares del colgajo en disecciones anatómicas. Se presentan detalles técnicos del procedimiento, así como se muestran casos clínicos, analizando resultados y evaluando los mismos en 30 casos

The objective of this article is to show the results of the reconstruction of the defects of the surface of the helix edge using the double flag flap of the retroauricular region. The vascular pedicles of the flap are described in anatomical dissections. Technical details of the procedure are presented, as well as clinical cases are shown, analyzing results and evaluating them in 30 cases.

Liposucción asistida por ultrasonido para tratamiento de la ginecomastia: experiencia en los últimos 5 años / Ultrasound-assisted liposuction in gynecomastia management: last 5-year experience

Antecedentes y Objetivo. La liposucción asistida por ultrasonido quirúrgico (USAL por sus siglas en inglés) se utiliza ampliamente en el tratamiento de las lipodistrofias corporales. En la ginecomastia, está demostrado que la combinación de ultrasonido y liposucción presenta mayor efectividad en el resultado final. Queremos dar a conocer la experiencia de nuestro Servicio en el abordaje de esta patología implementando la USAL como tratamiento único y como medida terapéutica coadyuvante de la cirugía abierta. Material y Método. Estudio descriptivo, de cohorte única, durante el segundo semestre del 2017 con registros de todos los pacientes tratados por el Servicio de Cirugía Plástica del Sanatorio Los Arroyos y Hospital Privado de Rosario (Rosario, Argentina) entre 2011-2016. En total, 43 pacientes con edades comprendidas entre 16 y 70 años evaluados retrospectivamente y estratificados según el método de corrección quirúrgica en 2 grupos. Grupo 1 (n=26) tratamiento exclusivo con USAL y grupo 2 (n=17) USAL combinada con resección abierta de tejido glandular mamario. Analizamos el tiempo de recuperación postquirúrgica, las complicaciones y la conformidad subjetiva de los pacientes. Resultados. Ninguno de los casos presentó complicaciones que requirieran intervención adicional. Como complicación menor hubo 4 casos de equimosis torácica importante (2 en cada grupo). Todos presentaron adecuada retracción cutánea y conformidad con el resultado a los 30 días. Comparativamente, encontramos amplia diferencia en el tiempo de reintegro a las actividades cotidianas tras la intervención: media de 7 días para el grupo 1 frente a 11 días para el grupo 2. Conclusiones. La USAL es una importante herramienta terapéutica en ginecomastia, de especial relevancia en casos de predominio graso dado que por si sola es un tratamiento efectivo en estos casos. En los casos de ginecomastia mixta que requieren resección quirúrgica, la USAL es una importante medida coadyuvante para un resultado postquirúrgico óptimo con adecuada retracción cutánea y mínimas complicaciones

Background and Objective. Surgical Ultrasound assisted liposuction (USAL) is widely used in the management of body lipodystrophies. In gynecomastia, it has been reported that thus combination of ultrasound and liposuction has proven an improved effectiveness and better outcomes. We present our experience in the management of this pathology using USAL exclusively and as adjuvant measure of conventional open surgery. Methods.A descriptive and single cohort study was performed during the second semester of 2017 with patients’ records from the Plastic Surgery Department of Sanatorio Los Arroyos and Hospital Privado de Rosario (Rosario, Argentina), treated between 2011-2016. A total of 43 patients diagnosed with gynecomastia, with ages between 16 and 70 years, retrospectively assessed and stratified into 2 groups according the therapeutical approach. Group 1, treated with USAL exclusively (n=26) and group 2, treated with combined USAL and open mammary glandular tissue excision surgery (n=17). Postoperative downtime, complications and patient’s satisfaction were assessed. Results. None had any major complications requiring additional measures. The only reported minor complication was significant thoracic bruising in 4 patients (2 in each group). All patients showed adequate skin retraction and 30 days post-operative results satisfaction. We found wide differences when compared the reported postoperative downtime, where group 1 reported a mean of 7 downtime days versus 11 downtime days for group 2. Conclusions. USAL is an important therapeutical measure when treating patients diagnosed with gynecomastia. It’s especially useful when treating gynecomastia with fatty dominance, being effective by itself. Additionally, when treating patients with significant glandular tissue which needs surgical excision (mixed gynecomastia), USAL seems to be an useful adjuvant measure to achieve an outstanding outcome with optimal skin retraction and minimal complication rates

Colgajo de Limberg: variantes y aplicaciones / Limberg´s fl aps: variants and applications

La piel es asiento frecuente de injurias en especial traumáticas o neoplásicas, surgiendo la necesidad de la reconstrucción de la brecha quirúrgica. Existen múltiples opciones de reconstrucción y su uso dependerá del tamaño del defecto quirúrgico a reparar y de la experiencia y preferencia del cirujano tratante. Presentaremos al colgajo romboidal de Limberg y sus variantes como una técnica reconstructiva de gran utilidad para cubrir defectos cutáneos en cara y otras partes del cuerpo respetando la función y estética de la región.

The skin is a frequent seat of injuries especially traumatic or neoplastic, arising the need for reconstruction of the surgical gap. There are multiple reconstruction options and their use will depend on the size of the surgical defect to be repaired and on the experience and preference of the treating surgeon. We will present the rhombus Limberg fl ap and its variants as a reconstructive technique of great utility to cover skin defects in the face and other parts of the body respecting the function and aesthetics of the region.