Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks

OBJECTIVE

Volitional control systems for powered prostheses require the detection of user intent to operate in real life scenarios. Ambulation mode classification has been proposed to address this issue. However, these approaches introduce discrete labels to the otherwise continuous task that is ambulation. An alternative approach is to provide users with direct, voluntary control of the powered prosthesis motion. Surface electromyography (EMG) sensors have been proposed for this task, but poor signal-to-noise ratios and crosstalk from neighboring muscles limit performance. B-mode ultrasound can address some of these issues at the cost of reduced clinical viability due to the substantial increase in size, weight, and cost. Thus, there is an unmet need for a lightweight, portable neural system that can effectively detect the movement intention of individuals with lower-limb amputation.

METHODS

In this study, we show that a small and lightweight A-mode ultrasound system can continuously predict prosthesis joint kinematics in seven individuals with transfemoral amputation across different ambulation tasks. Features from the A-mode ultrasound signals were mapped to the user's prosthesis kinematics via an artificial neural network.

RESULTS

Predictions on testing ambulation circuit trials resulted in a mean normalized RMSE across different ambulation modes of 8.7 ± 3.1%, 4.6 ± 2.5%, 7.2 ± 1.8%, and 4.6 ± 2.4% for knee position, knee velocity, ankle position, and ankle velocity, respectively.

CONCLUSION AND SIGNIFICANCE

This study lays the foundation for future applications of A-mode ultrasound for volitional control of powered prostheses during a variety of daily ambulation tasks.

Model Predictions that Consider Individualized Gait Patterns and Patient Mobility Level for the Use of Passive Hip-Flexion Exosuits by Persons with Unilateral Transfemoral Amputation

The muscular remodeling that occurs during a transfemoral amputation surgery and subsequent long-term use of mechanically-passive prostheses have significant impacts on the mobility and gait pattern of the patient. At toe-off and during the subsequent swing phase, this behavior is characterized by increased hip flexion moment and power provided by the biological limb. In other patient populations (e.g., individuals with multiple sclerosis) passive tension-generating assistive elements have been shown to restore altered hip flexion mechanics at toe off. We hypothesized that an exosuit of the same basic architecture could be well applied to individuals with transfemoral amputation. In this paper, we simulate the effects of such a device for 18 patients of K2 and K3 Medicare functional classification levels. The device consists of two parallel elastic bands. Our approach considers the wrapping and geometric behavior of these elements over the residual limb in full-body patient-specific kinematic simulations of level ground walking. A nonlinear least squares problem was solved via the Levenberg-Marquardt method to find the band properties that best match (in order to offset) the intrinsic power delivery of the muscles during the swing phase. We found higher mobility patients (K3) often require a stiffer device, which leads to a greater error in the kinetic match between the biological limb and exosuit. In contrast, this method appears to be effective for K2 patients, which suggests that a different means of parameter selection or power delivery (e.g., active devices) may be necessary for higher mobility levels.

User-Centered Configuration of Soft Hip Flexion Exosuit Designs to Assist Individuals with Multiple Sclerosis Through Simulated Human-in-the-Loop Optimization

Soft exosuits hold promise as assistive technology for people with gait deficits owing to a variety of causes. A key aspect of providing useful assistance is to keep the human user at the center of all considerations made in the design, configuration, and prescribed use of an assistive device. This work details a method for informing the configuration of a soft hip flexion exosuit by 1) modeling the user's shape and movements in order to simulate the mechanical interaction of the exosuit and user, 2) incorporating the mechanical effects of the exosuit into a muscle-driven musculoskeletal gait simulation, and 3) using the results of these simulations to define a cost function that is minimized via Bayesian optimization. This process is carried out for models of four different people with multiple sclerosis, and the final optimized configurations for each subject are compared. For all users, the estimated metabolic cost of transport was reduced below baseline, no-device levels. This work represents a step toward more individualized, user-centric modeling of assistive devices, and demonstrates a system for informing the physical configuration of an exosuit on a case-by-case basis using real patient data.

Relationships between self-perceived and clinical expression of pain and function differ based on the underlying pathology of the human hip

BACKGROUND

Patient-reported outcomes are commonly used to assess patient symptoms. The effect of specific hip pathology on relationships between perceived and objectively measured symptoms remains unclear. The purpose of this study was to evaluate differences of function and pain in patients with FAIS and DDH, to assess the correlation between perceived and objective function, and to determine the influence of pain on measures of function.

METHODS

This prospective cross-sectional study included 35 pre-operative patients (60% female) with femoroacetabular impingement syndrome (FAIS) and 37 pre-operative patients (92% female) with developmental dysplasia of the hip (DDH). Objectively measured function (6-min walk [6MWT], single leg hop [SLHT], Biodex sway [BST], hip abduction strength [HABST], and STAR excursion balance reach [STAR] tests), patient-reported function (UCLA Activity, Hip Outcome Score [HOS], Short Form 12 [SF-12], and Hip Disability and Osteoarthritis Outcome Score [HOOS]), and patient-reported pain (HOOS Pain, visual analogue scale (VAS), and a pain location scale) were collected during a pre-surgical clinic visit. Between-group comparisons of patient scores were performed using Wilcoxon Rank-Sum tests. Within-group correlations were analyzed using Spearman's rank correlation coefficients. Statistical correlation strength was defined as low (r =  ± 0.1-0.3), moderate (r =  ± 0.3-0.5) and strong (r >  ± 0.5).

RESULTS

Patients with DDH reported greater pain and lower function compared to patients with FAIS. 6MWT distance was moderately-to-strongly correlated with a number of patient-reported measures of function (FAIS: r = 0.37 to 0.62, DDH: r = 0.36 to 0.55). Additionally, in patients with DDH, SLHT distance was well correlated with patient reported function (r = 0.37 to 0.60). Correlations between patient-reported pain and objectively measured function were sparse in both patient groups. In patients with FAIS, only 6MWT distance and HOOS Pain (r = -0.53) were significantly correlated. In patients with DDH, 6MWT distance was significantly correlated with VAS Average (r = -0.52) and Best (r = -0.53) pain.

CONCLUSION

Pain is greater and function is lower in patients with DDH compared to patients with FAIS. Moreover, the relationship between pain and function differs between patient groups. Understanding these differences is valuable for informing treatment decisions. We recommend these insights be incorporated within the clinical continuum of care, particularly during evaluation and selection of surgical and therapeutic interventions.

Shaping Human Movement via Bimanually-Dependent Haptic Force Feedback

Haptic feedback can enhance training and performance of human operators; however, the design of haptic feedback for bimanual coordination in robot-assisted tasks (e.g., control of surgical robots) remains an open problem. In this study, we present four bimanually-dependent haptic force feedback conditions aimed at shaping bimanual movement according to geometric characteristics: the number of targets, direction, and symmetry. Haptic conditions include a virtual spring, damper, combination spring-damper, and dual springs placed between the hands. We evaluate the effects of these haptic conditions on trajectory shape, smoothness, and speed. We hypothesized that for subjects who perform worse with no haptic feedback (1) a spring will improve the shape of parallel trajectories, (2) a damper will improve the shape of point symmetric trajectories, (3) dual springs will improve the shape of trajectories with one target, and (4) a damper will improve smoothness for all trajectories. Hypotheses (1) and (2) were statistically supported at the p < 0.001 level, but hypotheses (3) and (4) were not supported. Moreover, bimanually-dependent haptic feedback tended to improve shape accuracy for movements that subjects performed worse on under no haptic condition. Thus, bimanual haptic feedback based on geometric trajectory characteristics shows promise to improve performance in robot-assisted motor tasks.

There are unique kinematics during locomotor transitions between level ground and stair ambulation that persist with increasing stair grade

Human ambulation is typically characterized during steady-state isolated tasks (e.g., walking, running, stair ambulation). However, general human locomotion comprises continuous adaptation to the varied terrains encountered during activities of daily life. To fill an important gap in knowledge that may lead to improved therapeutic and device interventions for mobility-impaired individuals, it is vital to identify how the mechanics of individuals change as they transition between different ambulatory tasks, and as they encounter terrains of differing severity. In this work, we study lower-limb joint kinematics during the transitions between level walking and stair ascent and descent over a range of stair inclination angles. Using statistical parametric mapping, we identify where and when the kinematics of transitions are unique from the adjacent steady-state tasks. Results show unique transition kinematics primarily in the swing phase, which are sensitive to stair inclination. We also train Gaussian process regression models for each joint to predict joint angles given the gait phase, stair inclination, and ambulation context (transition type, ascent/descent), demonstrating a mathematical modeling approach that successfully incorporates terrain transitions and severity. The results of this work further our understanding of transitory human biomechanics and motivate the incorporation of transition-specific control models into mobility-assistive technology.

A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics Via an Artificial Neural Network

Lower-limb powered prostheses can provide users with volitional control of ambulation. To accomplish this goal, they require a sensing modality that reliably interprets user intention to move. Surface electromyography (EMG) has been previously proposed to measure muscle excitation and provide volitional control to upper- and lower-limb powered prosthesis users. Unfortunately, EMG suffers from a low signal to noise ratio and crosstalk between neighboring muscles, often limiting the performance of EMG-based controllers. Ultrasound has been shown to have better resolution and specificity than surface EMG. However, this technology has yet to be integrated into lower-limb prostheses. Here we show that A-mode ultrasound sensing can reliably predict the prosthesis walking kinematics of individuals with a transfemoral amputation. Ultrasound features from the residual limb of 9 transfemoral amputee subjects were recorded with A-mode ultrasound during walking with their passive prosthesis. The ultrasound features were mapped to joint kinematics through a regression neural network. Testing of the trained model against untrained kinematics from an altered walking speed show accurate predictions of knee position, knee velocity, ankle position, and ankle velocity, with a normalized RMSE of 9.0 ± 3.1%, 7.3 ± 1.6%, 8.3 ± 2.3%, and 10.0 ± 2.5% respectively. This ultrasound-based prediction suggests that A-mode ultrasound is a viable sensing technology for recognizing user intent. This study is the first necessary step towards implementation of volitional prosthesis controller based on A-mode ultrasound for individuals with transfemoral amputation.

Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks

Many people struggle with mobility impairments due to lower limb amputations. To participate in society, they need to be able to walk on a wide variety of terrains, such as stairs, ramps, and level ground. Current lower limb powered prostheses require different control strategies for varying ambulation modes, and use data from mechanical sensors within the prosthesis to determine which ambulation mode the user is in. However, it can be challenging to distinguish between ambulation modes. Efforts have been made to improve classification accuracy by adding electromyography information, but this requires a large number of sensors, has a low signal-to-noise ratio, and cannot distinguish between superficial and deep muscle activations. An alternative sensing modality, A-mode ultrasound, can detect and distinguish between changes in superficial and deep muscles. It has also shown promising results in upper limb gesture classification. Despite these advantages, A-mode ultrasound has yet to be employed for lower limb activity classification. Here we show that A- mode ultrasound can classify ambulation mode with comparable, and in some cases, superior accuracy to mechanical sensing. In this study, seven transfemoral amputee subjects walked on an ambulation circuit while wearing A-mode ultrasound transducers, IMU sensors, and their passive prosthesis. The circuit consisted of sitting, standing, level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent, and a spatial-temporal convolutional network was trained to continuously classify these seven activities. Offline continuous classification with A-mode ultrasound alone was able to achieve an accuracy of 91.8±3.4%, compared with 93.8±3.0%, when using kinematic data alone. Combined kinematic and ultrasound produced 95.8±2.3% accuracy. This suggests that A-mode ultrasound provides additional useful information about the user's gait beyond what is provided by mechanical sensors, and that it may be able to improve ambulation mode classification. By incorporating these sensors into powered prostheses, users may enjoy higher reliability for their prostheses, and more seamless transitions between ambulation modes.

Biomechanical mechanism of peak braking force modulation during increased walking speed in healthy young adults

Walking speed is an important indicator of health and function across a variety of populations. Faster walking requires both larger propulsive and braking forces, thoughof the two, propulsive force generation has been far more extensively investigated. This study seeks to develop and validatea quasi-static biomechanical model of braking forcein healthy individualsacrossself-selected and fast walking speeds. Additionally, the model was used to quantify the relative contribution of knee extension torque versus leading limb angle (LLA) to changes in braking force across walking speeds. Kinetic and kinematic data from 44 young healthy participants walking overground at 2 different speeds were analyzed. The model prediction correlated strongly with actual braking force production at the self-selected speed (r = 0.9; p < 0.01), the fast speed (r = 0.97; p < 0.01) andthe change between speeds (r = 0.95, p < 0.01). On average, increases in knee extension torque and the LLA contributed 132 % and 12 %, respectively, to increases in peak braking force (PBF). Increases in the external lever arm length operated to reduce predicted braking force by 56 %. The results highlight the importance of rapid eccentric contraction of the knee extensors during braking force modulation in healthy gait.

Relating Underlying Performance Objectives of Overground Walking to Observable Walking Mechanics using Predictive Musculoskeletal Simulations

There exists motor redundancy during human gait that allows individuals to perform the same task in different observable ways (i.e., with varied styles). However, how differences in observable walking mechanics depend on unique and underlying biomechanical objectives is unclear. As an example, these objectives could include metabolic energy consumption, sum of muscle activations, limb mechanical loading, balance and combinations thereof. In this study, we develop predictive neuromuscular simulations to investigate the relationships between these biomechanical objectives and observable mechanics during level walking. We simulated 3D normal walking of five healthy subjects, while optimizing each of the aforementioned objectives-resulting in 25 forward dynamics simulations for analysis. We compared the resulting joint kinematics and moments of different simulations. One of main findings suggests that decreased hip abduction angle is tightly related to when the regulation of dynamic balance (computed as whole-body angular momentum) is included in a movement cost function. We also find that increased joint moments are related to including metabolic cost (i.e., objectives associated with improving the energy economy of movement). Further, the timing of joint kinematics is adjusted for different performance objectives. These findings could guide the development of rehabilitation training and assistive devices that target specific individuals, tasks, and specific styles of movement.

Modeling the Influence of the Human Form and Ambulation Context on Moment- and Power-Generating Abilities of Soft Hip-Flexion Exosuits

Exosuits are close-fitting devices, which are meant to be worn without restricting the motion of the user in the way that a rigid device would. These soft devices augment lower-limb biomechanics by using flexible, joint-spanning linear elements that are actuated to create moments about the spanned joints, effectively using the human body as the mechanical transmission from input to output. Consequently, the size of the moment arm that an exosuit creates about a given joint is dependent on the size and shape of the user, as well as their individualized gait patterns that depend on the terrain they are negotiating. These highly-variable human and environmental factors affect the performance of all soft exosuits (both passive and active), and the ability to quantify these effects would benefit assistive device development. In this work, we present a system for modeling the effects of user body mass index, biological sex, and gait kinematics on task-dependent exosuit performance. We use this system to estimate the performance of a hip-flexion exosuit over a range of body shapes obtained from a database of 3D human surface models, and with gait kinematics from physical experiments. Our results demonstrate that the user's body mass index, sex, and gait kinematics are necessary factors to consider when designing an exosuit for personalized assistance. This type of analysis can allow device developers to account for the unique shape and gait patterns of individuals, either in generating new designs, developing online control algorithms, or in configuring devices for specific individuals.

Evaluating Electromyography and Sonomyography Sensor Fusion to Estimate Lower-Limb Kinematics Using Gaussian Process Regression

Research on robotic lower-limb assistive devices over the past decade has generated autonomous, multiple degree-of-freedom devices to augment human performance during a variety of scenarios. However, the increase in capabilities of these devices is met with an increase in the complexity of the overall control problem and requirement for an accurate and robust sensing modality for intent recognition. Due to its ability to precede changes in motion, surface electromyography (EMG) is widely studied as a peripheral sensing modality for capturing features of muscle activity as an input for control of powered assistive devices. In order to capture features that contribute to muscle contraction and joint motion beyond muscle activity of superficial muscles, researchers have introduced sonomyography, or real-time dynamic ultrasound imaging of skeletal muscle. However, the ability of these sonomyography features to continuously predict multiple lower-limb joint kinematics during widely varying ambulation tasks, and their potential as an input for powered multiple degree-of-freedom lower-limb assistive devices is unknown. The objective of this research is to evaluate surface EMG and sonomyography, as well as the fusion of features from both sensing modalities, as inputs to Gaussian process regression models for the continuous estimation of hip, knee and ankle angle and velocity during level walking, stair ascent/descent and ramp ascent/descent ambulation. Gaussian process regression is a Bayesian nonlinear regression model that has been introduced as an alternative to musculoskeletal model-based techniques. In this study, time-intensity features of sonomyography on both the anterior and posterior thigh along with time-domain features of surface EMG from eight muscles on the lower-limb were used to train and test subject-dependent and task-invariant Gaussian process regression models for the continuous estimation of hip, knee and ankle motion. Overall, anterior sonomyography sensor fusion with surface EMG significantly improved estimation of hip, knee and ankle motion for all ambulation tasks (level ground, stair and ramp ambulation) in comparison to surface EMG alone. Additionally, anterior sonomyography alone significantly improved errors at the hip and knee for most tasks compared to surface EMG. These findings help inform the implementation and integration of volitional control strategies for robotic assistive technologies.

Performance of Sonomyographic and Electromyographic Sensing for Continuous Estimation of Joint Torque During Ambulation on Multiple Terrains

Advances in powered assistive device technology, including the ability to provide net mechanical power to multiple joints within a single device, have the potential to dramatically improve the mobility and restore independence to their users. However, these devices rely on the ability of their users to continuously control multiple powered lower-limb joints simultaneously. Success of such approaches rely on robust sensing of user intent and accurate mapping to device control parameters. Here, we compare two non-invasive sensing modalities: surface electromyography and sonomyography, (i.e., ultrasound imaging of skeletal muscle), as inputs to Gaussian process regression models trained to estimate hip, knee and ankle joint moments during varying forms of ambulation. Experiments were performed with ten non-disabled individuals instrumented with surface electromyography and sonomyography sensors while completing trials of level, incline (10°) and decline (10°) walking. Results suggest sonomyography of muscles on the anterior and posterior thigh can be used to estimate hip, knee and ankle joint moments more accurately than surface electromyography. Furthermore, these results can be achieved by training Gaussian process regression models in a task-independent manner; i.e., incorporating features of level and ramp walking within the same predictive framework. These findings support the integration of sonomyographic and electromyographic sensing within powered assistive devices to continuously control joint torque.

Whole-body and Segmental Contributions to Dynamic Balance in Stair Ambulation are Sensitive to Early-Stage Parkinson's Disease. Annu Int Conf IEEE Eng Med Biol Soc 2021;2021:6441-6444. [PMID: 34892586 DOI: 10.1109/embc46164.2021.9630416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Stair ambulation is commonplace in daily living activities, yet biomechanically more challenging compared to level-ground walking. With reduced lower-limb muscle strength and increased rigidity of extremities, people with Parkinson's disease (PD) experience impaired balance and higher incidence of falls each year. However, the regulation of whole-body dynamic balance of individuals with PD in stair walking is unclear. Whole-body angular momentum (H) is a useful metric for assessing dynamic balance that accounts for the angular movements of all body segments about the body center-of-mass (COM). In this study we investigated the regulation of H and segmental contributions to H during stair ascent and descent walking in individuals with PD compared to healthy subjects. During stair descent, the magnitude of sagittal-plane H increased in participants with PD compared to healthy subjects in ipsilateral (most affected side) leg stance. Meanwhile, the legs contributed more to sagittal-plane H in individuals with PD compared to healthy subjects. During stair descent walking, the magnitude of transverse-plane H was also greater in participants with PD compared to healthy subjects during the second half of ipsilateral leg gait cycle. The increased magnitude of negative (i.e., forward) sagittal-plane H in the ipsilateral stance of stair descent walking suggests that individuals with PD experience greater difficulties maintaining their forward rotation during such tasks.

Femur Abduction Associated with Transfemoral Amputation Alters the Profile of Lumbopelvic Mechanical Loads During Generalized End-Limb Loading

Pain in the lower back is frequent problem for most individuals with transfemoral amputation, which limits their overall mobility and quality of life. While the underlying root causes of back pain are multifactorial, a contributing factor is the mechanical loading environment within the lumbopelvic joint. Specifically, this study aims to explore the upstream effects amputation has on the mechanical loading environment of the lumbopelvic joint using a 3D musculoskeletal model of transfemoral amputation. A generic musculoskeletal model was altered to represent a transfemoral amputation. Muscle parameters were adjusted to represent a myodesis amputation surgery that preserved musculotendon tension in a neutral anatomical pose. The model contained a total of 28 degrees of freedom and 76 muscles spanning the lower-limb and torso. In forward dynamics simulations, generalized external forces were applied to the distal end of the residual limb at a series of directions. Axial, oblique and transverse 10 N end-limb loads were applied. In addition, simulations were performed for 0°, 4°, and 8° of femur abduction, which are clinically observed in individuals with transfemoral amputation. In these simulations, reaction forces and moments at the lumbopelvic joint were computed. In general, femur abduction had little effect on back loading for an axial applied end-limb force. These data showed that while the individual magnitudes of lumbopelvic force and moment reactions did not significantly deviate for differing levels of femur abduction, the pattern of how these forces changes in response to different end-limb force directions (applied circumferentially along the limb) was affected by femur abduction angle.Clinical Relevance- The changes in joint reaction forces in the lumbopelvic joint from an aligned position to an abducted position reinforce the importance of avoiding hip flexion-abduction contracture during amputation surgery. This suggests that surgical techniques such as myodesis, osseointegration, or medial thighplasty, which intend to maintain anatomical alignment may have beneficial upstream effects for the patients during locomotion. Given the prevalence of lower back pain in individuals with transfemoral amputation, teasing out the causes of lower back pain could bring relief to a population that struggles with community independence.

A Predictive Framework to Provide Neuromuscular Insights in Reshaping Dynamic Balance during Transient Locomotion

Anticipated and unanticipated directional changes are commonplace in daily lives. The need for dynamic balance is amplified when these transitions are performed in an unplanned (i.e., unanticipated) manner. In this study, we used predictive simulations and optimal control constructs to test a method for reshaping dynamic balance of unanticipated crossover cuts. We also compare how such improvements can be mediated at the musculotendon level. Our study shows that the performance of unanticipated crossover cuts can be optimized to improve dynamic balance, and highlight the potential for predictive simulations and optimal control to provide quantitative targets for reshaping dynamic balance in unanticipated crossover cuts-targets which are biologically-feasible.Clinical Relevance-This approach could inform task-specific rehabilitation therapy by suggesting how to reshape an individual's dynamic balance and which joint-level kinematic adjustments and muscle groups would be optimal to engage in doing so.

A Computational Framework based on Medical Imaging and Random Sampling to Guide Optimal Residual Limb Designs for Individuals with Transfemoral Limb Loss

The purpose of this study was to understand how the form (i.e., shape and presence of underlying soft tissue) of residual limb tissue influences limb function and comfort for individuals with transfemoral limb loss. Specifically, there exist surgical techniques that are frequently applied to the lower limbs of individuals to reduce an excessive soft tissue envelope. However, the clinical goals are frequently from a cosmetic perspective and are applied most commonly to individuals who are obese and not necessarily those with limb loss. For specific individuals with transfemoral limb loss, there likely exist limb shapes and distributions of underlying soft tissue that more optimally engage with lower-limb prostheses. Based on recent experimental findings, optimizing the limb and its physical connection to lower-limb prostheses, may have equivalent if not greater impact on user outcomes than selection of prosthetic components. This study develops and tests a method for informing optimal designs of the residual limb for individuals with transfemoral amputation. The framework uses patient-specific MRI images of an individual's residual limb, and within a mechanical modeling framework applies Latin hypercube sampling to investigate which portions of the underlying limb tissue most positively affect mechanical objectives associated with limb function and comfort. These theoretical results predicted from this system aimed to inform optimal limb designs were then compared to a currently used surgical method known as medial thighplasty, which was previously applied in one patient, to assess agreement. These simulations showed that the regions of the limb most contributing negatively to the objective function were located at the distal end of the limb and were far from muscle tissue (i.e., were mostly superficial). These findings suggest that limb techniques which seek to produce residual limbs that are most slim at their medial and distal end are beneficial and may lead to improved fit and function of lower-limb prostheses.Clinical Relevance-Prosthetic technology advancement within the last decade has heightened the hopes of individuals with amputation. However, how these devices integrate to their human users is non-trivial and can curtail these advancements. Tools are needed to inform how residual limb itself can be optimized to better integrate with prostheses.

Ultrasound-Derived Features of Muscle Architecture Provide Unique Temporal Characterization of Volitional Knee Motion

Sonomyography, or dynamic ultrasound imaging of skeletal muscle, has gained significant interest in rehabilitation medicine. Previously, correlations relating sonomyography features of muscle contraction, including muscle thickness, pennation angle, angle between aponeuroses and fascicle length, to muscle force production, strength and joint motion have been established. Additionally, relationships between grayscale image intensity, or echogenicity, with maximum voluntary isometric contraction of muscle have been noted. However, the time relationship between changes in various sonomyography features during volitional motion has yet to be explored, which would highlight if unique information pertaining to muscle contraction and motion can be obtained from this real-time imaging modality. These new insights could inform how we assess muscle function and/or how we use this modality for assistive device control. Thus, our objective was to characterize the time synchronization of changes in five features of rectus femoris contraction extracted from ultrasound images during seated knee extension and flexion. A cross-correlation analysis was performed on data recorded by a handheld ultrasound system as able-bodied subjects completed seated trials of volitional knee extension and flexion. Changes in muscle thickness, angle between aponeuroses, and mean image echogenicity, a change in brightness of the grayscale image, preceded changes in our estimates of pennation angle and fascicle length. The leading nature of these features suggest they could be objective features for early detection of impending joint motion. Finally, multiple sonomyographic features provided unique temporal information associated with this volitional task.Clinical Relevance-This work evaluates the time relationship between five commonly reported features of skeletal muscle architecture during volitional motion, which can be used for targeted clinical assessments and intent detection.

Lower-limb kinematics and kinetics during continuously varying human locomotion

Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. This data paper reports a new dataset that includes the lower-limb kinematics and kinetics of ten able-bodied participants walking at multiple inclines (±0°; 5° and 10°) and speeds (0.8 m/s; 1 m/s; 1.2 m/s), running at multiple speeds (1.8 m/s; 2 m/s; 2.2 m/s and 2.4 m/s), walking and running with constant acceleration (±0.2; 0.5), and stair ascent/descent with multiple stair inclines (20°; 25°; 30° and 35°). This dataset also includes sit-stand transitions, walk-run transitions, and walk-stairs transitions. Data were recorded by a Vicon motion capture system and, for applicable tasks, a Bertec instrumented treadmill.

Biomechanical analysis of an unpowered hip flexion orthosis on individuals with and without multiple sclerosis

BACKGROUND

Gait impairment is a common complication of multiple sclerosis (MS). Gait limitations such as limited hip flexion, foot drop, and knee hyperextension often require external devices like crutches, canes, and orthoses. The effects of mobility-assistive technologies (MATs) prescribed to people with MS are not well understood, and current devices do not cater to the specific needs of these individuals. To address this, a passive unilateral hip flexion-assisting orthosis (HFO) was developed that uses resistance bands spanning the hip joint to redirect energy in the gait cycle. The purpose of this study was to investigate the short-term effects of the HFO on gait mechanics and muscle activation for people with and without MS. We hypothesized that (1) hip flexion would increase in the limb wearing the device, and (2) that muscle activity would increase in hip extensors, and decrease in hip flexors and plantar flexors.

METHODS

Five healthy subjects and five subjects with MS walked for minute-long sessions with the device using three different levels of band stiffness. We analyzed peak hip flexion and extension angles, lower limb joint work, and muscle activity in eight muscles on the lower limbs and trunk. Single-subjects analysis was used due to inter-subject variability.

RESULTS

For subjects with MS, the HFO caused an increase in peak hip flexion angle and a decrease in peak hip extension angle, confirming our first hypothesis. Healthy subjects showed less pronounced kinematic changes when using the device. Power generated at the hip was increased in most subjects while using the HFO. The second hypothesis was not confirmed, as muscle activity showed inconsistent results, however several subjects demonstrated increased hip extensor and trunk muscle activity with the HFO.

CONCLUSIONS

This exploratory study showed that the HFO was well-tolerated by healthy subjects and subjects with MS, and that it promoted more normative kinematics at the hip for those with MS. Future studies with longer exposure to the HFO and personalized assistance parameters are needed to understand the efficacy of the HFO for mobility assistance and rehabilitation for people with MS.

Continuous Classification of Locomotion in Response to Task Complexity and Anticipatory State

Objective

Intent recognition in lower-extremity assistive devices (e.g., prostheses and exoskeletons) is typically limited to either recognition of steady-state locomotion or changes of terrain (e.g., level ground to stair) occurring in a straight-line path and under anticipated condition. Stability is highly affected during non-steady changes of direction such as cuts especially when they are unanticipated, posing high risk of fall-related injuries. Here, we studied the influence of changes of direction and user anticipation on task recognition, and accordingly introduced classification schemes accommodating such effects.

Methods

A linear discriminant analysis (LDA) classifier continuously classified straight-line walking, sidestep/crossover cuts (single transitions), and cuts-to-stair locomotion (mixed transitions) performed under varied task anticipatory conditions. Training paradigms with varying levels of anticipated/unanticipated exposures and analysis windows of size 100–600 ms were examined.

Results

More accurate classification of anticipated relative to unanticipated tasks was observed. Including bouts of target task in the training data was necessary to improve generalization to unanticipated locomotion. Only up to two bouts of target task were sufficient to reduce errors to <20% in unanticipated mixed transitions, whereas, in single transitions and straight walking, substantial unanticipated information (i.e., five bouts) was necessary to achieve similar outcomes. Window size modifications did not have a significant influence on classification performance.

Conclusion

Adjusting the training paradigm helps to achieve classification schemes capable of adapting to changes of direction and task anticipatory state.

Significance

The findings could provide insight into developing classification schemes that can adapt to changes of direction and user anticipation. They could inform intent recognition strategies for controlling lower-limb assistive to robustly handle “unknown” circumstances, and thus deliver increased level of reliability and safety.

Inherent Kinematic Features of Dynamic Bimanual Path Following Tasks

Bimanual coordination is critical in many robotic and haptic systems, such as surgical robots and rehabilitation robots. While these systems often incorporate two robotic manipulators for each limb, there may be a missed opportunity to leverage overarching models of human bimanual coordination to improve the way in which the robotic manipulators are controlled and respond to the dynamic human operator. In this paper, we study the influences of several bimanual motion factors (e.g., symmetry and direction) on kinematic human joint-space features and performance outcome task-space features in a user study with eleven subjects and two haptic devices. Additionally, we evaluated the ability to use joint-space features to classify types of bimanual movement, showing the potential for a robotic system to predict how users coordinate their limbs. Three classifiers: (1) likelihood ratio, (2) k-nearest neighbor, and (3) support vector machine, were evaluated for classification accuracy in regards to the factor of number of targets. Likelihood ratio resulted in an accuracy of 79.6% with the majority of correct predictions occurring immediately at the start of movement. The task-space performance results reveal that despite the relative direction of both hands, reaching two targets results in lower performance than a single target, and symmetry alone does not contribute to performance disparity. Also, dimensionless integrated absolute jerk (DIAJ) is an indicator of superior performance for this particular task. Furthermore, these results align with current bimanual coordination theory by showing manual performance disparities are a consequence of task constraints and conceptualization.

Ultrasound Sensing Can Improve Continuous Classification of Discrete Ambulation Modes Compared to Surface Electromyography

Clinical translation of "intelligent" lower-limb assistive technologies relies on robust control interfaces capable of accurately detecting user intent. To date, mechanical sensors and surface electromyography (EMG) have been the primary sensing modalities used to classify ambulation. Ultrasound (US) imaging can be used to detect user-intent by characterizing structural changes of muscle. Our study evaluates wearable US imaging as a new sensing modality for continuous classification of five discrete ambulation modes: level, incline, decline, stair ascent, and stair descent ambulation, and benchmarks performance relative to EMG sensing. Ten able-bodied subjects were equipped with a wearable US scanner and eight unilateral EMG sensors. Time-intensity features were recorded from US images of three thigh muscles. Features from sliding windows of EMG signals were analyzed in two configurations: one including 5 EMG sensors on muscles around the thigh, and another with 3 additional sensors placed on the shank. Linear discriminate analysis was implemented to continuously classify these phase-dependent features of each sensing modality as one of five ambulation modes. US-based sensing statistically improved mean classification accuracy to 99.8% (99.5-100% CI) compared to 8-EMG sensors (85.8%; 84.0-87.6% CI) and 5-EMG sensors (75.3%; 74.5-76.1% CI). Further, separability analyses show the importance of superficial and deep US information for stair classification relative to other modes. These results are the first to demonstrate the ability of US-based sensing to classify discrete ambulation modes, highlighting the potential for improved assistive device control using less widespread, less superficial and higher resolution sensing of skeletal muscle.

Kinematic simulations of static radiographs provides discriminating features of multiple hip pathologies

Osteoarthritis is one of the most common causes of ambulatory disability. Developmental dysplasia of the hip (DDH) and femoroacetabular impingement (FAI) may lead to premature osteoarthritis in a young adult population. Current clinical assessments of DDH and FAI include clinical history, static radiological metrics, and physical examinations. Methods involving dynamic radiography such as fluoroscopy are costly and time-consuming for both patient and physician. Therefore, our aim was to simulate dynamic gait on 3D static radiological images of hips to generate "virtual interference" points between the femur and acetabulum with three methods of center-of-rotation (COR): static spherical, dynamic spherical, and dynamic ellipsoidal. We simulated the gait kinematics on pre-segmented CT images for three groups: DDH (N=10), FAI (N=10), and asymptomatic normal (N=11). The femoral head was divided into four medial quadrants to compare the ratios of virtual interference within the regions along the gait cycle and as a temporal mean. We hypothesized that discriminating characteristics would exist between the two pathologies and for the dynamic COR methods to have more discriminating characteristics than the static COR method. For each COR method, we found the temporal mean of virtual interference to be significantly different in almost every region between the hip conditions. The significance was most distinct when using the static spherical method of COR. These results are promising to individual clinical assessments of hip pathologies using static radiographs and ultimately work towards preventing premature hip disease.

Ultrasound Features of Skeletal Muscle Can Predict Kinematics of Upcoming Lower-Limb Motion

Seamless integration of lower-limb assistive devices with the human body requires an intuitive human-machine interface, which would benefit from predicting the intent of individuals in advance of the upcoming motion. Ultrasound imaging was recently introduced as an intuitive sensing interface. The objective of the present study was to investigate the predictability of joint kinematics using ultrasound features of the rectus femoris muscle during a non-weight-bearing knee extension/flexion. Motion prediction accuracy was evaluated in 67 ms increments, up to 600 ms in time. Statistical analysis was used to evaluate the feasibility of motion prediction, and the linear mixed-effects model was used to determine a prediction time window where the joint angle prediction error is barely perceivable by the sample population, hence clinically reliable. Surprisingly, statistical tests revealed that the prediction accuracy of the joint angle was more sensitive to temporal shifts than the accuracy of the joint angular velocity prediction. Overall, predictability of the upcoming joint kinematics using ultrasound features of skeletal muscle was confirmed, and a time window for a statistically and clinically reliable prediction was found between 133 and 142 ms. A reliable prediction of user intent may provide the time needed for processing, control planning, and actuation of the assistive devices at critical points during ambulation, contributing to the intuitive behavior of lower-limb assistive devices.

Biomechanical Signals of Varied Modality and Location Contribute Differently to Recognition of Transient Locomotion

Intent recognition in lower-limb assistive devices typically relies on neuromechanical sensing of an affected limb acquired through embedded device sensors. It remains unknown whether signals from more widespread sources such as the contralateral leg and torso positively influence intent recognition, and how specific locomotor tasks that place high demands on the neuromuscular system, such as changes of direction, contribute to intent recognition. In this study, we evaluated the performances of signals from varying mechanical modalities (accelerographic, gyroscopic, and joint angles) and locations (the trailing leg, leading leg and torso) during straight walking, changes of direction (cuts), and cuts to stair ascent with varying task anticipation. Biomechanical information from the torso demonstrated poor performance across all conditions. Unilateral (the trailing or leading leg) joint angle data provided the highest accuracy. Surprisingly, neither the fusion of unilateral and torso data nor the combination of multiple signal modalities improved recognition. For these fused modality data, similar trends but with diminished accuracy rates were reported during unanticipated conditions. Finally, for datasets that achieved a relatively accurate (≥90%) recognition of unanticipated tasks, these levels of recognition were achieved after the mid-swing of the trailing/transitioning leg, prior to a subsequent heel strike. These findings suggest that mechanical sensing of the legs and torso for the recognition of straight-line and transient locomotion can be implemented in a relatively flexible manner (i.e., signal modality, and from the leading or trailing legs) and, importantly, suggest that more widespread sensing is not always optimal.

Time evolution of frontal plane dynamic balance during locomotor transitions of altered anticipation and complexity

Background

Locomotor transitions between different ambulatory tasks are essential activities of daily life. During these transitions, biomechanics are affected by various factors such as anticipation, movement direction, and task complexity. These factors are thought to influence the neuromotor regulation of dynamic balance, which can be quantified using whole-body angular momentum (H). However, the specific effects of these factors on balance during transitions are not well understood. The ability to regulate dynamic balance in the presence of these contextual factors is especially important in the frontal plane, as it is usually challenging to maintain walking balance in the frontal plane for individuals with neuromuscular impairments. The purpose of this study was to apportion their effects on the time evolution of frontal plane dynamic balance during locomotor transitions of healthy, unimpaired individuals.

Methods

Five healthy young subjects performed 10 separate types of transitions with discrete combinations of factors including complexity (straight walking, cuts, combined cut/stair ascent), cut style (crossover, sidestep), and anticipation (anticipated and unanticipated). A three-way analysis of variance (ANOVA) was used to compare the maxima, minima, and average rates of change of frontal-plane H among all transitions.

Results

Before transition, within anticipated state peak value of H increased 307% in crossover style relative to sidestep style (p < 0.0001). During Transition Phase, within unanticipated state the magnitudes of average rate of change and peak value increased 70 and 46% in sidestep style compared to crossover style (p < 0.0001 and p = 0.0003). Within sidestep style, they increased in unanticipated state relative to anticipated state. Later in Correction Phase, within both anticipation states peak value of H increased 41 and 75% in cut/stairs transitions relative to cuts (p = 0.010 and p < 0.0001). For cut/stairs transitions, peak value of H increased 45% in unanticipated state compared to anticipated state (p = 0.0001).

Conclusions

These results underlined the detrimental effects of unanticipated state and task complexity on dynamic balance during walking transitions. These findings imply increased demand of neuromuscular system and functional deficits of individuals with neuromuscular disorders during these tasks. In addition, cutting style influenced frontal plane dynamic balance before transition and in response to unanticipated direction change. Collectively, these results may help identify impaired balance control of fall-prone individuals and inform interventions targeting specific destabilizing scenarios.

Use of Sonomyographic Sensing to Estimate Knee Angular Velocity During Varying Modes of Ambulation

Ultrasound (US) imaging of muscle has been introduced as a promising sensing modality for assistive device control. Ten able-bodied subjects completed level, incline and decline walking on a treadmill in a motion capture laboratory while wearing reflective markers on upper- and lower-body. A wearable US transducer was affixed to subjects' anterior thigh, and time-intensity features were extracted from transverse US images of the knee extensor muscles. These features were used to train and test Gaussian process regression models for continuous estimation of knee flexion/extension angular velocity. Four regression models were evaluated: (1) subject-dependent/task-specific, (2) subject-dependent/pooled-tasks, (3) subject-independent/task-specific, and (4) subject-independent/pooled-tasks. Subject-independent models were "tuned" with up to six strides of the test subject's data to boost performance. A two-factor analysis of variance test was used to assess the effect of each approach on root mean square error (RMSE) of estimated knee angular velocity (α=0.05). Statistical parametric mapping (SPM) was completed to compare actual vs. estimated knee angular velocity as a function of the gait cycle (α=0.05). For incline and level walking, the subject-dependent/pooled-tasks model resulted in the lowest error while the subject-dependent/task-specific model resulted in the lowest error for decline walk. Impressively, the two-factor test revealed no difference between task-specific and pooled-task models. Furthermore, despite capturing many important features of knee velocity across individuals there were, as expected, significant differences between subject-dependent and subject-independent models. Collectively, these results are promising for potential assistive device control with error rates <10% for all regression models that were tested.Clinical Relevance-This work is the first study to demonstrate the feasibility of using ultrasound-based sensing for estimation of knee angular velocity during multiple modes of ambulation.

Body Segment Mechanical Signal Contributions to Continuous Prediction of Locomotor Transitions Performed under Varying Anticipation

A reliable, flexible and simple source of information would benefit robust handling of predicting locomotion modes for assistive device control (e.g., prostheses). However, to date, the sources of mechanical signals have been mainly limited to the information acquired through embedded sensors in the device. It remains unclear whether biomechanical signals from unaffected or less affected locations (e.g., contralateral side or upper body) would be reliable sources of information. Furthermore, the possible influence of the anticipatory state of the task on recognition accuracy, emphasizes the need to identify reliable data sources for both anticipated and unanticipated tasks. Here, accelerographic and gyroscopic signals from the leading leg, trailing leg, trunk-pelvis, and their fusion were compared with respect to their ability to predict changes of direction (cuts), cut-to-stair transitions, and level-ground walking performed under varied task anticipation. We hypothesized that fusion of lower- and upper-body signals would provide better accuracy than unilateral information (i.e., trailing/leading leg), and recognition accuracy would diminish when tasks were unanticipated. Surprisingly, signal fusion appeared not to be advantageous to unilateral signals. Leading and trailing leg data demonstrated statistically identical performances, and trunk-pelvis signals showed significantly (α=0.05) inferior performance relative to unilateral data. While anticipated tasks were accurately predicted (≥90%) even as early as 500 ms prior to entering each locomotor transition, in unanticipated tasks, similar accuracy rates were achieved only after the mid-swing of the transitioning leg. The findings could provide insight into flexible, yet, dependable sensor sets for intent recognition frameworks during varying user cognitive states.

Dynamic neural network approach to targeted balance assessment of individuals with and without neurological disease during non-steady-state locomotion

Background

Clinical balance assessments often rely on functional tasks as a proxy for balance (e.g., Timed Up and Go). In contrast, analyses of balance in research settings incorporate quantitative biomechanical measurements (e.g., whole-body angular momentum, H) using motion capture techniques. Fully instrumenting patients in the clinic is not feasible, and thus it is desirable to estimate biomechanical quantities related to balance from measurements taken from a subset of the body segments. Machine learning algorithms are well-suited for this type of low- to high-dimensional mapping. Thus, our goal was to develop and test an artificial neural network that to predict segment contributions to whole-body angular momentum from linear acceleration and angular velocity signals (i.e., those typically available to wearable inertial measurement units, IMUs) taken from a sparse set of body segments.

Methods

Optical motion capture data were collected from five able-bodied individuals and five individuals with Parkinson's disease (PD) walking on a non-steady-state locomotor circuit comprising stairs, ramps and changes of direction. Motion data were used to calculate angular momentum (i.e., “gold standard” output data) and body-segment linear acceleration and angular velocity data from local reference frames at the wrists, ankles and neck (i.e., network input). A dynamic nonlinear autoregressive neural network was trained using the able-bodied data (pooled across subjects). The neural network was tested on data from individuals with PD with noise added to simulate real-world IMU data.

Results

Correlation coefficients of the predicted segment contributions to whole-body angular momentum with the gold standard data were 0.989 for able-bodied individuals and 0.987 for individuals with PD. Mean RMS errors were between 2 and 7% peak signal magnitude for all body segments during completion of the locomotor circuits.

Conclusion

Our results suggest that estimating segment contributions to angular momentum from mechanical signals (linear acceleration, angular velocity) from a sparse set of body segments is a feasible method for assessing coordination of balance—even using a network trained on able-bodied data to assess individuals with neurological disease. These targeted estimates of segmental momenta could potentially be delivered to clinicians using a sparse sensor set (and likely in real-time) in order to enhance balance rehabilitation of people with PD.

Prediction of Distal Lower-Limb Motion Using Ultrasound-Derived Features of Proximal Skeletal Muscle

Control of lower-limb assistive devices would benefit from predicting the intent of individuals in advance of upcoming motion, rather than estimating the current states of their motion. Human lower-limb motion estimation using ultrasound (US) image derived features of skeletal muscle has been demonstrated. However, predictability of motion in time remains an open question. The objective of this study was to assess the predictability of distal lower-limb motion using US image features of rectus femoris (RF) muscle during non-weight-bearing knee flexion/extension. A series of time shifts was introduced between the US features and the joint position in 67 ms steps from 0 ms (i.e., estimation, no prediction) up to predicting 467 ms in advance. A US-based algorithm to estimate lower-limb motion was then used to predict the knee joint position in time using the US features after introducing the time shifts. The accuracy of joint motion prediction after each time shift was compared to the accuracy of joint motion estimation. The reliability of the prediction was then assessed using an analysis of variance (ANOVA) test. The motion prediction accuracy was found to be reliable up to 200 ms, where the average root mean square error (RMSE) of prediction across 9 healthy subjects was 0.89 degrees greater than the average RMSE (7.39 degrees) of motion estimation for the same group of subjects. These findings suggest a reliable prediction of upcoming lower-limb motion is feasible using the US features of skeletal muscle up to a certain point. A reliable prediction may provide lower-limb assistive device control systems with a time-window for processing and control planning, and actuation hence improving the volitional control behaviors of lower-limb assistive devices.

Gait Phase Identification During Level, Incline and Decline Ambulation Tasks Using Portable Sonomyographic Sensing

Clinical viability of powered lower-limb assistive devices requires reliable and intuitive control strategies. Stance and swing are the main phases of the gait cycle across different locomotion tasks. Hence, a reliable method to accurately identify these phases can decrease sensing complexity and assist in enabling high-level control of assistive devices. Ultrasound (US) imaging has recently been introduced as a new sensing modality that may provide a solution for intuitive device control. US images of the rectus femoris and vastus intermedius muscles were collected in humans during level, incline, and decline ambulation tasks. Five low-level static (i.e. time-independent) features of US images were measured with respect to a reference image, including correlation coefficient, sum of absolute differences, structural similarity index, sum of squared differences, and image echogenicity. Time-derivatives of the static features were also calculated as temporal features. Support vector machine classifiers were trained using these static features to identify the gait phase both dependent and independent of the ambulation tasks. The results indicate an accuracy of 88.3% in identifying the gait phases for task-independent classifiers when trained using only the static features. Performance of the classifiers improved significantly to 92.8% after using the temporal features (p $\lt0.01)$. The algorithm was efficient and the average processing speed was faster than 100 Hz. This study is the first demonstration on use of US imaging to provide continuous estimates of ambulation phase, and on multiple surfaces. These findings suggest task-independent approaches may reliably identify the main phases of the gait cycle. Advancements in this area of study may provide simpler intuitive strategies for high-level assistive device control and increase their clinical relevance.

Statistical analysis of timeseries data reveals changes in 3D segmental coordination of balance in response to prosthetic ankle power on ramps

Active ankle-foot prostheses generate mechanical power during the push-off phase of gait, which can offer advantages over passive prostheses. However, these benefits manifest primarily in joint kinetics (e.g., joint work) and energetics (e.g., metabolic cost) rather than balance (whole-body angular momentum, H), and are typically constrained to push-off. The purpose of this study was to analyze differences between active and passive prostheses and non-amputees in coordination of balance throughout gait on ramps. We used Statistical Parametric Mapping (SPM) to analyze time-series contributions of body segments (arms, legs, trunk) to three-dimensional H on uphill, downhill, and level grades. The trunk and prosthetic-side leg contributions to H at toe-off when using the active prosthesis were more similar to non-amputees compared to using a passive prosthesis. However, using either a passive or active prosthesis was different compared to non-amputees in trunk contributions to sagittal-plane H during mid-stance and transverse-plane H at toe-off. The intact side of the body was unaffected by prosthesis type. In contrast to clinical balance assessments (e.g., single-leg standing, functional reach), our analysis identifies significant changes in the mechanics of segmental coordination of balance during specific portions of the gait cycle, providing valuable biofeedback for targeted gait retraining.

Lower Limb Motion Estimation Using Ultrasound Imaging: A Framework for Assistive Device Control

OBJECTIVE

Powered assistive devices need improved control intuitiveness to enhance their clinical adoption. Therefore, the intent of individuals should be identified and the device movement should adhere to it. Skeletal muscles contract synergistically to produce defined lower limb movements, so unique contraction patterns in lower extremity musculature may provide a means of device joint control. Ultrasound (US) imaging enables direct measurement of the local deformation of muscle segments. Hence, the objective of this study was to assess the feasibility of using US to estimate human lower limb movements.

METHODS

A novel algorithm was developed to calculate US features of the rectus femoris muscle during a non-weight-bearing knee flexion/extension experiment by nine able-bodied subjects. Five US features of the skeletal muscle tissue were studied, namely thickness, angle between aponeuroses, pennation angle, fascicle length, and echogenicity. A multiscale ridge filter was utilized to extract the structures in the image and a random sample consensus (RANSAC) model was used to segment muscle aponeuroses and fascicles. A localization scheme further guided RANSAC to enable tracking in a US image sequence. Gaussian process regression models were trained using segmented features to estimate both knee joint angle and angular velocity.

RESULTS

The proposed segmentation-estimation approach could estimate knee joint angle and angular velocity with an average root mean square error value of 7.45° and 0.262 rad/s, respectively. The average processing rate was 3-6 frames/s that is promising toward real-time implementation.

CONCLUSION

Experimental results demonstrate the feasibility of using US to estimate human lower extremity motion. The ability of the algorithm to work in real time may enable the use of US as a neural interface for lower limb applications.

SIGNIFICANCE

Intuitive intent recognition of human lower extremity movements using wearable US imaging may enable volitional assistive device control and enhance locomotor outcomes for those with mobility impairments.

A machine learning approach to targeted balance rehabilitation in people with Parkinson's disease using a sparse sensor set

Clinical Balance Assessments Often Rely On Functional Tasks As A Proxy For Balance (E.G., Timed Up And Go). In Contrast, Analyses Of Balance In Research Settings Incorporate Quantitative Biomechanical Measurements (E.G., Whole-Body Angular Momentum, H) Using Motion Capture Techniques. Fully Instrumenting Patients In The Clinic Is Not Feasible, And Thus It Is Desirable To Estimate Biomechanical Quantities Related To Balance From Measurements Taken From A Subset Of The Body Segments. Machine Learning Algorithms Are Well-Suited For This Type Of Low- To High-Dimensional Mapping. Thus, Our Objective Was To Develop And Validate An Artificial Neural Network For Estimating Contributions To H From 12 Body Segments Using Only Five Inertial Measurement Units. The Network Was Trained, Tested And Validated On Data From Five Able-Bodied Individuals Performing Forty Trials Each Of A Circuit Involving Complex Walking Tasks, Including Stairs, Ramp, And Direction Changes. The Network Was Also Separately Tested On Four Trials Of An Individual With Parkinson'S Disease Walking On The Circuit. The Output Of The Network Was Strongly Correlated With The Segment Contributions To H In Both Able-Bodied (R= 0.997) And Parkinson'S Disease (R= (0.998) Subjects. The Estimated Values Also Had Low Error Relative To The Signal Magnitude, With The Largest Mean ± SD Rootmean-Squared Errors Of 8.04 ± 1.76% Peak Signal Magnitude In Able-Bodied Individuals And 7.96 ± 0.91% In The Individual With Parkinson'S Disease. These Promising Results Establish The Feasibility Of Using A Sparse Set Of Inertial Measurement Units To Provide Quantitative Data To Clinicians For Targeted Balance Rehabilitation Across Different Patients.

Residual Limb Revision Surgery Alters Velocity-Curvature Coupling During Stepping and Turning of a Transfemoral Amputee

Two-Thirds Power Law is a frequently observed relationship in human movement, relating velocity and curvature of movement trajectory. These movements span handwriting, larvae crawling, and human-robot interaction. Despite vast acceptance as a common principle of biology, it is unknown if the power law applies to interaction between amputees and prostheses, and if interventions to augment the physical connection between amputees and prostheses influence this speed-curvature coupling during demanding forms of human locomotion. The purpose of this study was to determine if individuals with transfemoral amputation exhibit a biologically-appropriate power law relationship during non-steady-state locomotion, and if a surgical intervention to reduce residual limb soft tissue would influence the observed coupling. We hypothesized that a power regression would well characterize amputee locomotion, and that limb revision surgery would result in a non-linear power coupling close to one-third and overall increased speed (i.e., higher linear coupling) in each non-steady-state movement. The subject performed repeated trials of left and right 90° turns during walking, as well as Foursquare Step Test (FSST), while whole-body kinematics were captured. After fitting center-of-mass velocity and curvature to the power law, the power coupling in FSST was similar to the Two-Thirds Power Law, while turning was not. Finally, the intervention was shown to increase linear coupling suggesting an overall improvement in movement tempo characterized by modest changes in velocity, enabling tasks to be achieved more quickly.

A comparison of stability metrics based on inverted pendulum models for assessment of ramp walking

Maintaining balance on ramps is important for mobility. However, balance is commonly assessed using inverted pendulum-based metrics (e.g., margin of stability), which may not be appropriate for assessment of human walking on non-level surfaces. To investigate this, we analyzed stability on ramps using four different inverted pendulum models: extrapolated center of mass (XCOM), foot placement estimate (FPE), foot placement estimate neglecting angular momentum (FPE_NoH), and capture point (CAP). We analyzed experimental data from 10 able-bodied individuals walking on a ramp at 0°, ±5°, and ±10°. Contrary to our hypothesis that the magnitude of differences between metrics would be greatest at ±10°, we observed the greatest magnitude of differences between metrics at 0°. In general, the stability metrics were bounded by FPE and CAP at each slope, consistent with prior studies of level walking. Our results also suggest that clinical providers and researchers should be aware that assessments that neglect angular momentum (e.g., margin of stability, XCOM) may underestimate stability in the sagittal-plane in comparison to analyses which incorporate angular momentum (e.g., FPE). Except for FPE_NoH-CAP (r = 0.82), differences between metrics were only moderately correlated (|r|≤0.65) with violations of leg length assumptions in the underlying inverted pendulum models. The differences in FPE_NoH relative to FPE and CAP were strongly correlated with body center of mass vertical velocity (max |r| = 0.92), suggesting that model representations of center of mass motion influence stability metrics. However, there was not a clear overall relationship between model inputs and differences in stability metrics. Future sensitivity analyses may provide additional insight into model characteristics that influence stability metrics.

3D CT segmentation of CAM type femoroacetabular impingement-reliability and relationship of CAM lesion with anthropomorphic features

OBJECTIVE:

Evaluate feasibility and reliability of 3DCT semi-automatic segmentation and volumetrics of CAM lesions in femoroacetabular impingement and determine correlations with anthropometrics.

METHODS:

A consecutive series of 43 patients with CAM type FAI underwent 3DCT. 20 males and 23 females (30 unilateral and 13 bilateral symptomatic hips) were included. 56 CAM lesions and femoral heads were segmented by two readers. Radial images were obtained for alpha angles. Pearson and ICC correlations were used for analysis.

RESULTS:

In 43 patients (male: female = 1 : 1.15), mean ± SD of age, height, BMI were 36.6 ± 11.47 years, 1.72 ± 0.10 meters and 26.25 ± 4.31  kg m^-². Femoral head and bumps were segmented in 4  min. Inter reader reliability was good to excellent for volumetrics and poor for alpha angles. Mean ± SD of CAM lesion and femoral head volumes were significantly larger (6.7 ± 2.5 cc³ and 62.9 ± 10.8 cc³) for males than females (p < 0.001) and these increased with increasing patient height (Pearson correlation and p-values = 0.45, 0.0006; 0.82, < 0.0001 respectively).

CONCLUSION:

Volumetric analysis of CAM lesion shows better inter reader reliability than alpha angle measurements. CAM and femoral head volumes exhibit significant positive correlations with patient heights and male gender that may aid in pre-operative planning for femoroplasty.

ADVANCES IN KNOWLEDGE:

Femoral head & CAM volumes are segmented three times faster than alpha angles with superior inter reader reliability than alpha angles. Femoral head & CAM volumes are significantly larger in males and positively correlate with patients' heights.

Segmental contributions to sagittal-plane whole-body angular momentum when using powered compared to passive ankle-foot prostheses on ramps

Understanding the effects of an assistive device on dynamic balance is crucial, particularly for robotic leg prostheses. Analyses of dynamic balance commonly evaluate the range of whole-body angular momentum (H). However, the contributions of individual body segments to overall H throughout gait may yield futher insights, specifically for people with transtibial amputation using powered prostheses. We evaluated segment contributions to H using Statistical Parametric Mapping to assess the effects of prosthesis type (powered vs passive) and ramp angle on segmental coordination. The slope main effect was significant in all segments, the prosthesis main effect was significant in the prosthetic leg (device and residuum) and trunk, and the slope by prosthesis interaction effect was significant in the prosthetic leg and trunk. The magnitude of contributions to sagittal-plane H from the prosthetic leg was larger when using the powered prosthesis. The trunk contributed more positive (backward) H after prosthetic leg toe-off when using the powered prosthesis on inclines, similar to the soleus muscle. However, trunk contributions to H on declines were similar when using a powered and passive prosthesis, suggesting that the powered prosthesis may not replicate soleus function when walking downhill. Our novel assessment method evaluated robotic leg prostheses not only based on local joint mechanics, but also considering whole-body biomechanics.

Considering passive mechanical properties and patient user motor performance in lower limb prosthesis design optimization to enhance rehabilitation outcomes

Background

Selection of prosthesis mechanical characteristics to restore function of persons with lower-limb loss can be framed as an optimization problem to satisfy a given performance objective. However, the choice of a particular objective is critical, and considering only device and generalizable outcomes across users without accounting for inherent motor performance likely restricts a given patient from fully realizing the benefits of a prosthetic intervention.

Objectives

This review presents methods for optimizing passive below-knee prosthesis designs to maximize rehabilitation outcomes and how considerations on patient motor performance may enhance these outcomes.

Major Findings

Available literature supports that considering patient-specific variables pertaining to motor performance permits a multidimensional landscape relating device characteristics and user function, which may yield more accurate predictions of rehabilitation outcomes for individual patients. Moreover, the addition of targeted physical therapeutic interventions that encourage user self-organization may further improve these outcomes. We note the potential of existing paradigms to address these additional dimensions, and we encourage investigators to consider the many different performance objectives available for prosthesis optimization.

Conclusions

By considering user motor performance in combination with prosthesis mechanical characteristics, a staged optimization approach can be formulated which acknowledges that device modifications may only improve outcomes to a certain extent and user self-organization is a critical component to complete rehabilitation. An iterative process that can be integrated within existing rehabilitative practices accounts for changes in patient status through combined targeted prosthetic solutions and physical therapeutic techniques, and embodies the concept of personalized intervention for patients with lower limb-loss.

Stability-maneuverability trade-offs during lateral steps

Selecting a specific foot placement strategy to perform walking maneuvers requires the management of several competing factors, including: maintaining stability, positioning oneself to actively generate impulses, and minimizing mechanical energy requirements. These requirements are unlikely to be independent. Our purpose was to determine the impact of lateral foot placement on stability, maneuverability, and energetics during walking maneuvers. Ten able-bodied adults performed laterally-directed walking maneuvers. Mediolateral placement of the "Push-off" foot during the maneuvers was varied, ranging from a cross-over step to a side-step. We hypothesized that as mediolateral foot placement became wider, passive stability in the direction of the maneuver, the lateral impulse generated to create the maneuver, and mechanical energy cost would all increase. We also hypothesized that subjects would prefer an intermediate step width reflective of trade-offs between stability vs. both maneuverability and energy. In support of our first hypothesis, we found that as Push-off step width increased, lateral margin of stability, peak lateral impulse, and total joint work all increased. In support of our second hypothesis, we found that when subjects had no restrictions on their mediolateral foot placement, they chose a foot placement between the two extreme positions. We found a significant relationship (p<0.05) between lateral margin of stability and peak lateral impulse (r=0.773), indicating a trade-off between passive stability and the force input required to maneuver. These findings suggest that during anticipated maneuvers people select foot placement strategies that balance competing costs to maintain stability, actively generate impulses, and minimize mechanical energy costs.

Assessing the Relative Contributions of Active Ankle and Knee Assistance to the Walking Mechanics of Transfemoral Amputees Using a Powered Prosthesis

Powered knee-ankle prostheses are capable of providing net-positive mechanical energy to amputees. Yet, there are limitless ways to deliver this energy throughout the gait cycle. It remains largely unknown how different combinations of active knee and ankle assistance affect the walking mechanics of transfemoral amputees. This study assessed the relative contributions of stance phase knee swing initiation, increasing ankle stiffness and powered plantarflexion as three unilateral transfemoral amputees walked overground at their self-selected walking speed. Five combinations of knee and ankle conditions were evaluated regarding the kinematics and kinetics of the amputated and intact legs using repeated measures analyses of variance. We found eliminating active knee swing initiation or powered plantarflexion was linked to increased compensations of the ipsilateral hip joint during the subsequent swing phase. The elimination of knee swing initiation or powered plantarflexion also led to reduced braking ground reaction forces of the amputated and intact legs, and influenced both sagittal and frontal plane loading of the intact knee joint. Gradually increasing prosthetic ankle stiffness influenced the shape of the prosthetic ankle plantarflexion moment, more closely mirroring the intact ankle moment. Increasing ankle stiffness also corresponded to increased prosthetic ankle power generation (despite a similar maximum stiffness value across conditions) and increased braking ground reaction forces of the amputated leg. These findings further our understanding of how to deliver assistance with powered knee-ankle prostheses and the compensations that occur when specific aspects of assistance are added/removed.

Improved Weight-Bearing Symmetry for Transfemoral Amputees During Standing Up and Sitting Down With a Powered Knee-Ankle Prosthesis

OBJECTIVE

To test a new user-modulated control strategy that enables improved control of a powered knee-ankle prosthesis during sit-to-stand and stand-to-sit movements.

DESIGN

Within-subject comparison study.

SETTING

Gait laboratory.

PARTICIPANTS

Unilateral transfemoral amputees (N=7; 4 men, 3 women) capable of community ambulation.

INTERVENTIONS

Subjects performed 10 repetitions of sit-to-stand and stand-to-sit with a powered knee-ankle prosthesis and with their prescribed passive prosthesis in a randomized order. With the powered prosthesis, knee and ankle power generation were controlled as a function of weight transferred onto the prosthesis.

MAIN OUTCOME MEASURES

Vertical ground reaction force limb asymmetry and durations of movement were compared statistically (Wilcoxon signed-rank test, α=.05).

RESULTS

For sit-to-stand, peak vertical ground reaction forces were significantly less asymmetric using the powered prosthesis (mean, 19.3%±11.8%) than the prescribed prosthesis (57.9%±13.5%; P=.018), where positive asymmetry values represented greater force through the intact limb. For stand-to-sit, peak vertical ground reaction forces were also significantly less asymmetric using the powered prosthesis (28.06%±11.6%) than the prescribed prosthesis (48.2%±16%; P=.028). Duration of movement was not significantly different between devices (sit-to-stand: P=.18; stand-to-sit: P=.063).

CONCLUSIONS

Allowing transfemoral amputees more control over the timing and rate of knee and ankle power generation enabled users to stand up and sit down with their weight distributed more equally between their lower limbs. Increased weight bearing on the prosthetic limb may make such activities of daily living easier for transfemoral amputees.

Intuitive control of a powered prosthetic leg during ambulation: a randomized clinical trial

IMPORTANCE

Some patients with lower leg amputations may be candidates for motorized prosthetic limbs. Optimal control of such devices requires accurate classification of the patient's ambulation mode (eg, on level ground or ascending stairs) and natural transitions between different ambulation modes.

OBJECTIVE

To determine the effect of including electromyographic (EMG) data and historical information from prior gait strides in a real-time control system for a powered prosthetic leg capable of level-ground walking, stair ascent and descent, ramp ascent and descent, and natural transitions between these ambulation modes.

DESIGN, SETTING, AND PARTICIPANTS

Blinded, randomized crossover clinical trial conducted between August 2012 and November 2013 in a research laboratory at the Rehabilitation Institute of Chicago. Participants were 7 patients with unilateral above-knee (n = 6) or knee-disarticulation (n = 1) amputations. All patients were capable of ambulation within their home and community using a passive prosthesis (ie, one that does not provide external power).

INTERVENTIONS

Electrodes were placed over 9 residual limb muscles and EMG signals were recorded as patients ambulated and completed 20 circuit trials involving level-ground walking, ramp ascent and descent, and stair ascent and descent. Data were acquired simultaneously from 13 mechanical sensors embedded on the prosthesis. Two real-time pattern recognition algorithms, using either (1) mechanical sensor data alone or (2) mechanical sensor data in combination with EMG data and historical information from earlier in the gait cycle, were evaluated. The order in which patients used each configuration was randomized (1:1 blocked randomization) and double-blinded so patients and experimenters did not know which control configuration was being used.

MAIN OUTCOMES AND MEASURES

The main outcome of the study was classification error for each real-time control system. Classification error is defined as the percentage of steps incorrectly predicted by the control system.

RESULTS

Including EMG signals and historical information in the real-time control system resulted in significantly lower classification error (mean, 7.9% [95% CI, 6.1%-9.7%]) across a mean of 683 steps (range, 640-756 steps) compared with using mechanical sensor data only (mean, 14.1% [95% CI, 9.3%-18.9%]) across a mean of 692 steps (range, 631-775 steps), with a mean difference between groups of 6.2% (95% CI, 2.7%-9.7%] (P = .01).

CONCLUSIONS AND RELEVANCE

In this study of 7 patients with lower limb amputations, inclusion of EMG signals and temporal gait information reduced classification error across ambulation modes and during transitions between ambulation modes. These preliminary findings, if confirmed, have the potential to improve the control of powered leg prostheses.

Contributions of knee swing initiation and ankle plantar flexion to the walking mechanics of amputees using a powered prosthesis

Recently developed powered prostheses are capable of producing near-physiological joint torque at the knee and/or ankle joints. Based on previous studies of biological joint impedance and the mechanics of able-bodied gait, an impedance-based controller has been developed for a powered knee and ankle prosthesis that integrates knee swing initiation and powered plantar flexion in late stance with increasing ankle stiffness throughout stance. In this study, five prosthesis configuration conditions were tested to investigate the individual contributions of each sub-strategy to the overall walking mechanics of four unilateral transfemoral amputees as they completed a clinical 10-m walk test using a powered knee and ankle prosthesis. The baseline condition featured constant ankle stiffness and no swing initiation or powered plantar flexion. The four remaining conditions featured knee swing initiation alone (SI) or in combination with powered plantar flexion (SI+PF), increasing ankle stiffness (SI+IK), or both (SI+PF+IK). Self-selected walking speed did not significantly change between conditions, although subjects tended to walk the slowest in the baseline condition compared to conditions with swing initiation. The addition of powered plantar flexion resulted in significantly higher ankle power generation in late stance irrespective of ankle stiffness. The inclusion of swing initiation resulted in a significantly more flexed knee at toe off and a significantly higher average extensor knee torque following toe off. Identifying individual contributions of intrinsic control strategies to prosthesis biomechanics could help inform the refinement of impedance-based prosthesis controllers and simplify future designs of prostheses and lower-limb assistive devices alike.

Experimental effective shape control of a powered transfemoral prosthesis

This paper presents the design and experimental implementation of a novel feedback control strategy that regulates effective shape on a powered transfemoral prosthesis. The human effective shape is the effective geometry to which the biological leg conforms--through movement of ground reaction forces and leg joints--during the stance period of gait. Able-bodied humans regulate effective shapes to be invariant across conditions such as heel height, walking speed, and body weight, so this measure has proven to be a very useful tool for the alignment and design of passive prostheses. However, leg joints must be actively controlled to assume different effective shapes that are unique to tasks such as standing, walking, and stair climbing. Using our previous simulation studies as a starting point, we model and control the effective shape as a virtual kinematic constraint on the powered Vanderbilt prosthetic leg with a custom instrumented foot. An able-bodied subject used a by-pass adapter to walk on the controlled leg over ground and over a treadmill. These preliminary experiments demonstrate, for the first time, that effective shape (or virtual constraints in general) can be used to control a powered prosthetic leg.

Controlling Knee Swing Initiation and Ankle Plantarflexion With an Active Prosthesis on Level and Inclined Surfaces at Variable Walking Speeds

Improving lower-limb prostheses is important to enhance the mobility of amputees. The purpose of this paper is to introduce an impedance-based control strategy (consisting of four novel algorithms) for an active knee and ankle prosthesis and test its generalizability across multiple walking speeds, walking surfaces, and users. The four algorithms increased ankle stiffness throughout stance, decreased knee stiffness during terminal stance, as well as provided powered ankle plantarflexion and knee swing initiation through modifications of equilibrium positions of the ankle and knee, respectively. Seven amputees (knee disarticulation and transfemoral levels) walked at slow, comfortable, and hurried speeds on level and inclined (10°) surfaces. The prosthesis was tuned at their comfortable level ground walking speed. We further quantified trends in prosthetic knee and ankle kinematics, and kinetics across conditions. Subjects modulated their walking speed by ±25% (average) from their comfortable speeds. As speed increased, increasing ankle angles and velocities as well as stance phase ankle power and plantarflexion torque were observed. At slow and comfortable speeds, plantarflexion torque was increased on the incline. At slow and comfortable speeds, stance phase positive knee power was increased and knee torque more flexor on the incline. As speed increased, knee torque became less flexor on the incline. These algorithms were shown to generalize well across speed, produce gait mechanics that compare favorably with non-amputee data, and display evidence of scalable device function. They have the potential to reduce the challenge of clinically configuring such devices and increase their viability during daily use.

Configuring a powered knee and ankle prosthesis for transfemoral amputees within five specific ambulation modes

Lower limb prostheses that can generate net positive mechanical work may restore more ambulation modes to amputees. However, configuration of these devices imposes an additional burden on clinicians relative to conventional prostheses; devices for transfemoral amputees that require configuration of both a knee and an ankle joint are especially challenging. In this paper, we present an approach to configuring such powered devices. We developed modified intrinsic control strategies--which mimic the behavior of biological joints, depend on instantaneous loads within the prosthesis, or set impedance based on values from previous states, as well as a set of starting configuration parameters. We developed tables that include a list of desired clinical gait kinematics and the parameter modifications necessary to alter them. Our approach was implemented for a powered knee and ankle prosthesis in five ambulation modes (level-ground walking, ramp ascent/descent, and stair ascent/descent). The strategies and set of starting configuration parameters were developed using data from three individuals with unilateral transfemoral amputations who had previous experience using the device; this approach was then tested on three novice unilateral transfemoral amputees. Only 17% of the total number of parameters (i.e., 24 of the 140) had to be independently adjusted for each novice user to achieve all five ambulation modes and the initial accommodation period (i.e., time to configure the device for all modes) was reduced by 56%, to 5 hours or less. This approach and subsequent reduction in configuration time may help translate powered prostheses into a viable clinical option where amputees can more quickly appreciate the benefits such devices can provide.

Optimization of prosthetic foot stiffness to reduce metabolic cost and intact knee loading during below-knee amputee walking: a theoretical study

Unilateral below-knee amputees develop abnormal gait characteristics that include bilateral asymmetries and an elevated metabolic cost relative to non-amputees. In addition, long-term prosthesis use has been linked to an increased prevalence of joint pain and osteoarthritis in the intact leg knee. To improve amputee mobility, prosthetic feet that utilize elastic energy storage and return (ESAR) have been designed, which perform important biomechanical functions such as providing body support and forward propulsion. However, the prescription of appropriate design characteristics (e.g., stiffness) is not well-defined since its influence on foot function and important in vivo biomechanical quantities such as metabolic cost and joint loading remain unclear. The design of feet that improve these quantities could provide considerable advancements in amputee care. Therefore, the purpose of this study was to couple design optimization with dynamic simulations of amputee walking to identify the optimal foot stiffness that minimizes metabolic cost and intact knee joint loading. A musculoskeletal model and distributed stiffness ESAR prosthetic foot model were developed to generate muscle-actuated forward dynamics simulations of amputee walking. Dynamic optimization was used to solve for the optimal muscle excitation patterns and foot stiffness profile that produced simulations that tracked experimental amputee walking data while minimizing metabolic cost and intact leg internal knee contact forces. Muscle and foot function were evaluated by calculating their contributions to the important walking subtasks of body support, forward propulsion and leg swing. The analyses showed that altering a nominal prosthetic foot stiffness distribution by stiffening the toe and mid-foot while making the ankle and heel less stiff improved ESAR foot performance by offloading the intact knee during early to mid-stance of the intact leg and reducing metabolic cost. The optimal design also provided moderate braking and body support during the first half of residual leg stance, while increasing the prosthesis contributions to forward propulsion and body support during the second half of residual leg stance. Future work will be directed at experimentally validating these results, which have important implications for future designs of prosthetic feet that could significantly improve amputee care.