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.

Dysplastic hips demonstrate increased joint translation at higher body mass index.

OBJECTIVE: To determine which radiographic measures used to define the severity of hip dysplasia are associated with hip joint translation and to investigate relationships between position, body mass index, and joint translation. MATERIALS AND METHODS: This is a cross-sectional retrospective study evaluating 10 validated radiographic measures of dysplasia on weight-bearing AP pelvis and supine 45-degree bilateral Dunn radiographs of 93 young adults with symptomatic hip dysplasia presenting to a single academic institution between October 2016 and May 2019. We determined the difference between standing and supine measurements for each hip and the correlation of each measure with the patient's body mass index. RESULTS: Femoral head extrusion index was 2.49% lower on supine X-ray (p = 0.0020). Patients with higher body mass index had higher center gap distance (p = 0.0274), femoral head extrusion (p = 0.0170), and femoral head lateralization (p = 0.0028) when standing. They also had higher Tönnis angle (pstanding = 0.0076, psupine = 0.0121) and lower lateral center-edge angle (pstanding = 0.0196, psupine = 0.0410) in both positions. The difference in femoral head lateralization between standing and supine positions increased with higher body mass index (p = 0.0081). CONCLUSION: Translation of the hip joint with position change is demonstrated by decreased femoral head extrusion index on supine X-ray. Patients with higher body mass index had more dysplastic hips, as measured by five of six radiographic outcomes of dysplasia, and experienced more translation with weight-bearing, reflected by increased femoral head lateralization.

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 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.

Regional muscle changes in adult dysfunctional hip conditions of femoroacetabular impingement and hip dysplasia.

OBJECTIVE: To analyze regional muscle CT density and bulk in femoroacetabular impingement (FAI) and hip dysplasia (HD) versus controls. MATERIALS AND METHODS: Patients who obtained perioperative CT imaging for FAI and HD before surgery were retrospectively studied. Asymptomatic controls included for comparison. Two readers independently evaluated regional hip muscle [iliopsoas (IP), rectus femoris (RF), gluteus minimus (Gm), and medius (GM)] density, muscle area, and muscle circumference. Inter-observer reliability calculated using intra-class correlation coefficient (ICC). RESULTS: A consecutive series of 25 FAI patients, 16 HD patients, and 38 controls were recruited in the study. FAI patients had significantly greater Gm and GM circumferences as well as greater RF and IP areas on the normal side compared to the asymptomatic control group (p values 0.004, 0.032, 0.033, and 0.028, respectively). In addition, Gm and RF circumferences and RF area were significantly larger (p values 0.029, 0.036, and 0.014, respectively) in FAI patients on the affected side compared to the control group. HD patients had significantly smaller Gm and GM circumferences on the affected side than normal side measurements in FAI group (p values 0.043 and 0.003, respectively). Normal side GM circumference was also smaller in HD patients than normal side FAI hips (p value 0.02). There was no significant difference between the measurements on normal and abnormal sides in each disease group. No significant difference was found between measurements of HD compared to controls (p > 0.05). No muscle density differences were seen among different groups. There was moderate to excellent inter-reader reliability for all measurements except Gm muscle density. CONCLUSIONS: Muscle analysis was able to quantify differences among patients with FAI, HD, and asymptomatic controls. These changes could indicate either a muscle imbalance contributing to the pathology or disuse atrophy, which may have implications for specific muscle-strengthening therapies and rehabilitation procedures in such patients.

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.

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.

Cam-type femoroacetabular impingement-correlations between alpha angle versus volumetric measurements and surgical findings.

AIM: Determine correlations of 3DCT cam-type femoroacetabular impingement (FAI) measurements with surgical findings of labral tear and cartilage loss. METHODS: Digital search of symptomatic cam-type FAI from July 2013 to August 2016 yielded 43 patients. Two readers calculated volumes of femoral head, bump, and alpha angles on 3DCT images. Correlations between CT and surgical findings, inter-, and intra-reader reliabilities were assessed using Spearman rank correlation and intraclass correlation coefficients (ICC). RESULTS: Thirteen men and 14 women aged 37 ± 10 (mean ± SD) years were included. Most common clinical finding was positive flexion-adduction-internal rotation (70.4%). Twenty-seven labral tears and 20 cartilage defects were surgically detected. Significant correlations existed between femoral bump, head volumes, and extent of the labral tear (p = 0.008 and 0.003). No significant correlations were found between the alpha angles at 12 to 3 o'clock and the extent of labral tear (p = 0.2, 0.8, 0.9, and 0.09) or any measurement with the cartilage loss (p values for alpha 12 to 3, bump, and head volumes = 0.7, 0.3, 0.9, 0.9, 0.07, and 0.2). Inter- and intra-reader reliabilities were excellent to moderate for femoral head and bump volumes (ICC = 0.85, 0.52, and 0.8, 0.5) and moderate to poor for alpha angles (ICC = 0.48, 0.40, 0.05, 0.25 and 0.3, 0.24, 0.29, 0.49). CONCLUSION: Three dimensional volumetric measurements of cam-type FAI significantly correlate with the extent of intraoperative labral tears. Superior inter- and intra-reader reliability to that of alpha angles renders it a more clinically relevant measurement for quantifying cam morphology. KEY POINTS: • The 3DCT bump volume and femoral head volume showed significant correlations with the extent of labral tear (p values = 0.008 and 0.003). • No significant correlations were seen between alpha angles and the extent of labral tear (p values > 0.05). • Inter- and intra-reader reliability was excellent to moderate (ICC = 0.85 and 0.52, 0.8, and 0.5) for femoral head and bump volumes while inter- and intra-reader reliability was fair to poor (ICC = 0.48, 0.40, 0.05, 0.25 and 0.3, 0.24, 0.29, 0.49) for alpha angles.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.