The Black Box of Technological Outcome Measures: An Example in Duchenne Muscular Dystrophy.

BACKGROUND: Outcome measures for non-ambulant Duchenne muscular dystrophy (DMD) patients are limited, with only the Performance of the Upper Limb (PUL) approved as endpoint for clinical trials. OBJECTIVE: We assessed four outcome measures based on devices developed for the gaming industry, aiming to overcome disadvantages of observer-dependency and motivation. METHODS: Twenty-two non-ambulant DMD patients (range 8.6-24.1 years) and 14 healthy controls (HC; range 9.5-25.4 years) were studied at baseline and 16 patients at 12 months using Leap Motion to quantify wrist/hand active range of motion (aROM) and a Kinect sensor for reached volume with Ability Captured Through Interactive Video Evaluation (ACTIVE), Functional Workspace (FWS) summed distance to seven upper extremity body points, and trunk compensation (KinectTC). PUL 2.0 was performed in patients only. A stepwise approach assessed quality control, construct validity, reliability, concurrent validity, longitudinal change and patient perception. RESULTS: Leap Motion aROM distinguished patients and HCs for supination, radial deviation and wrist flexion (range p = 0.006 to <0.001). Reliability was low and the manufacturer's hand model did not match the sensor's depth images. ACTIVE differed between patients and HCs (p < 0.001), correlated with PUL (rho = 0.76), and decreased over time (p = 0.030) with a standardized response mean (SRM) of -0.61. It was appraised as fun on a 10-point numeric rating scale (median 9/10). PUL decreased over time (p < 0.001) with an SRM of -1.28, and was appraised as fun (median 7/10). FWS summed distance distinguished patients and HCs (p < 0.001), but reliability in patients was insufficient. KinectTC differed between patients and HCs (p < 0.01), but correlated insufficiently with PUL (rho = -0.69). CONCLUSIONS: Only ACTIVE qualified as potential outcome measure in non-ambulant DMD patients, although the SRM was below the commonly used threshold of 0.8. Lack of insight in technological constraints due to intellectual property and software updates made the technology behind these outcome measures a kind of black box that could jeopardize long-term use in clinical development.

Compensatory biomechanics and spinal loading during dynamic maneuvers in patients with chronic low back pain.

PURPOSE: This study explores the biomechanics underlying the sit-to-stand (STS) functional maneuver in chronic LBP patients to understand how different spinal disorders and levels of pain severity relate to unique compensatory biomechanical behaviors. This work stands to further our understanding of the relationship between spinal loading and symptoms in LBP patients. METHODS: We collected in-clinic motion data from 44 non-specific LBP (NS-LBP) and 42 spinal deformity LBP (SD-LBP) patients during routine clinical visits. An RGB-depth camera tracked 3D joint positions from the frontal view during unassisted, repeated STS maneuvers. Patient-reported outcomes (PROs) for back pain (VAS) and low back disability (ODI) were collected during the same clinical visit. RESULTS: Between patient groups, SD-LBP patients had 14.3% greater dynamic sagittal vertical alignment (dSVA) and 10.1% greater peak spine torque compared to NS-LBP patients (p < 0.001). SD-LBP patients also had 11.8% greater hip torque (p < 0.001) and 86.7% greater knee torque (p = 0.04) compared to NS-LBP patients. There were no significant differences between patient groups in regard to anterior or vertical torso velocities, but anterior and vertical torso velocities correlated with both VAS (r = - 0.38, p < 0.001) and ODI (r = - 0.29, p = 0.01). PROs did not correlate with other variables. CONCLUSION: Patients with LBP differ in movement biomechanics during an STS transfer as severity of symptoms may relate to different compensatory strategies that affect spinal loading. Further research aims to establish relationships between movement and PROs and to inform targeted rehabilitation approaches.

Unsupervised Machine Learning on Motion Capture Data Uncovers Movement Strategies in Low Back Pain.

Chronic low back pain (LBP) is a leading cause of disability and opioid prescriptions worldwide, representing a significant medical and socioeconomic problem. Clinical heterogeneity of LBP limits accurate diagnosis and precise treatment planning, culminating in poor patient outcomes. A current priority of LBP research is the development of objective, multidimensional assessment tools that subgroup LBP patients based on neurobiological pain mechanisms, to facilitate matching patients with the optimal therapies. Using unsupervised machine learning on full body biomechanics, including kinematics, dynamics, and muscle forces, captured with a marker-less depth camera, this study identified a forward-leaning sit-to-stand strategy (STS) as a discriminating movement biomarker for LBP subjects. A forward-leaning STS strategy, as opposed to a vertical rise strategy seen in the control participants, is less efficient and results in increased spinal loads. Inefficient STS with the subsequent higher spinal loading may be a biomarker of poor motor control in LBP patients as well as a potential source of the ongoing symptomology.

Point-of-care motion capture and biomechanical assessment improve clinical utility of dynamic balance testing for lower extremity osteoarthritis.

Musculoskeletal conditions impede patient biomechanical function. However, clinicians rely on subjective functional assessments with poor test characteristics for biomechanical outcomes because more advanced assessments are impractical in the ambulatory care setting. Using markerless motion capture (MMC) in clinic to record time-series joint position data, we implemented a spatiotemporal assessment of patient kinematics during lower extremity functional testing to evaluate whether kinematic models could identify disease states beyond conventional clinical scoring. 213 trials of the star excursion balance test (SEBT) were recorded by 36 subjects during routine ambulatory clinic visits using both MMC technology and conventional clinician scoring. Conventional clinical scoring failed to distinguish patients with symptomatic lower extremity osteoarthritis (OA) from healthy controls in each component of the assessment. However, principal component analysis of shape models generated from MMC recordings revealed significant differences in subject posture between the OA and control cohorts for six of the eight components. Additionally, time-series models of subject posture change over time revealed distinct movement patterns and reduced overall postural change in the OA cohort compared to the controls. Finally, a novel metric quantifying postural control was derived from subject specific kinematic models and was shown to distinguish OA (1.69), asymptomatic postoperative (1.27), and control (1.23) cohorts (p = 0.0025) and to correlate with patient-reported OA symptom severity (R = -0.72, p = 0.018). Time series motion data have superior discriminative validity and clinical utility than conventional functional assessments in the case of the SEBT. Novel spatiotemporal assessment approaches can enable routine in-clinic collection of objective patient-specific biomechanical data for clinical decision-making and monitoring recovery.

Automated assessment and classification of spine, hip, and knee pathologies from sit-to-stand movements collected in clinical practice.

Efficient, cost-effective methods for quantifying patient biomechanics at the point of care can facilitate faster and more accurate diagnoses. This work presents a new method to diagnose pre-surgical back, hip, and knee patients by analysing their sit-to-stand motion captured by a Kinect camera. Kinematic and dynamic time-series features were extracted from patient movements collected in clinic. These features were used to test a variety of machine learning methods for patient classification. The performance of models trained on time-series features were compared against models trained on domain-knowledge features, highlighting the importance of using time-series data for the classification of human movement. Additionally, the effectiveness of using semi-supervised learning is tested on partially labelled datasets, providing insight on how to boost classification performance in situations where labelled patient data is difficult to obtain. The best semi-supervised model achieves ∼73% accuracy in distinguishing individuals with low-back pain, and hip and knee degeneration from control subjects.

Reachable Workspace and Proximal Function Measures for Quantifying Upper Limb Motion.

There are a lack of quantitative measures for clinically assessing upper limb function. Conventional biomechanical performance measures are restricted to specialist labs due to hardware cost and complexity, while the resulting measurements require specialists for analysis. Depth cameras are low cost and portable systems that can track surrogate joint positions. However, these motions may not be biologically consistent, which can result in noisy, inaccurate movements. This paper introduces a rigid body modelling method to enforce biological feasibility of the recovered motions. This method is evaluated on an existing depth camera assessment: the reachable workspace (RW) measure for assessing gross shoulder function. As a rigid body model is used, position estimates of new proximal targets can be added, resulting in a proximal function (PF) measure for assessing a subject's ability to touch specific body landmarks. The accuracy, and repeatability of these measures is assessed on ten asymptomatic subjects, with and without rigid body constraints. This analysis is performed both on a low-cost depth camera system and a gold-standard active motion capture system. The addition of rigid body constraints was found to improve accuracy and concordance of the depth camera system, particularly in lateral reaching movements. Both RW and PF measures were found to be feasible candidates for clinical assessment, with future analysis needed to determine their ability to detect changes within specific patient populations.

ISSLS PRIZE IN BIOENGINEERING SCIENCE 2019: biomechanical changes in dynamic sagittal balance and lower limb compensatory strategies following realignment surgery in adult spinal deformity patients.

STUDY DESIGN: A longitudinal cohort study. OBJECTIVE: To define a set of objective biomechanical metrics that are representative of adult spinal deformity (ASD) post-surgical outcomes and that may forecast post-surgical mechanical complications. Current outcomes for ASD surgical planning and post-surgical assessment are limited to static radiographic alignment and patient-reported questionnaires. Little is known about the compensatory biomechanical strategies for stabilizing sagittal balance during functional movements in ASD patients. METHODS: We collected in-clinic motion data from 15 ASD patients and 10 controls during an unassisted sit-to-stand (STS) functional maneuver. Joint motions were measured using noninvasive 3D depth mapping sensor technology. Mathematical methods were used to attain high-fidelity joint-position tracking for biomechanical modeling. This approach provided reliable measurements for biomechanical behaviors at the spine, hip, and knee. These included peak sagittal vertical axis (SVA) over the course of the STS, as well as forces and muscular moments at various joints. We compared changes in dynamic sagittal balance (DSB) metrics between pre- and post-surgery and then separately compared pre- and post-surgical data to controls. RESULTS: Standard radiographic and patient-reported outcomes significantly improved following realignment surgery. From the DSB biomechanical metrics, peak SVA and biomechanical loads and muscular forces on the lower lumbar spine significantly reduced following surgery (- 19 to - 30%, all p < 0.05). In addition, as SVA improved, hip moments decreased (- 28 to - 65%, all p < 0.05) and knee moments increased (+ 7 to + 28%, p < 0.05), indicating changes in lower limb compensatory strategies. After surgery, DSB data approached values from the controls, with some post-surgical metrics becoming statistically equivalent to controls. CONCLUSIONS: Longitudinal changes in DSB following successful multi-level spinal realignment indicate reduced forces on the lower lumbar spine along with altered lower limb dynamics matching that of controls. Inadequate improvement in DSB may indicate increased risk of post-surgical mechanical failure. These slides can be retrieved under Electronic Supplementary Material.

Simple Spline Representation for Identifying Sit-to-Stand Strategies.

Standing from a seated position is an activity of daily living and a common clinical test of strength and balance. While this action is well-studied biomechanically, there remains a need for a clear modelling method for appropriately capturing performance and discriminating between standing strategies. This paper presents a simple framework for representing the rise from a chair as a set of splines. This formulation is inherently differentiable, defines a clear start and end point of the motion, and allows for secondary analysis of dynamic and energetic effects. This method is tested on two healthy subjects performing four different standing strategies. The spline method was found to accurately capture the standing action, with mean absolute errors of 1-2 cm for joint position, and 2-3 degrees angular error across the different standing strategies. Analysis of the spline trajectories revealed strategy-specific differences in kinematic, kinetic, and dynamic bio-markers. This suggests that low order splines can be used to accurately capture variations in sit-to-stand actions.

Passive Knee Assistance Affects Whole-Body Biomechanics During Sit-to-Stand.

The sit-to-stand (STS) motion is an activity of daily living which requires significant torque generation and coordinated movement at multiple joints. It is therefore important to consider the whole-body biomechanics when designing an assistive device for STS. In this study, a passive elastic orthotic was developed which provides bilateral knee extension assistance. Initial human experiments were conducted with two subjects under two foot-placement conditions. The human and device kinematics and dynamics were modelled, allowing for the assessment of the biomechanical effects of the device. The assistance resulted in a decrease in the human knee torque as well as changes in whole-body biomechanics, notably an increase in the linear momentum of the upper body and a decrease in the anterior excursion of the center of mass. These results indicate that single-joint assistance at the knee has the potential to both facilitate successful STS and positively alter whole-body biomechanics.