Exploring the relationship between AM-PAC scores and mobility components in falls and pressure injury risk assessment tools: A pathway to improve nursing clinical efficiency.

BACKGROUND: Nurses routinely perform multiple risk assessments related to patient mobility in the hospital. Use of a single mobility assessment for multiple risk assessment tools could improve clinical documentation efficiency, accuracy and lay the groundwork for automated risk evaluation tools. PURPOSE: We tested how accurately Activity Measure for Post-Acute Care (AM-PAC) mobility scores predicted the mobility components of various fall and pressure injury risk assessment tools. METHOD: AM-PAC scores along with mobility and physical activity components on risk assessments (Braden Scale, Get Up and Go used within the Hendrich II Fall Risk Model®, Johns Hopkins Fall Risk Assessment Tool (JHFRAT) and Morse Fall Scale) were collected on a cohort of hospitalised patients. We predicted scores of risk assessments based on AM-PAC scores by fitting of ordinal logistic regressions between AM-PAC scores and risk assessments. STROBE checklist was used to report the present study. FINDINGS: AM-PAC scores predicted the observed mobility components of Braden, Get Up and Go and JHFRAT with high accuracy (≥85%), but with lower accuracy for the Morse Fall Scale (40%). DISCUSSION: These findings suggest that a single mobility assessment has the potential to be a good solution for the mobility components of several fall and pressure injury risk assessments.

Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change.

Gait dysfunction is common in many clinical populations and often has a profound and deleterious impact on independence and quality of life. Gait analysis is a foundational component of rehabilitation because it is critical to identify and understand the specific deficits that should be targeted prior to the initiation of treatment. Unfortunately, current state-of-the-art approaches to gait analysis (e.g., marker-based motion capture systems, instrumented gait mats) are largely inaccessible due to prohibitive costs of time, money, and effort required to perform the assessments. Here, we demonstrate the ability to perform quantitative gait analyses in multiple clinical populations using only simple videos recorded using low-cost devices (tablets). We report four primary advances: 1) a novel, versatile workflow that leverages an open-source human pose estimation algorithm (OpenPose) to perform gait analyses using videos recorded from multiple different perspectives (e.g., frontal, sagittal), 2) validation of this workflow in three different populations of participants (adults without gait impairment, persons post-stroke, and persons with Parkinson's disease) via comparison to ground-truth three-dimensional motion capture, 3) demonstration of the ability to capture clinically relevant, condition-specific gait parameters, and 4) tracking of within-participant changes in gait, as is required to measure progress in rehabilitation and recovery. Importantly, our workflow has been made freely available and does not require prior gait analysis expertise. The ability to perform quantitative gait analyses in nearly any setting using only low-cost devices and computer vision offers significant potential for dramatic improvement in the accessibility of clinical gait analysis across different patient populations.

Accuracy of Video-Based Gait Analysis Using Pose Estimation During Treadmill Walking Versus Overground Walking in Persons After Stroke.

OBJECTIVE: Video-based pose estimation is an emerging technology that shows significant promise for improving clinical gait analysis by enabling quantitative movement analysis with little costs of money, time, or effort. The objective of this study is to determine the accuracy of pose estimation-based gait analysis when video recordings are constrained to 3 common clinical or in-home settings (ie, frontal and sagittal views of overground walking and sagittal views of treadmill walking). METHODS: Simultaneous video and motion capture recordings were collected from 30 persons after stroke during overground and treadmill walking. Spatiotemporal and kinematic gait parameters were calculated from videos using an open-source human pose estimation algorithm and from motion capture data using traditional gait analysis. Repeated-measures analyses of variance were then used to assess the accuracy of the pose estimation-based gait analysis across the different settings, and the authors examined Pearson and intraclass correlations with ground-truth motion capture data. RESULTS: Sagittal videos of overground and treadmill walking led to more accurate measurements of spatiotemporal gait parameters versus frontal videos of overground walking. Sagittal videos of overground walking resulted in the strongest correlations between video-based and motion capture measurements of lower extremity joint kinematics. Video-based measurements of hip and knee kinematics showed stronger correlations with motion capture versus ankle kinematics for both overground and treadmill walking. CONCLUSION: Video-based gait analysis using pose estimation provides accurate measurements of step length, step time, and hip and knee kinematics during overground and treadmill walking in persons after stroke. Generally, sagittal videos of overground gait provide the most accurate results. IMPACT: Many clinicians lack access to expensive gait analysis tools that can help identify patient-specific gait deviations and guide therapy decisions. These findings show that video-based methods that require only common household devices provide accurate measurements of a variety of gait parameters in persons after stroke and could make quantitative gait analysis significantly more accessible.

Simple within-stride changes in treadmill speed can drive selective changes in human gait symmetry.

Millions of people walk with asymmetric gait patterns, highlighting a need for customizable rehabilitation approaches that can flexibly target different aspects of gait asymmetry. Here, we studied how simple within-stride changes in treadmill speed could drive selective changes in gait symmetry. In Experiment 1, healthy adults (n = 10) walked on an instrumented treadmill with and without a closed-loop controller engaged. This controller changed the treadmill speed to 1.50 or 0.75 m/s depending on whether the right or left leg generated propulsive ground reaction forces, respectively. Participants walked asymmetrically when the controller was engaged: the leg that accelerated during propulsion (right) showed smaller leading limb angles, larger trailing limb angles, and smaller propulsive forces than the leg that decelerated (left). In Experiment 2, healthy adults (n = 10) walked on the treadmill with and without an open-loop controller engaged. This controller changed the treadmill speed to 1.50 or 0.75 m/s at a prescribed time interval while a metronome guided participants to step at different time points relative to the speed change. Different patterns of gait asymmetry emerged depending on the timing of the speed change: step times, leading limb angles, and peak propulsion were asymmetric when the speed changed early in stance while step lengths, step times, and propulsion impulses were asymmetric when the speed changed later in stance. In sum, we show that simple manipulations of treadmill speed can drive selective changes in gait symmetry. Future work will explore the potential for this technique to restore gait symmetry in clinical populations.

Opportunities for Improving Motor Assessment and Rehabilitation After Stroke by Leveraging Video-Based Pose Estimation.

ABSTRACT: Stroke is a leading cause of long-term disability in adults in the United States. As the healthcare system moves further into an era of digital medicine and remote monitoring, technology continues to play an increasingly important role in post-stroke care. In this Analysis and Perspective article, opportunities for using human pose estimation-an emerging technology that uses artificial intelligence to track human movement kinematics from simple videos recorded using household devices (e.g., smartphones, tablets)-to improve motor assessment and rehabilitation after stroke are discussed. The focus is on the potential of two key applications: (1) improving access to quantitative, objective motor assessment and (2) advancing telerehabilitation for persons post-stroke.

Neurologic music therapy combined with EEG-tDCS for upper motor extremity performance in patients with corticobasal syndrome: Study protocol for a novel approach.

BACKGROUND: Corticobasal syndrome (CBS) is an atypical parkinsonian disorder that involves degeneration of brain regions associated with motor coordination and sensory processing. Combining transcranial direct current stimulation (tDCS) with rehabilitation training has been shown to improve upper-limb performance in other disease models. Here, we describe the protocol investigating whether tDCS with neurologic music therapy (NMT) (patterned sensory enhancement and therapeutic instrumental music performance) enhances functional arm/hand performance in individuals with CBS. METHODS: Study participants are randomly assigned to six 30-min sessions (twice per week for 3 weeks) of NMT + either sham tDCS or active tDCS. We aim to stimulate the frontoparietal cortex, which is associated with movement execution/coordination and sensory processing. The hemisphere contralateral to the more affected arm is stimulated (total stimulation current of 2 mA from 5 dime-sized electrodes). Individualized NMT sessions designed to exercise the upper limb are provided. Participants undergo gross/fine motor, cognitive and emotional assessments at baseline and follow-up (one month after the final session). To investigate the immediate effects of tDCS and NMT training, gross /fine motor, affective level, and kinematic parameter measurements using motion sensors are collected before and after each session. Electroencephalography is used to collect electrical neurophysiological responses before, during, and after tDCS+NMT sessions. The study participants, neurologic music therapist and outcome assessor are blinded to whether participants are in the sham or active tDCS group. CONCLUSION: This noninvasive and patient-centered clinical trial for CBS may provide insight into rehabilitation options that are sorely lacking in this population.

Video-based quantification of human movement frequency using pose estimation: A pilot study.

Assessment of repetitive movements (e.g., finger tapping) is a hallmark of motor examinations in several neurologic populations. These assessments are traditionally performed by a human rater via visual inspection; however, advances in computer vision offer potential for remote, quantitative assessment using simple video recordings. Here, we evaluated a pose estimation approach for measurement of human movement frequency from smartphone videos. Ten healthy young participants provided videos of themselves performing five repetitive movement tasks (finger tapping, hand open/close, hand pronation/supination, toe tapping, leg agility) at four target frequencies (1-4 Hz). We assessed the ability of a workflow that incorporated OpenPose (a freely available whole-body pose estimation algorithm) to estimate movement frequencies by comparing against manual frame-by-frame (i.e., ground-truth) measurements for all tasks and target frequencies using repeated measures ANOVA, Pearson's correlations, and intraclass correlations. Our workflow produced largely accurate estimates of movement frequencies; only the hand open/close task showed a significant difference in the frequencies estimated by pose estimation and manual measurement (while statistically significant, these differences were small in magnitude). All other tasks and frequencies showed no significant differences between pose estimation and manual measurement. Pose estimation-based detections of individual events (e.g., finger taps, hand closures) showed strong correlations (all r>0.99) with manual detections for all tasks and frequencies. In summary, our pose estimation-based workflow accurately tracked repetitive movements in healthy adults across a range of tasks and movement frequencies. Future work will test this approach as a fast, quantitative, video-based approach to assessment of repetitive movements in clinical populations.

Applications of Pose Estimation in Human Health and Performance across the Lifespan.

The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient's home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations.

Disentangling the energetic costs of step time asymmetry and step length asymmetry in human walking.

The metabolic cost of walking in healthy individuals increases with spatiotemporal gait asymmetries. Pathological gait, such as post-stroke, often has asymmetry in step length and step time which may contribute to an increased energy cost. But paradoxically, enforcing step length symmetry does not reduce metabolic cost of post-stroke walking. The isolated and interacting costs of asymmetry in step time and step length remain unclear, because previous studies did not simultaneously enforce spatial and temporal gait asymmetries. Here, we delineate the isolated costs of asymmetry in step time and step length in healthy human walking. We first show that the cost of step length asymmetry is predicted by the cost of taking two non-preferred step lengths (one short and one long), but that step time asymmetry adds an extra cost beyond the cost of non-preferred step times. The metabolic power of step time asymmetry is about 2.5 times greater than the cost of step length asymmetry. Furthermore, the costs are not additive when walking with asymmetric step time and asymmetric step length: the metabolic power of concurrent asymmetry in step length and step time is driven by the cost of step time asymmetry alone. The metabolic power of asymmetry is explained by positive mechanical power produced during single support phases to compensate for a net loss of center of mass power incurred during double support phases. These data may explain why metabolic cost remains invariant to step length asymmetry in post-stroke walking and suggest how effects of asymmetry on energy cost can be attenuated.

Two-dimensional video-based analysis of human gait using pose estimation.

Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s-1. Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for human gait analysis.

Unilateral step training can drive faster learning of novel gait patterns.

Humans are capable of learning many new walking patterns. People have learned to snowshoe up mountains, racewalk marathons, and march in precise synchrony. But what is required to learn a new walking pattern? Here, we demonstrate that people can learn new walking patterns without actually walking. Through a series of experiments, we observe that stepping with only one leg can facilitate learning of an entirely new walking pattern (i.e., split-belt treadmill walking). We find that the nervous system learns from the relative speed difference between the legs-whether or not both legs are moving-and can transfer this learning to novel gaits. We also show that locomotor learning requires active movement: observing another person adapt their gait did not result in significantly faster learning. These findings reveal that people can learn new walking patterns without bilateral gait training, as stepping with one leg can facilitate adaptive learning that transfers to novel gait patterns.

Step time asymmetry but not step length asymmetry is adapted to optimize energy cost of split-belt treadmill walking.

KEY POINTS: The relationship between spatiotemporal gait asymmetry and walking energetics is currently under debate. The split-belt treadmill paradigm has been used to study adaptation of spatiotemporal gait parameters in relation to energetics, but it remains unclear why people reduce asymmetry in step lengths, but prefer asymmetry in step times. In this study we characterized the effects of step time asymmetry and step length asymmetry on energy cost during steady-state walking on a split-belt treadmill at increasing speed-differences. Both the optimal and preferred step time asymmetry increased with greater speed differences, while preferred step lengths remained constant and nearly symmetric. Preferred asymmetric step times were energetically optimal across all speed-difference conditions, while preferred step length asymmetry was not optimal. The findings show that humans will adopt an asymmetric gait that is associated with an energy reduction and suggest that step time asymmetry plays a dominant role in shaping the energetic cost of gait asymmetry. ABSTRACT: Healthy human walking is symmetric and economical; hemiparetic and amputee gait is often asymmetric and requires more energy. Consequently, asymmetry has been attributed to account for the added energy cost of pathological gait. But it is also possible that asymmetric gait may be adopted if it is energetically optimal under certain biomechanical and neurological constraints of the locomotor system. Here, we assessed how preferred asymmetry in step times and step lengths of healthy human gait is adapted during split-belt treadmill walking and tested the hypothesis that asymmetry is adapted to optimize metabolic energy cost. Ten healthy, young participants walked on a split-belt treadmill in three conditions in which the average belt speed was always 1.25 m s-1 and the speed difference between the belts was 0.5 m s-1 , 1.0 m s-1 and 1.5 m s-1 while a range of values of step time asymmetry and step length asymmetry were enforced. We found that preferred step time asymmetry increased with greater speed differences while preferred step length asymmetry remained constant and nearly symmetric. With increasing speed differences participants increased their preferred value of step time asymmetry to coincide with the lowest energy cost. However, our results show that preferred step length asymmetry was not optimal even with extensive experience of split-belt treadmill walking. Overall, our results indicate that humans will adopt an asymmetric gait that is associated with an energy reduction and suggest that step time asymmetry plays a dominant role in shaping the energetic cost of gait asymmetry.

Local dynamic stability during treadmill walking can detect children with developmental coordination disorder.

OBJECTIVE: Developmental coordination disorder (DCD) is an innate impairment of motor coordination that affects basic locomotion and balance. This study investigated local dynamic stability of trunk accelerations during treadmill walking as an objective evaluation of gait stability and the sensitivity and specificity of this measure to discriminate children with DCD from typically developing children. METHOD: Eight children with DCD and ten age- and gender-matched typically developing children (TD) walked four minutes on a treadmill. Trunk accelerations in vertical, medio-lateral and anterior-posterior directions were recorded with a sternum mounted accelerometer at 256Hz. Short term local dynamic stability (λs), root mean square (RMS) and relative root mean square (RMSR) were calculated from measures of orthogonal trunk accelerations. Receiver operating characteristic curve (ROC) analysis was performed to discriminate between groups based on short term local dynamic stability. RESULTS: λs was significantly greater in children with DCD in the main movement direction (AP) (DCD: 1.69±0.17 λs; TD:1.41±0.17 λs; p=0.005), indicating reduced local dynamic stability. RMS and RMSR accelerations showed no difference between children with DCD and TD children in any direction. The ROC analysis of λs in separate directions and in two dimensions showed an excellent accuracy of discriminating between children with DCD and TD children. Anterior-posterior direction in combination with medio-lateral or vertical showed best performance with an area under the curve (AUC) of 0.91. CONCLUSION: We have shown that children with developmental coordination disorder have general reduced local dynamic stability and that the short term Lyapunov exponent has good power of discrimination between DCD and TD.

Neuromuscular effort predicts walk-run transition speed in normal and adapted human gaits.

Often, humans and other animals move in a manner that minimizes energy costs. It is more economical to walk at slow speeds, and to run at fast speeds. Here, we asked whether humans select a gait that minimizes neuromuscular effort under novel and unfamiliar conditions, by imposing interlimb asymmetry during split-belt treadmill locomotion. The walk-run transition speed changed markedly across different gait conditions: forward, backward, hybrid (one leg forward, one leg backward) and forward with speed differences (one leg faster than the other). Most importantly, we showed that the human walk-run transition speed across conditions was predicted by changes in neuromuscular effort (i.e. summed leg muscle activations). Our results for forward gait and forward gait with speed differences suggest that human locomotor patterns are optimized under both familiar and novel gait conditions by minimizing the motor command for leg muscle activation.

The effect of walking speed on local dynamic stability is sensitive to calculation methods.

Local dynamic stability has been assessed by the short-term local divergence exponent (λS), which quantifies the average rate of logarithmic divergence of infinitesimally close trajectories in state space. Both increased and decreased local dynamic stability at faster walking speeds have been reported. This might pertain to methodological differences in calculating λS. Therefore, the aim was to test if different calculation methods would induce different effects of walking speed on local dynamic stability. Ten young healthy participants walked on a treadmill at five speeds (60%, 80%, 100%, 120% and 140% of preferred walking speed) for 3min each, while upper body accelerations in three directions were sampled. From these time-series, λS was calculated by three different methods using: (a) a fixed time interval and expressed as logarithmic divergence per stride-time (λS-a), (b) a fixed number of strides and expressed as logarithmic divergence per time (λS-b) and (c) a fixed number of strides and expressed as logarithmic divergence per stride-time (λS-c). Mean preferred walking speed was 1.16±0.09m/s. There was only a minor effect of walking speed on λS-a. λS-b increased with increasing walking speed indicating decreased local dynamic stability at faster walking speeds, whereas λS-c decreased with increasing walking speed indicating increased local dynamic stability at faster walking speeds. Thus, the effect of walking speed on calculated local dynamic stability was significantly different between methods used to calculate local dynamic stability. Therefore, inferences and comparisons of studies employing λS should be made with careful consideration of the calculation method.