Publications by authors named "Scott Delp"

202 Publications

Muscle coordination retraining inspired by musculoskeletal simulations reduces knee contact force.

Sci Rep 2022 Jul 7;12(1):9842. Epub 2022 Jul 7.

Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA.

Humans typically coordinate their muscles to meet movement objectives like minimizing energy expenditure. In the presence of pathology, new objectives gain importance, like reducing loading in an osteoarthritic joint, but people often do not change their muscle coordination patterns to meet these new objectives. Here we use musculoskeletal simulations to identify simple changes in coordination that can be taught using electromyographic biofeedback, achieving the therapeutic goal of reducing joint loading. Our simulations predicted that changing the relative activation of two redundant ankle plantarflexor muscles-the gastrocnemius and soleus-could reduce knee contact force during walking, but it was unclear whether humans could re-coordinate redundant muscles during a complex task like walking. Our experiments showed that after a single session of walking with biofeedback of summary measures of plantarflexor muscle activation, healthy individuals reduced the ratio of gastrocnemius-to-soleus muscle activation by 25 ± 15% (p = 0.004, paired t test, n = 10). Participants who walked with this "gastrocnemius avoidance" gait pattern reduced late-stance knee contact force by 12 ± 12% (p = 0.029, paired t test, n = 8). Simulation-informed coordination retraining could be a promising treatment for knee osteoarthritis and a powerful tool for optimizing coordination for a variety of rehabilitation and performance applications.
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http://dx.doi.org/10.1038/s41598-022-13386-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262899PMC
July 2022

Changes in foot progression angle during gait reduce the knee adduction moment and do not increase hip moments in individuals with knee osteoarthritis.

J Biomech 2022 Jun 20;141:111204. Epub 2022 Jun 20.

Department of Mechanical Engineering, Stanford University, Stanford, CA, United States; Department of Bioengineering, Stanford University, Stanford, CA, United States; Department of Orthopaedic Surgery, Stanford University, Stanford, CA, United States.

People with knee osteoarthritis who adopt a modified foot progression angle (FPA) during gait often benefit from a reduction in the knee adduction moment. It is unknown, however, whether changes in the FPA increase hip moments, a surrogate measure of hip loading, which will increase the mechanical demand on the joint. This study examined how altering the FPA affects hip moments. Individuals with knee osteoarthritis walked on an instrumented treadmill with their baseline gait, 10° toe-in gait, and 10° toe-out gait. A musculoskeletal modeling package was used to compute joint moments from the experimental data. Fifty participants were selected from a larger study who reduced their peak knee adduction moment with a modified FPA. In this group, participants reduced the first peak of the knee adduction moment by 7.6% with 10° toe-in gait and reduced the second peak by 11.0% with 10° toe-out gait. Modifying the FPA reduced the early-stance hip abduction moment, at the time of peak hip contact force, by 4.3% ± 1.3% for 10° toe-in gait (p = 0.005, d = 0.49) and by 4.6% ± 1.1% for 10° toe-out gait (p < 0.001, d = 0.59) without increasing the flexion and internal rotation moments (p > 0.15). Additionally, 74% of individuals reduced their total hip moment at time of peak hip contact force with a modified FPA. In summary, when adopting a FPA modification that reduced the knee adduction moment, participants, on average, did not increase surrogate measures of hip loading.
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http://dx.doi.org/10.1016/j.jbiomech.2022.111204DOI Listing
June 2022

Medical and Biomechanical Risk Factors for Incident Bone Stress Injury in Collegiate Runners: Can Plantar Pressure Predict Injury?

Orthop J Sports Med 2022 Jun 15;10(6):23259671221104793. Epub 2022 Jun 15.

Stanford Medical Center, Redwood City, California, USA.

Background: Bone stress injury (BSI) is a common reason for missed practices and competitions in elite track and field runners.

Hypothesis: It was hypothesized that, after accounting for medical risk factors, higher plantar loading during running, walking, and athletic movements would predict the risk of future BSI in elite collegiate runners.

Study Design: Cohort study; Level of evidence, 2.

Methods: A total of 39 elite collegiate runners (24 male, 15 female) were evaluated during the 2014-2015 academic year to determine the degree to which plantar pressure data and medical history (including Female and Male Athlete Triad risk factors) could predict subsequent BSI. Runners completed athletic movements while plantar pressures and contact areas in 7 key areas of the foot were recorded, and the measurements were reported overall and by specific foot area. Regression models were constructed to determine factors related to incident BSI.

Results: Twenty-one runners (12 male, 9 female) sustained ≥1 incident BSI during the study period. Four regression models incorporating both plantar pressure measurements and medical risk factors were able to predict the subsequent occurrence of (A) BSIs in female runners, (B) BSIs in male runners, (C) multiple BSIs in either male or female runners, and (D) foot BSIs in female runners. Model A used maximum mean pressure (MMP) under the first metatarsal during a jump takeoff and only misclassified 1 female with no BSI. Model B used increased impulses under the hindfoot and second through fifth distal metatarsals while walking, and under the lesser toes during a cutting task, correctly categorizing 83.3% of male runners. Model C used higher medial midfoot peak pressure during a shuttle run and triad cumulative risk scores and correctly categorized 93.3% of runners who did not incur multiple BSIs and 66.7% of those who did. Model D included lower hindfoot impulses in the shuttle run and higher first metatarsal MMP during treadmill walking to correctly predict the subsequent occurrence of a foot BSI for 75% of women and 100% without.

Conclusion: The models collectively suggested that higher plantar pressure may contribute to risk for BSI.
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http://dx.doi.org/10.1177/23259671221104793DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208063PMC
June 2022

Running in the wild: Energetics explain ecological running speeds.

Curr Biol 2022 May 28;32(10):2309-2315.e3. Epub 2022 Apr 28.

Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Mechanical Engineering, Stanford University, Stanford, CA, USA; Department of Orthopaedic Surgery, Stanford University, Stanford, CA, USA.

Human runners have long been thought to have the ability to consume a near-constant amount of energy per distance traveled, regardless of speed, allowing speed to be adapted to particular task demands with minimal energetic consequence. However, recent and more precise laboratory measures indicate that humans may in fact have an energy-optimal running speed. Here, we characterize runners' speeds in a free-living environment and determine if preferred speed is consistent with task- or energy-dependent objectives. We analyzed a large-scale dataset of free-living runners, which was collected via a commercial fitness tracking device, and found that individual runners preferred a particular speed that did not change across commonly run distances. We compared the data from lab experiments that measured participants' energy-optimal running speeds with the free-living preferred speeds of age- and gender-matched runners in our dataset and found the speeds to be indistinguishable. Human runners prefer a particular running speed that is independent of task distance and is consistent with the objective of minimizing energy expenditure. Our findings offer an insight into the biological objectives that shape human running preferences in the real world-an important consideration when examining human ecology or creating training strategies to improve performance and prevent injury.
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http://dx.doi.org/10.1016/j.cub.2022.03.076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9169516PMC
May 2022

Non-invasive electrical stimulation of peripheral nerves for the management of tremor.

J Neurol Sci 2022 04 19;435:120195. Epub 2022 Feb 19.

Stanford University, Stanford, CA, USA.

Pathological tremor in patients with essential tremor and Parkinsons disease is typically treated using medication or neurosurgical interventions. There is a widely recognized need for new treatments that avoid the side effects of current medications and do not carry the risks of surgical interventions. Building on decades of research and engineering development, non-invasive electrical stimulation of peripheral nerves has emerged as a safe and effective strategy for reducing pathologic tremor in essential tremor. This review surveys the peripheral electrical stimulation (PES) literature and summarizes effectiveness, safety, clinical translatability, and hypothesized tremor-reduction mechanisms of various PES approaches. The review also proposes guidelines for assessing tremor in the context of evaluating new therapies that combine the strengths of clinician assessments, patient evaluations, and novel motion sensing technology. The review concludes with a summary of future directions for PES, including expanding clinical access for patients with Parkinson's disease and leveraging large, at-home datasets to learn more about tremor physiology and treatment effect that will better characterize the state of tremor management and accelerate discovery of new therapies. Growing evidence suggests that non-invasive electrical stimulation of afferent neural pathways provides a viable new option for management of pathological tremor, with one specific PES therapy cleared for prescription and home use, suggesting that PES be considered along with medication and neurosurgical interventions for treatment of tremor. This article is part of the Special Issue "Tremor" edited by Daniel D. Truong, Mark Hallett, and Aasef Shaikh.
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http://dx.doi.org/10.1016/j.jns.2022.120195DOI Listing
April 2022

OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations.

J Neuroeng Rehabil 2022 02 20;19(1):22. Epub 2022 Feb 20.

Department of Bioengineering, Stanford University, Stanford, CA, USA.

Background: The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate and capable of assessing and mitigating drift.

Methods: We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-min trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject's RMS differences over time.

Results: IMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3 and 6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60-0.87). We observed minimal drift in the RMS differences over 10 min; the average slopes of the linear fits to these data were near zero (- 0.14-0.17 deg/min).

Conclusions: Our workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, which may obviate the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches when studying similar activities. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.
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http://dx.doi.org/10.1186/s12984-022-01001-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8859896PMC
February 2022

Assessing inertial measurement unit locations for freezing of gait detection and patient preference.

J Neuroeng Rehabil 2022 02 13;19(1):20. Epub 2022 Feb 13.

Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA.

Background: Freezing of gait, a common symptom of Parkinson's disease, presents as sporadic episodes in which an individual's feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference.

Methods: Sixteen people with Parkinson's disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson's disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events.

Results: The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively.

Conclusions: Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.
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http://dx.doi.org/10.1186/s12984-022-00992-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842967PMC
February 2022

Mindset is associated with future physical activity and management strategies in individuals with knee osteoarthritis.

Ann Phys Rehabil Med 2022 Apr 28;65(6):101634. Epub 2022 Apr 28.

Department of Psychology, Stanford University, Stanford, USA, California.

Background: Despite the benefits of physical activity for individuals with knee osteoarthritis (KOA), physical activity levels are low in this population.

Objectives: We conducted a repeated cross-sectional study to compare mindset about physical activity among individuals with and without KOA and to investigate whether mindset relates to physical activity.

Methods: Participants with (n = 150) and without (n = 152) KOA completed an online survey at enrollment (T1). Participants with KOA repeated the survey 3 weeks later (T2; n = 62). The mindset questionnaire, scored from 1 to 4, assessed the extent to which individuals associate the process of exercising with less appeal-focused qualities (e.g., boring, painful, isolating, and depriving) versus appeal-focused (e.g., fun, pleasurable, social, and indulgent). Using linear regression, we examined the relationship between mindset and having KOA, and, in the subgroup of KOA participants, the relationship between mindset at T1 and self-reported physical activity at T2. We also compared mindset between people who use medication for management and those who use exercise.

Results: Within the KOA group, a more appeal-focused mindset was associated with higher future physical activity (β=38.72, p = 0.006) when controlling for demographics, health, and KOA symptoms. Individuals who used exercise with or without pain medication or injections had a more appeal-focused mindset than those who used medication or injections without exercise (p<0.001). A less appeal-focused mindset regarding physical activity was not significantly associated with KOA (β = -0.14, p = 0.067). Further, the mindset score demonstrated strong internal consistency (α = 0.92; T1; n = 150 and α = 0.92; T2; n = 62) and test-retest reliability (intraclass correlation coefficient (ICC) > 0.84, p < 0.001) within the KOA sample.

Conclusions: In individuals with KOA, mindset is associated with future physical activity levels and relates to the individual's management strategy. Mindset is a reliable and malleable construct and may be a valuable target for increasing physical activity and improving adherence to rehabilitation strategies involving exercise among individuals with KOA.
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http://dx.doi.org/10.1016/j.rehab.2022.101634DOI Listing
April 2022

Coupled exoskeleton assistance simplifies control and maintains metabolic benefits: A simulation study.

PLoS One 2022;17(1):e0261318. Epub 2022 Jan 5.

Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America.

Assistive exoskeletons can reduce the metabolic cost of walking, and recent advances in exoskeleton device design and control have resulted in large metabolic savings. Most exoskeleton devices provide assistance at either the ankle or hip. Exoskeletons that assist multiple joints have the potential to provide greater metabolic savings, but can require many actuators and complicated controllers, making it difficult to design effective assistance. Coupled assistance, when two or more joints are assisted using one actuator or control signal, could reduce control dimensionality while retaining metabolic benefits. However, it is unknown which combinations of assisted joints are most promising and if there are negative consequences associated with coupled assistance. Since designing assistance with human experiments is expensive and time-consuming, we used musculoskeletal simulation to evaluate metabolic savings from multi-joint assistance and identify promising joint combinations. We generated 2D muscle-driven simulations of walking while simultaneously optimizing control strategies for simulated lower-limb exoskeleton assistive devices to minimize metabolic cost. Each device provided assistance either at a single joint or at multiple joints using massless, ideal actuators. To assess if control could be simplified for multi-joint exoskeletons, we simulated different control strategies in which the torque provided at each joint was either controlled independently or coupled between joints. We compared the predicted optimal torque profiles and changes in muscle and total metabolic power consumption across the single joint and multi-joint assistance strategies. We found multi-joint devices-whether independent or coupled-provided 50% greater metabolic savings than single joint devices. The coupled multi-joint devices were able to achieve most of the metabolic savings produced by independently-controlled multi-joint devices. Our results indicate that device designers could simplify multi-joint exoskeleton designs by reducing the number of torque control parameters through coupling, while still maintaining large reductions in metabolic cost.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0261318PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730392PMC
February 2022

Biceps femoris long head sarcomere and fascicle length adaptations after 3 weeks of eccentric exercise training.

J Sport Health Sci 2022 01 9;11(1):43-49. Epub 2021 Sep 9.

School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, QLD 4072, Australia.

Background: Eccentric exercise increases muscle fascicle lengths; however, the mechanisms behind this adaptation are still unknown. This study aimed to determine whether biceps femoris long head (BFlh) fascicle length increases in response to 3 weeks of eccentric exercise training are the result of an in-series addition of sarcomeres within the muscle fibers.

Methods: Ten recreationally active participants (age = 27 ± 3 years; mass = 70 ± 14 kg; height = 174 ± 9 cm; mean ± SD) completed 3 weeks of Nordic hamstring exercise (NHE) training on a custom exercise device that was instrumented with load cells. We collected in vivo sarcomere and muscle fascicle images of the BFlh in 2 regions (central and distal) by using microendoscopy and 3 dimension ultrasonography. We then estimated sarcomere length, sarcomere number, and fascicle length before and after the training intervention.

Results: Eccentric knee flexion strength increased after the training (15%; p < 0.001; η = 0.75). Further, we found a significant increase in fascicle length (21%; p < 0.001; η = 0.81) and sarcomere length (17%; p < 0.001; η = 0.90) in the distal but not in the central portion of the muscle. The estimated number of sarcomeres in series did not change in either region.

Conclusion: Fascicle length adaptations appear to be heterogeneous in the BFlh in response to 3 weeks of NHE training. An increase in sarcomere length, rather than the addition of sarcomeres in series, appears to underlie increases in fascicle length in the distal region of the BFlh. The mechanism driving regional increases in fascicle and sarcomere length remains unknown, but we speculate that it may be driven by regional changes in the passive tension of muscle or connective tissue adaptations.
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http://dx.doi.org/10.1016/j.jshs.2021.09.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847943PMC
January 2022

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.

Cartilage 2021 12 8;13(1_suppl):747S-756S. Epub 2021 Sep 8.

Department of Bioengineering, Stanford University, Stanford, CA, USA.

Objective: We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically.

Design: We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison.

Results: Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values.

Conclusions: Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.
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http://dx.doi.org/10.1177/19476035211042406DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808775PMC
December 2021

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation.

J Neuroeng Rehabil 2021 08 16;18(1):126. Epub 2021 Aug 16.

Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.

Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This "Learn to Move" competition was an official competition at the NeurIPS conference from 2017 to 2019 and attracted over 1300 teams from around the world. Top teams adapted state-of-the-art deep reinforcement learning techniques and produced motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.
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http://dx.doi.org/10.1186/s12984-021-00919-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365920PMC
August 2021

An Open-Source and Wearable System for Measuring 3D Human Motion in Real-Time.

IEEE Trans Biomed Eng 2022 02 21;69(2):678-688. Epub 2022 Jan 21.

Objective: Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation for conditions like osteoarthritis, stroke, and Parkinson's disease. Optical motion capture systems are the standard for estimating kinematics, but the equipment is expensive and requires a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Many wearable sensor systems require a computer in close proximity and use proprietary software, limiting experimental reproducibility.

Methods: Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller.

Results: We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task. The open-source software and hardware are scalable, tracking 1 to 14 body segments, with one sensor per segment. A musculoskeletal model and inverse kinematics solver estimate Kinematics in real-time. The computation frequency depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment.

Significance: The OpenSenseRT system is validated against optical motion capture, low-cost, and simple to replicate, enabling movement analysis in clinics, homes, and free-living settings.
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http://dx.doi.org/10.1109/TBME.2021.3103201DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8792207PMC
February 2022

Sensing leg movement enhances wearable monitoring of energy expenditure.

Nat Commun 2021 07 13;12(1):4312. Epub 2021 Jul 13.

Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.

Physical inactivity is the fourth leading cause of global mortality. Health organizations have requested a tool to objectively measure physical activity. Respirometry and doubly labeled water accurately estimate energy expenditure, but are infeasible for everyday use. Smartwatches are portable, but have significant errors. Existing wearable methods poorly estimate time-varying activity, which comprises 40% of daily steps. Here, we present a Wearable System that estimates metabolic energy expenditure in real-time during common steady-state and time-varying activities with substantially lower error than state-of-the-art methods. We perform experiments to select sensors, collect training data, and validate the Wearable System with new subjects and new conditions for walking, running, stair climbing, and biking. The Wearable System uses inertial measurement units worn on the shank and thigh as they distinguish lower-limb activity better than wrist or trunk kinematics and converge more quickly than physiological signals. When evaluated with a diverse group of new subjects, the Wearable System has a cumulative error of 13% across common activities, significantly less than 42% for a smartwatch and 44% for an activity-specific smartwatch. This approach enables accurate physical activity monitoring which could enable new energy balance systems for weight management or large-scale activity monitoring.
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http://dx.doi.org/10.1038/s41467-021-24173-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277831PMC
July 2021

A marker registration method to improve joint angles computed by constrained inverse kinematics.

PLoS One 2021 28;16(5):e0252425. Epub 2021 May 28.

Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands.

Accurate computation of joint angles from optical marker data using inverse kinematics methods requires that the locations of markers on a model match the locations of experimental markers on participants. Marker registration is the process of positioning the model markers so that they match the locations of the experimental markers. Markers are typically registered using a graphical user interface (GUI), but this method is subjective and may introduce errors and uncertainty to the calculated joint angles and moments. In this investigation, we use OpenSim to isolate and quantify marker registration-based error from other sources of error by analyzing the gait of a bipedal humanoid robot for which segment geometry, mass properties, and joint angles are known. We then propose a marker registration method that is informed by the orientation of anatomical reference frames derived from surface-mounted optical markers as an alternative to user registration using a GUI. The proposed orientation registration method reduced the average root-mean-square error in both joint angles and joint moments by 67% compared to the user registration method, and eliminated variability among users. Our results show that a systematic method for marker registration that reduces subjective user input can make marker registration more accurate and repeatable.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252425PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162579PMC
October 2021

Wearable sensors enable personalized predictions of clinical laboratory measurements.

Nat Med 2021 06 24;27(6):1105-1112. Epub 2021 May 24.

Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.

Vital signs, including heart rate and body temperature, are useful in detecting or monitoring medical conditions, but are typically measured in the clinic and require follow-up laboratory testing for more definitive diagnoses. Here we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic. Vital sign data collected from wearables can also predict several clinical laboratory measurements with lower prediction error than predictions made using clinically obtained vital sign measurements. The length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models. These results demonstrate the value of commercial wearable devices for continuous and longitudinal assessment of physiological measurements that today can be measured only with clinical laboratory tests.
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http://dx.doi.org/10.1038/s41591-021-01339-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293303PMC
June 2021

Assessment of Extractability and Accuracy of Electronic Health Record Data for Joint Implant Registries.

JAMA Netw Open 2021 03 1;4(3):e211728. Epub 2021 Mar 1.

Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, California.

Importance: Implant registries provide valuable information on the performance of implants in a real-world setting, yet they have traditionally been expensive to establish and maintain. Electronic health records (EHRs) are widely used and may include the information needed to generate clinically meaningful reports similar to a formal implant registry.

Objectives: To quantify the extractability and accuracy of registry-relevant data from the EHR and to assess the ability of these data to track trends in implant use and the durability of implants (hereafter referred to as implant survivorship), using data stored since 2000 in the EHR of the largest integrated health care system in the United States.

Design, Setting, And Participants: Retrospective cohort study of a large EHR of veterans who had 45 351 total hip arthroplasty procedures in Veterans Health Administration hospitals from 2000 to 2017. Data analysis was performed from January 1, 2000, to December 31, 2017.

Exposures: Total hip arthroplasty.

Main Outcomes And Measures: Number of total hip arthroplasty procedures extracted from the EHR, trends in implant use, and relative survivorship of implants.

Results: A total of 45 351 total hip arthroplasty procedures were identified from 2000 to 2017 with 192 805 implant parts. Data completeness improved over the time. After 2014, 85% of prosthetic heads, 91% of shells, 81% of stems, and 85% of liners used in the Veterans Health Administration health care system were identified by part number. Revision burden and trends in metal vs ceramic prosthetic femoral head use were found to reflect data from the American Joint Replacement Registry. Recalled implants were obvious negative outliers in implant survivorship using Kaplan-Meier curves.

Conclusions And Relevance: Although loss to follow-up remains a challenge that requires additional attention to improve the quantitative nature of calculated implant survivorship, we conclude that data collected during routine clinical care and stored in the EHR of a large health system over 18 years were sufficient to provide clinically meaningful data on trends in implant use and to identify poor implants that were subsequently recalled. This automated approach was low cost and had no reporting burden. This low-cost, low-overhead method to assess implant use and performance within a large health care setting may be useful to internal quality assurance programs and, on a larger scale, to postmarket surveillance of implant performance.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.1728DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961313PMC
March 2021

OpenSim Moco: Musculoskeletal optimal control.

PLoS Comput Biol 2020 12 28;16(12):e1008493. Epub 2020 Dec 28.

Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America.

Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves-which typically requires extensive technical expertise-and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.
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http://dx.doi.org/10.1371/journal.pcbi.1008493DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793308PMC
December 2020

High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff.

Elife 2020 12 16;9. Epub 2020 Dec 16.

Department of Bioengineering, Stanford University, Stanford, United States.

A long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. Here, we introduce a biomechanically realistic computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements. This model revealed that the speed-accuracy tradeoff, as described by Fitts' law, emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. Next, we analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural activity to movement duration (MD) variability is greater for smaller targets than larger targets, and that movements to smaller targets exhibit less variability in population-level preparatory activity, but greater MD variability. These results propose a new theory underlying the speed-accuracy tradeoff: Fitts' law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of 'good' control solutions (i.e., faster reaches). Thus, contrary to current beliefs, the speed-accuracy tradeoff could be a consequence of motor planning variability and not exclusively signal-dependent noise.
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http://dx.doi.org/10.7554/eLife.57021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787661PMC
December 2020

Transcutaneous Afferent Patterned Stimulation Therapy Reduces Hand Tremor for One Hour in Essential Tremor Patients.

Front Neurosci 2020 12;14:530300. Epub 2020 Nov 12.

Department of Bioengineering, Stanford University, Stanford, CA, United States.

Essential tremor (ET) patients often experience hand tremor that impairs daily activities. Non-invasive electrical stimulation of median and radial nerves in the wrist using a recently developed therapy called transcutaneous afferent patterned stimulation (TAPS) has been shown to provide symptomatic tremor relief in ET patients and improve patients' ability to perform functional tasks, but the duration of tremor reduction is unknown. In this single-arm, open-label study, fifteen ET patients performed four hand tremor-specific tasks (postural hold, spiral drawing, finger-to-nose reach, and pouring) from the Fahn-Tolosa-Marin Clinical Rating Scale (FTM-CRS) prior to, during, and 0, 30, and 60 min following TAPS. At each time point, tremor severity was visually rated according to the FTM-CRS and simultaneously measured by wrist-worn accelerometers. The duration of tremor reduction was assessed using (1) improvement in the mean FTM-CRS score across all four tasks relative to baseline, and (2) reduction in accelerometer-measured tremor power relative to baseline for each task. Patients were labeled as having at least 60 min of therapeutic benefit from TAPS with respect to each specified metric if all three (i.e., 0, 30, and 60 min) post-therapy measurements were better than that metric's baseline value. The mean FTM-CRS scores improved for at least 60 min beyond the end of TAPS for 80% (12 of 15, = 4.6e-9) of patients. Similarly, for each assessed task, tremor power improved for at least 60 min beyond the end of TAPS for over 70% of patients. The postural hold task had the largest reduction in tremor power (median 5.9-fold peak reduction in tremor power) and had at least 60 min of improvement relative to baseline beyond the end of TAPS therapy for 73% (11 of 15, = 9.8e-8) of patients. Clinical ratings of tremor severity were correlated to simultaneously recorded accelerometer-measured tremor power ( = 0.33-0.76 across the four tasks), suggesting tremor power is a valid, objective tremor assessment metric that can be used to track tremor symptoms outside the clinic. These results suggest TAPS can provide reductions in upper limb tremor symptoms for at least 1 h post-therapy in some patients, which may improve patients' ability to perform tasks of daily living.
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http://dx.doi.org/10.3389/fnins.2020.530300DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689107PMC
November 2020

Prospective Home-use Study on Non-invasive Neuromodulation Therapy for Essential Tremor.

Tremor Other Hyperkinet Mov (N Y) 2020 08 14;10:29. Epub 2020 Aug 14.

Cala Health, Burlingame, CA, US.

Highlights: This prospective study is one of the largest clinical trials in essential tremor to date. Study findings suggest that individualized non-invasive neuromodulation therapy used repeatedly at home over three months results in safe and effective hand tremor reduction and improves quality of life for many essential tremor patients.

Background: Two previous randomized, controlled, single-session trials demonstrated efficacy of non-invasive neuromodulation therapy targeting the median and radial nerves for reducing hand tremor. This current study evaluated efficacy and safety of the therapy over three months of repeated home use.

Methods: This was a prospective, open-label, post-clearance, single-arm study with 263 patients enrolled across 26 sites. Patients were instructed to use the therapy twice daily for three months. Pre-specified co-primary endpoints were improvements on clinician-rated Tremor Research Group Essential Tremor Rating Assessment Scale (TETRAS) and patient-rated Bain & Findley Activities of Daily Living (BF-ADL) dominant hand scores. Other endpoints included improvement in the tremor power detected by an accelerometer on the therapeutic device, Clinical and Patient Global Impression scores (CGI-I, PGI-I), and Quality of Life in Essential Tremor (QUEST) survey.

Results: 205 patients completed the study. The co-primary endpoints were met (p≪0.0001), with 62% (TETRAS) and 68% (BF-ADL) of 'severe' or 'moderate' patients improving to 'mild' or 'slight'. Clinicians (CGI-I) reported improvement in 68% of patients, 60% (PGI-I) of patients reported improvement, and QUEST improved (p = 0.0019). Wrist-worn accelerometer recordings before and after 21,806 therapy sessions showed that 92% of patients improved, and 54% of patients experienced ≥50% improvement in tremor power. Device-related adverse events (e.g., wrist discomfort, skin irritation, pain) occurred in 18% of patients. No device-related serious adverse events were reported.

Discussion: This study suggests that non-invasive neuromodulation therapy used repeatedly at home over three months results in safe and effective hand tremor reduction in many essential tremor patients.
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http://dx.doi.org/10.5334/tohm.59DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7427656PMC
August 2020

Deep neural networks enable quantitative movement analysis using single-camera videos.

Nat Commun 2020 08 13;11(1):4054. Epub 2020 Aug 13.

Center for Gait and Motion Analysis, Gillette Children's Specialty Healthcare, St. Paul, MN, 55101, USA.

Many neurological and musculoskeletal diseases impair movement, which limits people's function and social participation. Quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and highly trained personnel. Here, we present a method for predicting clinically relevant motion parameters from an ordinary video of a patient. Our machine learning models predict parameters include walking speed (r = 0.73), cadence (r = 0.79), knee flexion angle at maximum extension (r = 0.83), and Gait Deviation Index (GDI), a comprehensive metric of gait impairment (r = 0.75). These correlation values approach the theoretical limits for accuracy imposed by natural variability in these metrics within our patient population. Our methods for quantifying gait pathology with commodity cameras increase access to quantitative motion analysis in clinics and at home and enable researchers to conduct large-scale studies of neurological and musculoskeletal disorders.
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http://dx.doi.org/10.1038/s41467-020-17807-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426855PMC
August 2020

Pre-operative gastrocnemius lengths in gait predict outcomes following gastrocnemius lengthening surgery in children with cerebral palsy.

PLoS One 2020 5;15(6):e0233706. Epub 2020 Jun 5.

Center for Gait and Motion Analysis, Gillette Children's Specialty Healthcare, St. Paul, MN, United States of America.

Equinus deformity is one of the most common gait deformities in children with cerebral palsy. We examined whether estimates of gastrocnemius length in gait could identify limbs likely to have short-term and long-term improvements in ankle kinematics following gastrocnemius lengthening surgery to correct equinus. We retrospectively analyzed data of 891 limbs that underwent a single-event multi-level surgery (SEMLS), and categorized outcomes based on the normalcy of ankle kinematics. Limbs with short gastrocnemius lengths that received a gastrocnemius lengthening surgery as part of a SEMLS (case limbs) were 2.2 times more likely than overtreated limbs (i.e., limbs who did not have short lengths, but still received a lengthening surgery) to have a good surgical outcome at the follow-up gait visit (good outcome rate of 71% vs. 33%). Case limbs were 1.2 times more likely than control limbs (i.e., limbs that had short gastrocnemius lengths but no lengthening surgery) to have a good outcome (71% vs. 59%). Three-fourths of the case limbs with a good outcome at the follow-up gait visit maintained this outcome over time, compared to only one-half of the overtreated limbs. Our results caution against over-prescription of gastrocnemius lengthening surgery and suggest gastrocnemius lengths can be used to identify good surgical candidates.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233706PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274436PMC
August 2020

The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis.

J R Soc Interface 2020 06 3;17(167):20200011. Epub 2020 Jun 3.

Department of Bioengineering and Mechanical Engineering, Stanford University, Stanford, CA, USA.

It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviours seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks-such as reaching to targets on a higher or lower plane, and starting from alternative initial poses-with average errors similar to a full-dimensional controller.
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http://dx.doi.org/10.1098/rsif.2020.0011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328389PMC
June 2020

Rapid volumetric gagCEST imaging of knee articular cartilage at 3 T: evaluation of improved dynamic range and an osteoarthritic population.

NMR Biomed 2020 08 23;33(8):e4310. Epub 2020 May 23.

Radiology, Stanford University, Stanford, California, USA.

Chemical exchange saturation transfer of glycosaminoglycans, gagCEST, is a quantitative MR technique that has potential for assessing cartilage proteoglycan content at field strengths of 7 T and higher. However, its utility at 3 T remains unclear. The objective of this work was to implement a rapid volumetric gagCEST sequence with higher gagCEST asymmetry at 3 T to evaluate its sensitivity to osteoarthritic changes in knee articular cartilage and in comparison with T and T measures. We hypothesize that gagCEST asymmetry at 3 T decreases with increasing severity of osteoarthritis (OA). Forty-two human volunteers, including 10 healthy subjects and 32 subjects with medial OA, were included in the study. Knee Injury and Osteoarthritis Outcome Scores (KOOS) were assessed for all subjects, and Kellgren-Lawrence grading was performed for OA volunteers. Healthy subjects were scanned consecutively at 3 T to assess the repeatability of the volumetric gagCEST sequence at 3 T. For healthy and OA subjects, gagCEST asymmetry and T and T relaxation times were calculated for the femoral articular cartilage to assess sensitivity to OA severity. Volumetric gagCEST imaging had higher gagCEST asymmetry than single-slice acquisitions (p = 0.015). The average scan-rescan coefficient of variation was 6.8%. There were no significant differences in average gagCEST asymmetry between younger and older healthy controls (p = 0.655) or between healthy controls and OA subjects (p = 0.310). T and T relaxation times were elevated in OA subjects (p < 0.001 for both) compared with healthy controls and both were moderately correlated with total KOOS scores (rho = -0.181 and rho = -0.332 respectively). The gagCEST technique developed here, with volumetric scan times under 10 min and high gagCEST asymmetry at 3 T, did not vary significantly between healthy subjects and those with mild-moderate OA. This further supports a limited utility for gagCEST imaging at 3 T for assessment of early changes in cartilage composition in OA.
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http://dx.doi.org/10.1002/nbm.4310DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347437PMC
August 2020

The turning and barrier course reveals gait parameters for detecting freezing of gait and measuring the efficacy of deep brain stimulation.

PLoS One 2020 29;15(4):e0231984. Epub 2020 Apr 29.

Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, United States of America.

Freezing of gait (FOG) is a devastating motor symptom of Parkinson's disease that leads to falls, reduced mobility, and decreased quality of life. Reliably eliciting FOG has been difficult in the clinical setting, which has limited discovery of pathophysiology and/or documentation of the efficacy of treatments, such as different frequencies of subthalamic deep brain stimulation (STN DBS). In this study we validated an instrumented gait task, the turning and barrier course (TBC), with the international standard FOG questionnaire question 3 (FOG-Q3, r = 0.74, p < 0.001). The TBC is easily assembled and mimics real-life environments that elicit FOG. People with Parkinson's disease who experience FOG (freezers) spent more time freezing during the TBC compared to during forward walking (p = 0.007). Freezers also exhibited greater arrhythmicity during non-freezing gait when performing the TBC compared to forward walking (p = 0.006); this difference in gait arrhythmicity between tasks was not detected in non-freezers or controls. Freezers' non-freezing gait was more arrhythmic than that of non-freezers or controls during all walking tasks (p < 0.05). A logistic regression model determined that a combination of gait arrhythmicity, stride time, shank angular range, and asymmetry had the greatest probability of classifying a step as FOG (area under receiver operating characteristic curve = 0.754). Freezers' percent time freezing and non-freezing gait arrhythmicity decreased, and their shank angular velocity increased in the TBC during both 60 Hz and 140 Hz STN DBS (p < 0.05) to non-freezer values. The TBC is a standardized tool for eliciting FOG and demonstrating the efficacy of 60 Hz and 140 Hz STN DBS for gait impairment and FOG. The TBC revealed gait parameters that differentiated freezers from non-freezers and best predicted FOG; these may serve as relevant control variables for closed loop neurostimulation for FOG in Parkinson's disease.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0231984PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190141PMC
July 2020

Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks.

Radiol Artif Intell 2020 Mar 18;2(2):e190065. Epub 2020 Mar 18.

Departments of Biomedical Data Science (K.A.T., S.L.F., G.R.V.), Bioengineering (Ł.K., S.L.D.), and Radiology (G.E.G.), Stanford University, Clark Center, 318 Campus Dr, Room S321, Stanford, CA 94305; Department of Radiology, Erasmus University Rotterdam, Rotterdam, the Netherlands (E.H.G.O.); and Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa (E.H.).

Purpose: To develop an automated model for staging knee osteoarthritis severity from radiographs and to compare its performance to that of musculoskeletal radiologists.

Materials And Methods: Radiographs from the Osteoarthritis Initiative staged by a radiologist committee using the Kellgren-Lawrence (KL) system were used. Before using the images as input to a convolutional neural network model, they were standardized and augmented automatically. The model was trained with 32 116 images, tuned with 4074 images, evaluated with a 4090-image test set, and compared to two individual radiologists using a 50-image test subset. Saliency maps were generated to reveal features used by the model to determine KL grades.

Results: With committee scores used as ground truth, the model had an average F1 score of 0.70 and an accuracy of 0.71 for the full test set. For the 50-image subset, the best individual radiologist had an average F1 score of 0.60 and an accuracy of 0.60; the model had an average F1 score of 0.64 and an accuracy of 0.66. Cohen weighted κ between the committee and model was 0.86, comparable to intraexpert repeatability. Saliency maps identified sites of osteophyte formation as influential to predictions.

Conclusion: An end-to-end interpretable model that takes full radiographs as input and predicts KL scores with state-of-the-art accuracy, performs as well as musculoskeletal radiologists, and does not require manual image preprocessing was developed. Saliency maps suggest the model's predictions were based on clinically relevant information. © RSNA, 2020.
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http://dx.doi.org/10.1148/ryai.2020190065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104788PMC
March 2020

Foot strike pattern during running alters muscle-tendon dynamics of the gastrocnemius and the soleus.

Sci Rep 2020 04 3;10(1):5872. Epub 2020 Apr 3.

Department of Mechanical Engineering, Stanford University, Stanford, CA, United States.

Running is thought to be an efficient gait due, in part, to the behavior of the plantar flexor muscles and elastic energy storage in the Achilles tendon. Although plantar flexor muscle mechanics and Achilles tendon energy storage have been explored during rearfoot striking, they have not been fully characterized during forefoot striking. This study examined how plantar flexor muscle-tendon mechanics during running differs between rearfoot and forefoot striking. We used musculoskeletal simulations, driven by joint angles and electromyography recorded from runners using both rearfoot and forefoot striking running patterns, to characterize plantar flexor muscle-tendon mechanics. The simulations revealed that foot strike pattern affected the soleus and gastrocnemius differently. For the soleus, forefoot striking decreased tendon energy storage and fiber work done while the muscle fibers were shortening compared to rearfoot striking. For the gastrocnemius, forefoot striking increased muscle activation and fiber work done while the muscle fibers were lengthening compared to rearfoot striking. These changes in gastrocnemius mechanics suggest that runners planning to convert to forefoot striking might benefit from a progressive eccentric gastrocnemius strengthening program to avoid injury.
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http://dx.doi.org/10.1038/s41598-020-62464-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125118PMC
April 2020

Microendoscopy detects altered muscular contractile dynamics in a mouse model of amyotrophic lateral sclerosis.

Sci Rep 2020 01 16;10(1):457. Epub 2020 Jan 16.

Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA.

Amyotrophic lateral sclerosis (ALS) is a fatal disease involving motor neuron degeneration. Effective diagnosis of ALS and quantitative monitoring of its progression are crucial to the success of clinical trials. Second harmonic generation (SHG) microendoscopy is an emerging technology for imaging single motor unit contractions. To assess the potential value of microendoscopy for diagnosing and tracking ALS, we monitored motor unit dynamics in a B6.SOD1G93A mouse model of ALS for several weeks. Prior to overt symptoms, muscle twitch rise and relaxation time constants both increased, consistent with a loss of fast-fatigable motor units. These effects became more pronounced with disease progression, consistent with the death of fast fatigue-resistant motor units and superior survival of slow motor units. From these measurements we constructed a physiological metric that reflects the changing distributions of measured motor unit time constants and effectively diagnoses mice before symptomatic onset and tracks disease state. These results indicate that SHG microendoscopy provides a means for developing a quantitative, physiologic characterization of ALS progression.
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http://dx.doi.org/10.1038/s41598-019-56555-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6965652PMC
January 2020
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