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respect to t 0 ∈ [t 0,L , t 0,U ] t n ∈ [t f ,L , t f ,U ] y 0 ∈ [ y 0,L , y 0,U ] y n ∈ [ y f ,L , y f ,U ] y i ∈ [ y L , y U ] i = 1, . . . , n − 1 ζ i ∈ [ζ L , ζ U ] i = 0, . . . , n x 0 ∈ [x 0,L , ... L , y U ] i = 1, . . . , n ζ i ∈ [ζ L , ζ U ] i = 0, . . . , n ζ i ∈ [ζ L , ζ U ] i = 1, . . . , n x 0 ∈ [x 0,L , x 0,U ] x n ∈ [x f ,L , x f ,U ] x i ∈ [x L , x U ] i = 1, . . . , n − 1 x i ∈ [x L , ...doi:10.1101/839381 fatcat:vh7qkram2nbgxkyukmiusttjbu
Sherman, Friedl de Groote, Antonie J. van den Bogert, Michael Posa, Joris Gillis, and Joel Andersson for discussing methodology and implementation; Bradley Humphreys, Carmichael Ong, Noah Gordon, and Jennifer ...doi:10.1371/journal.pcbi.1008493 pmid:33370252 fatcat:7gl2kldtpnggtmixptdvngaqey
Gait & Posture
Hicks et al. / Gait & Posture 34 (2011) 197-201 ...doi:10.1016/j.gaitpost.2011.04.009 pmid:21616666 pmcid:PMC3130107 fatcat:dlv3rrpds5e2zjbj3ljvvbqdki
Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing movement dynamics using videos captured from smartphones. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automaticallydoi:10.1101/2022.07.07.499061 fatcat:wwuubd44yrcvxjeye6m6w5lgly
more »... sed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated movement dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.
We first report the heritabilities of courtship traits in founder-flush and control populations of the housefly (Musca domestica L.). ... Jones, and L. Vo. We also thank C. Boake, B. Brodie III, and an anonymous referee for their valuable comments on an earlier draft of the manuscript. ... Prior housefly experiments (Meffert and Regan 2002; L. M. Meffert, J. Regan, S. Hicks, N. Mukana, S. Day, J. Bersola, and S. ...doi:10.1086/342896 pmid:18707477 fatcat:vtgim66ggjfbpayvsxq2gkg5em
Additional examples come from the Stanford group (Delp, Hicks) who, together with collaborators at Gillette Children's Specialty Healthcare, combined biomechanical modeling and data science methods to ... This information helped develop a statistical model that was able to predict improvement in crouch gait with 73% accuracy, while in practice only 48% of patients improve after surgery (Hicks et al., 2011 ...doi:10.1016/j.jbiomech.2016.10.033 pmid:27814971 pmcid:PMC5407492 fatcat:irmnfnaubrehbahvyizhdtjgni
Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation interventions for conditions such as osteoarthritis, stroke, and Parkinson's disease. Optical motion capture systems are the current standard for estimating kinematics but require expensive equipment located in a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Further, many wearabledoi:10.1101/2021.03.24.436725 fatcat:f5ydhefnirf6vmnevh5g53ozgi
more »... ensor systems require a computer in close proximity and rely on proprietary software, making it difficult for researchers to reproduce experimental findings. 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. 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 (n = 5) and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task (n = 5). The open-source software and hardware are scalable, tracking between 1 and 14 body segments, with one sensor per segment. Kinematics are estimated in real-time using a musculoskeletal model and inverse kinematics solver. 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 up to 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. The OpenSenseRT system is accurate, low-cost, and simple to replicate, enabling movement analysis in labs, clinics, homes, and free-living settings.
Understanding the basic principles that govern physical activity is needed to curb the global pandemic of physical inactivity 1-7 and the 5.3 million deaths per year associated with in-activity 2 . Our knowledge, however, remains limited owing to the lack of large-scale measurements of physical activity patterns across free-living populations worldwide 1, 6 . Here, we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at planetary scale. We study adoi:10.1038/nature23018 pmid:28693034 pmcid:PMC5774986 fatcat:ig34vixlkzei5nkplijluedrde
more »... taset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, were associated with less gender gap in activity and activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment for improving physical activity and health. Physical activity improves musculoskeletal health and function, prevents cognitive decline, reduces symptoms of depression and anxiety, and helps maintain a healthy weight 4, 7 . While Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature. Extended Data Figure 1. Activity and obesity data gathered with smartphones exhibit well established trends (a) Daily step counts across age and (b) BMI groups for all users. Error bars correspond to bootstrapped 95% confidence intervals. Observed trends in the dataset are consistent with previous findings; that is, activity decreases with increasing age 1, 8, 31, 32 and BMI 8, 15, 31 , and is lower in females than in males 1, 8, 31-33 . Althoff et al. Figure 2 . Activity and obesity data gathered with smartphones are significantly correlated with previously reported estimates based on self-report (a) WHO physical activity measure 34 versus smartphone activity measure. The WHO measure corresponds to the percentage of the population meeting the WHO guidelines for moderate to vigorous physical activity based on self-report. The smartphone activity measure is based on accelerometer-defined average daily steps. We find a correlation of r=0.3194 between the two measures (p < 0.05). Note that this comparison is limited because there is no direct correspondence between the two measures-values of self-report and accelerometer-defined activity can differ 14 , and the WHO confidence intervals are very large for many countries (Methods). (b) WHO obesity estimates 35 , based on self-reports to survey conductors, versus obesity estimates in our dataset, based on height and weight reported to the activity-tracking app. We find a significant correlation of r=0.691 between the two estimates (p < 10 −6 ). (c) Gender gap in activity estimated from smartphones is strongly correlated with previously reported estimates based on self-report. We find that the difference in average steps per day between females and males is strongly correlated to the Althoff et al. Extended Data
., JD, LLM, John L. Hick, MD, and Jennifer L. Piatt, JD, report no conflicts of interest. The author, Dan Hanfling, MD, reports that he serves as Co-Chair, U.S. ... ., JD, LLM, Dan Hanfling, MD, John L. Hick, MD, and Jennifer L. ...doi:10.1016/j.eclinm.2021.100838 pmid:33898956 pmcid:PMC8060580 fatcat:gfj22im4jreojjstyzfc2un4zi
RMS and peak residual forces were 1.6% (SD 0.6%) and 3.5% (SD 1.7%) of the maximum ground reaction force magnitude, respectively, within the recommended limit of 5% (Hicks 2015) . ...doi:10.1101/2022.06.02.22275843 fatcat:t56ruuln7fb6dkex3ytiadfn7u
Ankle inversion sprains are the most frequent acute musculoskeletal injuries occurring in physical activity. Interventions that retrain muscle coordination have helped rehabilitate injured ankles, but it is unclear which muscle coordination strategies, if any, can prevent ankle sprains. The purpose of this study was to determine whether coordinated activity of the ankle muscles could prevent excessive ankle inversion during a simulated landing on a 30-degree incline. We used a set ofdoi:10.1016/j.jbiomech.2016.11.002 pmid:28057351 pmcid:PMC5798431 fatcat:vvye5zbx6na3zpzktaeiadwo7u
more »... etal simulations to evaluate the efficacy of two strategies for coordinating the ankle evertor and invertor muscles during simulated landing scenarios: planned co-activation and stretch reflex activation with physiologic latency (60-millisecond delay). A full-body musculoskeletal model of landing was used to generate simulations of a subject dropping onto an inclined surface with each coordination condition. Within each condition, the intensity of evertor and invertor coactivity or stretch reflexes were varied systematically. The simulations revealed that strong preparatory co-activation of the ankle evertors and invertors prior to ground contact prevented ankle inversion from exceeding injury thresholds by rapidly generating eversion moments after initial contact. Conversely, stretch reflexes were too slow to generate eversion moments before the simulations reached the threshold for inversion injury. These results suggest that training interventions to protect the ankle should focus on stiffening the ankle with muscle co-activation prior to landing. The musculoskeletal models, controllers, software, and simulation results are freely available online at http://simtk.org/home/ankle-sprains, enabling others to reproduce the results and explore new injury scenarios and interventions.
Tools have been used for millions of years to augment the capabilities of the human body, allowing us to accomplish tasks that would otherwise be difficult or impossible. Powered exoskeletons and other assistive devices are sophisticated modern tools that have restored bipedal locomotion in individuals with paraplegia and have endowed unimpaired individuals with superhuman strength. Despite these successes, designing assistive devices that reduce energy consumption during running remains adoi:10.1371/journal.pone.0163417 pmid:27656901 pmcid:PMC5033584 fatcat:yh2cnoxyzbg3zdk4z5amxqm5k4
more »... antial challenge, in part because these devices disrupt the dynamics of a complex, finely tuned biological system. Furthermore, designers have hitherto relied primarily on experiments, which cannot report muscle-level energy consumption and are fraught with practical challenges. In this study, we use OpenSim to generate muscle-driven simulations of 10 human subjects running at 2 and 5 m/s. We then add ideal, massless assistive devices to our simulations and examine the predicted changes in muscle recruitment patterns and metabolic power consumption. Our simulations suggest that an assistive device should not necessarily apply the net joint moment generated by muscles during unassisted running, and an assistive device can reduce the activity of muscles that do not cross the assisted joint. Our results corroborate and suggest biomechanical explanations for similar effects observed by experimentalists, and can be used to form hypotheses for future experimental studies. The models, simulations, and software used in this study are freely available at simtk.org and can provide insight into assistive device design that complements experimental approaches.
CE ) beyond a threshold H, delayed by D seconds: u L ¼ G L l CE t À D ð ÞÀH ½ þ : The notation [x] + for the PD and stretch feedback controller indicates that the signal is zero for,x<0 and set to x otherwise ... PD controllers were defined as: u y ¼ K p ðy t À D ð ÞÀy desired Þ þ K d _ y t À D ð Þ h i þ : The muscle gain parameters G F ,G L ,K p ,K d , the threshold parameter H, and the desired feature target ...doi:10.1371/journal.pone.0121407 pmid:25830913 pmcid:PMC4382289 fatcat:vm3erixq5za4vktqked5jns3we
Acknowledgments We would like to thank Apoorva Rajagopal and Jennifer Yong for their insightful feedback on this manuscript, and Christopher Dembia, Shrinidhi Lakshmikanth, Ajay Seth, and Thomas Uchida ... ., L+, V+, F+) onto the same muscle, except for a negative force feedback (F-) from the soleus to the tibialis anterior. ... There were 5 types of control laws: constant (C), length feedback (L), velocity feedback (V), force feedback (F), and proportional-derivative (PD). ...doi:10.1101/597294 fatcat:azmyyano7neyta4xu4pgtcejqu
Łukasz Kidziński, Carmichael Ong, Jennifer Hicks and Scott Delp are affiliated with Department of Bioengineering, Stanford University. ... The following equation shows how the total force was calculated, due to both active and passive force, in the each muscle (F muscle ), F muscle = F max−iso (a f active (l) f velocity (v) + f passive (l ...arXiv:1804.00198v1 fatcat:igfpn6joujg57l3r5yy3dj4fwe
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