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Objective: Hamstring injuries are common in field-based sports and reinjury rates are high. Recent evidence suggests hamstring injuries often occur during accelerative running, but investigations of hamstring mechanics have primarily considered constant-speed running. Thus, our objective was to compare hamstring lengths and velocities between accelerative running and constant-speed running., Methods: We recorded videos of 10 participants during 6 accelerative running trials and 6 constant-speed running trials. We used OpenCap to estimate body segment kinematics and a 3-dimensional musculoskeletal model to compute peak length and step-average lengthening velocity of the biceps femoris (long head) muscle-tendon unit. We compared running conditions using linear mixed models with running speed as the independent variable., Results: At running speeds below 75% of top speed, accelerative running resulted in greater peak lengths than constant-speed running. For example, the peak hamstring muscle-tendon length when a person accelerated from running at only 50% of top speed was equivalent to running at a constant 88% of top speed. Lengthening velocities were greater during accelerative running at all running speeds. Differences in hip flexion kinematics drove the greater peak lengths and lengthening velocities observed in accelerative running., Conclusions: Hamstrings are subjected to longer lengths and faster lengthening velocities in accelerative running than in constant-speed running. This provides a potential biomechanical perspective towards understanding the occurrence of hamstring injuries during acceleration. Our results suggest coaches and sports medicine staff should consider the accelerative nature of running in addition to running speed to quantify exposure to high-risk circumstances with long lengths and fast lengthening velocities of the hamstrings., Competing Interests: Conflict of Interest and Funding Source: This work was supported by the Wu Tsai Human Performance Alliance at Stanford University and the Joe and Clara Tsai Foundation, and the National Institutes of Health through Grant P41EB027060. A. F. is the cofounder of Model Health, Inc., which supports the non-academic, commercial use of the open-source software used for data collection in this study., (Copyright © 2024 by the American College of Sports Medicine.)

Background: Eccentric training, such as Nordic hamstring exercise (NHE) training, is commonly used as a preventive measure for hamstring strains. Eccentric training is believed to induce lengthening of muscle fascicles and to be associated with the addition of sarcomeres in series within muscle fibers. However, the difficulty in measuring sarcomere adaptation in human muscles has severely limited information about the precise mechanisms of adaptation. This study addressed this limitation by measuring the multiscale hamstring muscle adaptations in response to 9 weeks of NHE training and 3 weeks of detraining., Methods: Twelve participants completed 9 weeks of supervised NHE training, followed by a 3-week detraining period. We assessed biceps femoris long-head (BFlh) muscle fascicle length, sarcomere length, and serial sarcomere number in the central and distal regions of the muscle. Additionally, we measured muscle volume and eccentric strength at baseline, post-training, and post-detraining., Results: NHE training over 9 weeks induced significant architectural and strength adaptations in the BFlh muscle. Fascicle length increased by 19% in the central muscle region and 33% in the distal muscle region. NHE also induced increases in serial sarcomere number (25% in the central region and 49% in the distal region). BFlh muscle volume increased by 8%, and knee flexion strength increased by 40% with training. Following 3 weeks of detraining, fascicle length decreased by 12% in the central region and 16% in the distal region along with reductions in serial sarcomere number., Conclusion: Nine weeks of NHE training produced substantial, region-specific increases in BFlh muscle fascicle length, muscle volume, and force generation. The direct measurement of sarcomere lengths revealed that the increased fascicle length was accompanied by the addition of sarcomeres in series within the muscle fascicles., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024. Production and hosting by Elsevier B.V.)

Introduction: Several investigations have examined utilizing inertial measurement units (IMU) to estimate ground reaction force (GRF) during exercise. The purpose of this investigation was to determine the effect of inertial measurement units location on the estimation of ground reaction force during vertical jumping. Methods: Eight male subjects completed a series of ten countermovement jumps on a force plate (FP). The subjects had an inertial measurement units attached to the sacrum, back and chest. Ground reaction force was estimated from data from the individual inertial measurement units and by using a two-segment model and combined sensor approach. Results: The peak ground reaction force values for the sacrum, back, chest and combined inertial measurement units were 1,792 ± 278 N, 1,850 ± 341 N, 2,054 ± 346 N and 1,812 ± 323 N, respectively. The sacral inertial measurement units achieved the smallest differences for ground reaction force estimates providing a root mean square error (RMSE) between 88 N and 360 N. The inertial measurement units on the sacrum also showed significant correlations in peak ground reaction force ( p < 0.001) and average ground reaction force ( p < 0.001) using the Bland-Altman 95% Limits of Agreement (LOA) when in comparison to the force plate. Discussion: Based on assessment of bias, Limits of Agreement, and RMSE, the inertial measurement units located on the sacrum appears to be the best placement to estimate both peak and average ground reaction force during jumping., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kerns, Zwart, Perez, Gurchiek and McBride.)

Inertial measurement units (IMUs) offer an attractive way to study human lower-limb kinematics without traditional laboratory constraints. We present an error-state Kalman filter method to estimate 3D joint angles, joint angle ranges of motion, stride length, and step width using data from an array of seven body-worn IMUs. Importantly, this paper contributes a novel joint axis measurement correction that reduces joint angle drift errors without assumptions of strict hinge-like joint behaviors of the hip and knee. We evaluate the method compared to two optical motion capture methods on twenty human subjects performing six different types of walking gait consisting of forward walking (at three speeds), backward walking, and lateral walking (left and right). For all gaits, RMS differences in joint angle estimates generally remain below 5 degrees for all three ankle joint angles and for flexion/extension and abduction/adduction of the hips and knees when compared to estimates from reflective markers on the IMUs. Additionally, mean RMS differences in estimated stride length and step width remain below 0.13 m for all gait types, except stride length during slow walking. This study confirms the method's potential for non-laboratory based gait analysis, motivating further evaluation with IMU-only measurements and pathological gaits.

Falls are frequent and associated with morbidity in persons with multiple sclerosis (PwMS). Symptoms of MS fluctuate, and standard biannual clinical visits cannot capture these fluctuations. Remote monitoring techniques that leverage wearable sensors have recently emerged as an approach sensitive to disease variability. Previous research has shown that fall risk can be identified from walking data collected by wearable sensors in controlled laboratory conditions however this data may not be generalizable to variable home environments. To investigate fall risk and daily activity performance from remote data, we introduce a new open-source dataset featuring data collected from 38 PwMS, 21 of whom are identified as fallers and 17 as non-fallers based on their six-month fall history. This dataset contains inertial-measurement-unit data from eleven body locations collected in the laboratory, patient-reported surveys and neurological assessments, and two days of free-living sensor data from the chest and right thigh. Six-month (n = 28) and one-year repeat assessment (n = 15) data are also available for some patients. To demonstrate the utility of these data, we explore the use of free-living walking bouts for characterizing fall risk in PwMS, compare these data to those collected in controlled environments, and examine the impact of bout duration on gait parameters and fall risk estimates. Both gait parameters and fall risk classification performance were found to change with bout duration. Deep learning models outperformed feature-based models using home data; the best performance was observed with all bouts for deep-learning and short bouts for feature-based models when evaluating performance on individual bouts. Overall, short duration free-living walking bouts were found to be the least similar to laboratory walking, longer duration free-living walking bouts provided more significant differences between fallers and non-fallers, and an aggregation of all free-living walking bouts yields the best performance in fall risk classification., Competing Interests: AJS discloses consulting for EMD Serono, Biogen, and Alexion, and research funding from Biogen and NIH. RSM discloses stock ownership in Impellia, Epicore Biosystems, Allostatech, and PanicMechanic; consulting for Impellia, HX Innovations, Pfizer, Solid Biosciences, and Happy Health; and research funding from MC10, Medidata, Epicore Biosystems, NIH, and NSF., (Copyright: © 2022 Meyer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.

Abstract: Hawley, VS, Gurchiek, RD, and van Werkhoven, H. Can foot anthropometry predict vertical jump performance? J Strength Cond Res 36(7): 1860-1865, 2022-Vertical jumping is an important element of many sporting activities, and whether anthropometric adaptations can predict jumping performance is of interest. Few studies have specifically considered anthropometric measures of the foot and its link to performance. Furthermore, previous studies have mainly focused on a male subject pool, and whether relationships are consistent across sexes is unclear. The purpose of this study was to investigate relationships between common anthropometric measures, as well as specific foot measures, and jump performance in men and women. Anthropometric measures of 21 men (age: 22.0 ± 1.5 years; stature: 181.4 ± 6.3 cm; body mass: 85.6 ± 9.4 kg) and 21 women (age: 21.2 ± 1.8 years; stature: 166.1 ± 7.5 cm; body mass: 61.4 ± 11.4 kg) were taken before performing 3 maximal countermovement jumps (CMJs). Correlational analysis was used to determine relationships between anthropometric measures and CMJ height (a priori significance set at p≤ 0.05, effect size: small >0.1; medium >0.3; large >0.5). There was no significant correlation between anthropometric variables and CMJ height for men, whereas for women, mass (r = -0.585, p = 0.005, large effect), foot length (r = -0.533, p = 0.013, large effect), and toe length (r = -0.604, p = 0.004, large effect) showed significant negative correlations with CMJ height. The unexpected result that smaller feet and toes predicted higher jumps for women warrants further investigation. Furthermore, these results highlight the need to incorporate diverse subject pools, and a need for caution when generalizing across sexes., (Copyright © 2020 National Strength and Conditioning Association.)

Complex sensor arrays prohibit practical deployment of existing wearables-based algorithms for free-living analysis of muscle and joint mechanics. Machine learning techniques have been proposed as a potential solution, however, they are less interpretable and generalizable when compared to physics-based techniques. Herein, we propose a hybrid method utilizing inertial sensor- and electromyography (EMG)-driven simulation of muscle contraction to characterize knee joint and muscle mechanics during walking gait. Machine learning is used only to map a subset of measured muscle excitations to a full set thereby reducing the number of required sensors. We demonstrate the utility of the approach for estimating net knee flexion moment (KFM) as well as individual muscle moment and work during the stance phase of gait across nine unimpaired subjects. Across all subjects, KFM was estimated with 0.91%BW•H RMSE and strong correlations (r = 0.87) compared to ground truth inverse dynamics analysis. Estimates of individual muscle moments were strongly correlated (r = 0.81-0.99) with a reference EMG-driven technique using optical motion capture and a full set of electrodes as were estimates of muscle work (r = 0.88-0.99). Implementation of the proposed technique in the current work included instrumenting only three muscles with surface electrodes (lateral and medial gastrocnemius and vastus medialis) and both the thigh and shank segments with inertial sensors. These sensor locations permit instrumentation of a knee brace/sleeve facilitating a practically deployable mechanism for monitoring muscle and joint mechanics with performance comparable to the current state-of-the-art.

Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments could help in prescribing preventative interventions. To this end, retrospective fall status classification commonly serves as an intermediate step in developing prospective fall risk assessments. Previous research has identified measures of gait biomechanics that differ between PwMS who have fallen and those who have not, but these biomechanical indices have not yet been leveraged to detect PwMS who have fallen. Moreover, they require the use of laboratory-based measurement technologies, which prevent clinical deployment. Here we demonstrate that a bidirectional long short-term (BiLSTM) memory deep neural network was able to identify PwMS who have recently fallen with good performance (AUC of 0.88) based on accelerometer data recorded from two wearable sensors during a one-minute walking task. These results provide substantial improvements over machine learning models trained on spatiotemporal gait parameters (21% improvement in AUC), statistical features from the wearable sensor data (16%), and patient-reported (19%) and neurologist-administered (24%) measures in this sample. The success and simplicity (two wearable sensors, only one-minute of walking) of this approach indicates the promise of inexpensive wearable sensors for capturing fall risk in PwMS.

Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet., Competing Interests: The authors have read the journal’s policy and have the following competing interests: RSM reports stock ownership in Epicore Biosystems, Inc.; Impellia, Inc.; and Allostatech, LLC. RSM reports research funding from MC10, Inc.; Epicore Biosystems, Inc.; US National Science Foundation; US National Institute of Health. RSM also receives funding from consulting for HX Innovations Inc. and Happy Health Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.

Estimation of ground reaction forces in runners has been limited to laboratory environments by means of instrumented treadmills, in-ground force plates and optoelectronic systems. Recent advances in estimation techniques using wearable sensors for kinematic analysis and sports performance could enable estimation outside the laboratory. This paper proposes a state-input-parameter estimation framework to continuously estimate the vertical ground reaction force waveform during running. By modeling a runner as a single degree of freedom mass-spring-damper with acceleration measurements at the sacrum a state-space formulation can be applied using Newtonian methods. A dual-Kalman filter is employed to estimate the unmeasured system input which feeds through to an unscented Kalman filter to estimate system dynamics and unknown model parameters (e.g. spring stiffness). For validation, 14 subjects performed three one-minute running trials at three different speeds (self-selected slow, comfortable, and fast) on a pressure-sensor-instrumented treadmill. The estimated vertical ground reaction force waveform parameters; peak vertical ground reaction force (RMSE=6.1-7.2%,ρ=0.95-0.97), vertical impulse (RMSE=8.5-13.0%,ρ=0.50-0.60), loading rate (RMSE=24.6-39.4%,ρ=0.85-0.93), and cadence RMSE<1%,ρ=1.00 were compared against the instrumented treadmill measurements. The proposed state-input-parameter estimation framework could monitor personalized vertical ground reaction force metrics for potential biofeedback applications. The feedback mechanism could provide information about the vertical ground reaction force characteristics to the runner as they are running to provide knowledge of both desirable and undesirable loading characteristics experienced., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Objectives: Understanding the differences in baseball pitching biomechanics between American and Japanese pitchers may help with training and developing these athletes. The purpose of this study was to investigate the kinematic and kinetic differences in collegiate baseball pitchers from United States of American and Japan., Design: Controlled laboratory study., Methods: Data were analyzed for 11 American and 11 Japanese collegiate pitchers throwing fastballs using 3D motion capture (480Hz)., Results: The Americans were heavier (95±7kg vs 81±7kg), taller (189±3cm vs 180±6cm), and had faster ball velocity (39±1m/s vs 35±2m/s). By the end of arm cocking phase, the American pitchers had rotated their shoulder to a greater degree (p=0.021, d=1.5) and at ball release the Japanese had greater knee flexion (p=0.020, d=1.2). American pitchers exhibited greater peak kinetics on the throwing arm; however, when normalized for height and weight only three differences remained., Conclusion: The differences found between the American and Japanese players could contribute to the increased ball velocity in the American pitchers. Additionally, throwing arm peak kinetics were greater in the American pitchers which may help generate greater ball velocity; however, increased kinetics may also lead to increased risk of injury., (Copyright © 2020 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

Estimation of muscle excitations from a reduced sensor array could greatly improve current techniques in remote patient monitoring. Such an approach could allow continuous monitoring of clinically relevant biomechanical variables that are ideal for personalizing rehabilitation. In this paper, we introduce the notion of a muscle synergy function which describes the synergistic relationship between a subset of muscles. We develop from first principles an approximation to their behavior using Gaussian process regression and demonstrate the utility of the technique for estimating the excitation time-series of leg muscles during normal walking for nine healthy subjects. Specifically, excitations for six muscles were estimated using surface electromyography (sEMG) data during a finite time interval (called the input window) from four different muscles (called the input muscles) with mean absolute error (MAE) less than 5.0% of the maximum voluntary contraction (MVC) and that accounts for 82-88% of the variance (VAF) in the true excitations. Further, these estimated excitations informed muscle activations with less than 4.0% MAE and 89-93% VAF. We also present a detailed analysis of a number of different modeling choices, including every possible combination of four-, three- and two-muscle input sets, the size and structure of the input window, and the stationarity of the Gaussian process covariance functions. Further, application specific modifications for future use are discussed. The proposed technique lays a foundation to explore the use of reduced wearable sensor arrays and muscle synergy functions for monitoring clinically relevant biomechanics during daily life.

Background: Gait event detection is critical for remote gait analysis. Algorithms using a thigh-worn accelerometer for estimating spatiotemporal gait variables have demonstrated clinical utility in monitoring the gait of patients with gait and balance impairment. However, one may obtain accurate estimates of spatiotemporal variables, but with biased estimates of foot contact and foot off events. Some biomechanical analyses depend on accurate gait phase segmentation, but previous studies using a thigh-worn accelerometer have not quantified the error in estimating foot contact and foot off events., Methods: Gait events and spatiotemporal gait variables were estimated using a thigh-worn accelerometer from 32 healthy subjects across a range of walking speeds (0.56-1.78 m/s). Ground truth estimates were obtained using vertical ground reaction forces measured using a pressure treadmill. Estimation performance was quantified using absolute error, root mean square error, and correlation analysis., Results: Across all strides (N = 3,898), the absolute error in estimating foot contact, foot off, stride time, stance time, and swing time was similar to other accelerometer-based techniques (39 ± 28 ms, 28 ± 28 ms, 11 ± 14 ms, 46 ± 31 ms, and 45 ± 30 ms, respectively). The correlation between reference measurements and estimates of bout-average stride time, stance time, and swing time were 1.00, 0.92, and 0.80, respectively. The (5th, 95th) percentiles of the foot contact and foot off estimation errors were (-91 ms, 51 ms) and (-70 ms, 60 ms), the largest of which amounts to about three samples using the 31.25 Hz sampling frequency used in this study., Significance: Use of the proposed algorithm for estimating spatiotemporal gait variables is supported by the strong correlations with reference measurements. The gait event estimation error distributions provide bounds on the estimated gait events for enforcing gait phase-dependent task constraints for biomechanical analysis., Competing Interests: Declaration of Competing Interest RSM owns stock in MC10, Inc. This conflict is being managed by the University of Vermont., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Continuous observation of muscle activity could provide a comprehensive picture of the loads experienced by muscles and joints during daily life. However, a major limitation to the practical application of this approach is the need to have surface electromyography (sEMG) sensors on all involved muscles. In this work, we model the synergistic relationship between muscles as a Gaussian process enabling the inference of unmeasured muscle excitations using a subset of measured data. Specifically, we developed a model for a single subject which uses sEMG data from four leg muscles to estimate the muscle excitation time-series of six other leg muscles during level walking at a self-selected speed. The proposed technique was able to accurately estimate the held-out muscle excitation time-series of the six muscles with correlation coefficients ranging from 0.74 to 0.87 and with mean absolute error less than 3%.

Critical to digital medicine is the promise of improved patient monitoring to allow assessment and personalized intervention to occur in real-time. Wearable sensor-enabled observation of physiological data in free-living conditions is integral to this vision. However, few open-source algorithms have been developed for analyzing and interpreting these data which slows development and the realization of digital medicine. There is clear need for open-source tools that analyze free-living wearable sensor data and particularly for gait analysis, which provides important biomarkers in multiple clinical populations. We present an open-source analytical platform for automated free-living gait analysis and use it to investigate a novel, multi-domain (accelerometer and electromyography) asymmetry measure for quantifying rehabilitation progress in patients recovering from surgical reconstruction of the anterior cruciate ligament (ACL). Asymmetry indices extracted from 41,893 strides were more strongly correlated (r = -0.87, p < 0.01) with recovery time than standard step counts (r = 0.25, p = 0.52) and significantly differed between patients 2- and 17-weeks post-op (p < 0.01, effect size: 2.20-2.96), and controls (p < 0.01, effect size: 1.74-4.20). Results point toward future use of this open-source platform for capturing rehabilitation progress and, more broadly, for free-living gait analysis.

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( 12 . 32 ∘ and 11 . 82 ∘ vs. 24 . 61 ∘ and 23 . 76 ∘ ) and flexion/extension joint angles ( 7 . 88 ∘ and 8 . 62 ∘ vs. 14 . 14 ∘ and 15 . 64 ∘ ). Also, ROM estimation error was less than 2 . 2 ∘ during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles.

Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying young children with internalizing disorders using a 3-min speech task. We show that machine learning analysis of audio data from the task can be used to identify children with an internalizing disorder with 80% accuracy (54% sensitivity, 93% specificity). The speech features most discriminative of internalizing disorder are analyzed in detail, showing that affected children exhibit especially low-pitch voices, with repeatable speech inflections and content, and high-pitched response to surprising stimuli relative to controls. This new tool is shown to outperform clinical thresholds on parent-reported child symptoms, which identify children with an internalizing disorder with lower accuracy (67-77% versus 80%), and similar specificity (85-100% versus 93%), and sensitivity (0-58% versus 54%) in this sample. These results point toward the future use of this approach for screening children for internalizing disorders so that interventions can be deployed when they have the highest chance for long-term success.

Purpose of Review: Walking impairments are highly prevalent in persons with multiple sclerosis (PwMS) and are associated with reduced quality of life. Walking is traditionally quantified with various measures, including patient self-reports, clinical rating scales, performance measures, and advanced lab-based movement analysis techniques. Yet, the majority of these measures do not fully characterize walking (i.e., gait quality) nor adequately reflect walking in the real world (i.e., community ambulation) and have limited timescale (only measure walking at a single point in time). We discuss the potential of wearable sensors to provide sensitive, objective, and easy-to-use assessment of community ambulation in PwMS., Recent Findings: Wearable technology has the ability to measure all aspects of gait in PwMS yet is under-studied in comparison with other populations (e.g., older adults). Within the studies focusing on PwMS, half that measure pace collected free-living data, while only one study explored gait variability in free-living conditions. No studies explore gait asymmetry or complexity in free-living conditions. Wearable technology has the ability to provide objective, comprehensive, and sensitive measures of gait in PwMS. Future research should investigate this technology's ability to accurately assess free-living measures of gait quality, specifically gait asymmetry and complexity.

Field-based sprint performance assessments rely on metrics derived from a simple model of sprinting dynamics parameterized by 2 constants, v 0 and τ, which indicate a sprinter's maximal theoretical velocity and the time it takes to approach v 0 , respectively. This study aims to automate sprint assessment by estimating v 0 and τ using machine learning and accelerometer data. To this end, photocells recorded 10-m split times of 28 subjects for three 40-m sprints while wearing an accelerometer around the waist. Features extracted from the accelerometer data were used to train a classifier to identify the sprint start and regression models to estimate the sprint model parameters. Estimates of v 0 , τ, and 30-m sprint time (t 30 ) were compared between the proposed method and a photocell method using root mean square error and Bland-Altman analysis. The root mean square error of the sprint start estimate was .22 seconds and ranged from .52 to .93 m/s for v 0 , .14 to .17 seconds for τ, and .23 to .34 seconds for t 30 . Model-derived sprint performance metrics from most regression models were significantly (P < .01) correlated with t 30 . Comparison of the proposed method and a physics-based method suggests pursuit of a combined approach because their strengths appear to complement each other.

Rice, PE, Gurchiek, RD, and McBride, JM. Physiological and biomechanical responses to an acute bout of high kicking in dancers. J Strength Cond Res 32(10): 2954-2961, 2018-High-kick precision competitive dance involves the integration of kicking inline, jumping, and turning during a 3-minute routine and causes a disturbance to both aerobic and anaerobic systems. However, no known study has quantified the effect of a hick-kick-specific protocol on blood lactate (BL), heart rate (HR), and countermovement jump (CMJ) performance. A group of adolescent high-kick dancers (n = 20; age = 15.0 ± 1.8 years; height = 161.5 ± 8.2 cm; body mass = 56.3 ± 11.0 kg; and dance team/weight lifting experience = 2.1 ± 0.9 years) participated in this investigation. Participants' BL was collected, and 3 CMJ trials were performed before (PRE) and after (POST) completing the high-kick protocol. The high-kick protocol consisted of intermittent running and kicking in place for a total of 1:45 at a tempo of 160 b·min, while average and peak HR (HRavg; HRpeak) were recorded. Jump height (JH), maximal trunk flexion (MTF), take-off angle (TOA), and relative peak power (PP) were determined from PRE and POST CMJ trials. During kicking, HRavg and HRpeak (n = 13) were 172.4 ± 15.7 b·min and 207.0 ± 23.0 b·min, respectively. Dancers' (n = 20) BL significantly (p ≤ 0.05) increased from 1.5 ± 0.5 to 8.6 ± 1.5 mmol·L, and JH and PP significantly decreased from 0.42 ± 0.08 to 0.36 ± 0.04 m and 41.3 ± 5.5 to 38.5 ± 4.0 W·kg, respectively, from PRE to POST. No significant changes occurred in MTF and TOA measurements from PRE to POST. The demands introduced during a high-kick-specific protocol can therefore be recognized as a metabolic and mechanical stimulus for fatigue; however, esthetics seem to be unaffected. Such findings may indicate that this modality of dance necessitates supplemental strength and conditioning training similar to other sports to sustain the loads of high-kick dance.

Dance involves a high volume of aesthetic, stretch-shortening cycle (SSC) actions, which may cause unique adaptations to performance. The strength dancers possess to withstand such frequency of SSCs remains elusive. The extensive training that dancers experience from a young age, however, yields anatomical and strength development that may contrast with that of untrained individuals. Therefore, the purpose of this study was to investigate differences in musculo-articular stiffness and maximal isometric plantar flexion and knee extension force between dancers and untrained individuals. A total of 16 females volunteered to participate in the study (N = 8 dancers; N = 8 untrained individuals). Dancers had a minimum of 10 years of dance experience and were currently training at the collegiate dance level three or more times per week. Untrained individuals had no dance background, nor were they currently involved in any form of regularized physical activity. All subjects completed a series of lower leg measurements and strength tests. This included a musculo-articular stiffness measurement using a free-oscillation technique, along with maximal isometric plantar flexion (MIP) and maximal isometric knee extension (MIKE) testing. The data indicate that dancers had a significantly greater rate of force development and peak force during MIP and rate of force development during MIKE in comparison to untrained individuals. Dancers also possessed significantly greater musculo-articular stiffness. Hence, the data provide some evidence that involvement in dance can result in greater muscle force generating capacity and musculo-articular stiffness due to the SSC mechanisms involved in dance movements.

The purpose of this investigation was to determine the feasibility of using a single inertial measurement unit (IMU) placed on the sacrum to estimate 3-dimensional ground reaction force (F) during linear acceleration and change of direction tasks. Force plate measurements of F and estimates from the proposed IMU method were collected while subjects (n=15) performed a standing sprint start (SS) and a 45° change of direction task (COD). Error in the IMU estimate of step-averaged component and resultant F was quantified by comparison to estimates from the force plate using Bland-Altman 95% limits of agreement (LOA), root mean square error (RMSE), Pearson's product-moment correlation coefficient (r), and the effect size (ES) of the differences between the two systems. RMSE of the IMU estimate of step-average F ranged from 37.70 N to 77.05 N with ES between 0.04 and 0.47 for SS while for COD, RMSE was between 54.19 N to 182.92 N with ES between 0.08 and 1.69. Correlation coefficients between the IMU and force plate measurements were significant (p≤0.05) for all values (r=0.53 to 0.95) except the medio-lateral component of step-average F. The average angular error in the IMU estimate of the orientation of step-average F was ≤10° for all tasks. The results of this study suggest the proposed IMU method may be used to estimate sagittal plane components and magnitude of step-average F during a linear standing sprint start as well as the vertical component and magnitude of step-average F during a 45° change of direction task., (Copyright © 2017 Elsevier Ltd. All rights reserved.)