Resonance tuning of rhythmic movements is disrupted at short time scales: A centrifuge study.

The human body exploits its neural mechanisms to optimize actions. Rhythmic movements are optimal when their frequency is close to the natural frequency of the system. In a pendulum, gravity modulates this spontaneous frequency. Participants unconsciously adjust their natural pace when cyclically moving the arm in altered gravity. However, the timescale of this adaptation is unexplored. Participants performed cyclic movements before, during, and after fast transitions between hypergravity levels (1g-3g and 3g-1g) induced by a human centrifuge. Movement periods were modulated with the average value of gravity during transitions. However, while participants increased movement pace on a cycle basis when gravity increased (1g-3g), they did not decrease pace when gravity decreased (3g-1g). We highlight asymmetric effects in the spontaneous adjustment of movement dynamics on short timescales, suggesting the involvement of cognitive factors, beyond standard dynamical models.

Occupational Risk Factors for Musculoskeletal Disorders among Workers in Dairy Diversification.

BACKGROUND: In a changing European agricultural context, diversification of dairy farms is gaining attention. This study seeks to (1) assess musculoskeletal pain prevalence associated with tasks such as butter, yogurt, and cheese production; and (2) analyze associated risks. METHODS: Observing 31, mostly female, workers, we utilized the ERGOROM questionnaire, a methodology adapted from the Institut National de Recherche et de Sécurité, and Key Indicator Method forms. RESULTS: Findings revealed that tasks like load carrying (42% of workers), manual work (17%), and awkward postures (14%) resulted in musculoskeletal pain, predominantly in the lower back (65%), neck (39%), and dominant upper limb areas (shoulder: 61%, elbow: 26%, and wrist: 65%). While psychosocial risks remained low, concerns arose from workload, hygiene standards, and resource unpredictability. CONCLUSIONS: As dairy farming evolves from artisanal to semi-industrial, our study emphasizes the importance of ergonomic adaptations to protect farmers' health and prevent musculoskeletal disorders during diversification.

Quasi-experimental pilot study to improve mobility and balance in recurrently falling nursing home residents by voluntary non-targeted side-stepping exercise intervention.

BACKGROUND: Side-stepping is a potential exercise program to reduce fall risk in community-dwelling adults in their seventies, but it has never been tested in nursing home residents. This was a pilot quasi-experimental study to examine the feasibility and potential mobility and balance benefits of an intervention based on voluntary non-targeted side-stepping exercises in nursing home residents who fall recurrently. METHODS: Twenty-two participants were recruited and non-randomly assigned to an intervention group ([Formula: see text]11, side-stepping exercises, STEP) participating in an 8-week protocol and to a control group ([Formula: see text]11, usual physiotherapy care, CTRL). They were clinically assessed at 4-time points: baseline, after 4 and 8 weeks, and after a 4-week follow-up period (usual physiotherapy care). Statistical differences between time points were assessed with a Friedman repeated measures ANOVA on ranks or a one-way repeated measures ANOVA. RESULTS: Compared to baseline, significant benefits were observed in the STEP group at 8 weeks for the Timed Up and Go ([Formula: see text]0.020) and 6-minute walking test ([Formula: see text]0.001) as well as for the Berg Balance Scale ([Formula: see text]0.041) and Mini motor test ([Formula: see text]0.026). At follow-up, the Tinetti Performance Oriented Mobility Assessment and Berg Balance Scale significantly worsened in the STEP group ([Formula: see text]0.009 and [Formula: see text]0.001, respectively). No significant differences were found between the groups at the same time points. CONCLUSIONS: Our intervention was feasible and improved mobility and balance after almost 8 weeks. Studies with larger samples and randomized control trials are needed to consolidate our preliminary observations and confirm the deterioration of some tests when side-stepping exercises are discontinued. TRIAL REGISTRATION: Identifier: ISRCTN13584053. Retrospectively registered 01/09/2022.

Comparison of the movement behaviour of experienced and novice performers during the Cat exercise.

Two previous studies showed kinematic differences between novice and experienced performers during unchoreographed movements executed in standing position. However, no study explores if these kinematic differences holds during unchoreographed movements executed in quadrupedal position. The aim of this study is to compare the movement behaviour of experienced and novice performers during an exercise wherein they are challenged to use dynamic and largely unchoreographed movement patterns executed in quadrupedal position. The exercise studied was the Cat exercise, in which participants were asked to behave like a feline for 10 minutes. An inventory of the chosen movements and the assessment of their average and coefficient of variation of the ground contact temporal parameters, computed by analysing the tri-dimensional whole-body kinematics of 25 performers (n = 13 novices and n = 12 experienced), was compared according to their experience level. No significant difference was found between the groups for the number of chosen movements, and median or coefficient of variation of ground contact temporal parameters, except for a greater foot/ knee swing coefficient of variation in experienced performers. This suggests that biomechanical constraints induced by quadrupedal position "prevent" a different selection of motor strategies by experienced performers, although the latter can be more variable in their movements.

Wearable Sensors Applied in Movement Analysis.

Recent advances in the miniaturization of electronics have resulted in sensors whose sizes and weights are such that they can be attached to living systems without interfering with their natural movements and behaviors [...].

Adiabatic Invariant of Center-of-Mass Motion during Walking as a Dynamical Stability Constraint on Stride Interval Variability and Predictability.

Human walking exhibits properties of global stability, and local dynamic variability, predictability, and complexity. Global stability is typically assessed by quantifying the whole-body center-of-mass motion while local dynamic variability, predictability, and complexity are assessed using the stride interval. Recent arguments from general mechanics suggest that the global stability of gait can be assessed with adiabatic invariants, i.e., quantities that remain approximately constant, even under slow external changes. Twenty-five young healthy participants walked for 10 min at a comfortable pace, with and without a metronome indicating preferred step frequency. Stride interval variability was assessed by computing the coefficient of variation, predictability using the Hurst exponent, and complexity via the fractal dimension and sample entropy. Global stability of gait was assessed using the adiabatic invariant computed from averaged kinetic energy value related to whole-body center-of-mass vertical displacement. We show that the metronome alters the stride interval variability and predictability, from autocorrelated dynamics to almost random dynamics. However, despite these large local variability and predictability changes, the adiabatic invariant is preserved in both conditions, showing the global stability of gait. Thus, the adiabatic invariant theory reveals dynamical global stability constraints that are "hidden" behind apparent local walking variability and predictability.

Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test.

Nowadays, the better assessment of low back pain (LBP) is an important challenge, as it is the leading musculoskeletal condition worldwide in terms of years of disability. The objective of this study was to evaluate the relevance of various machine learning (ML) algorithms and Sample Entropy (SampEn), which assesses the complexity of motion variability in identifying the condition of low back pain. Twenty chronic low-back pain (CLBP) patients and 20 healthy non-LBP participants performed 1-min repetitive bending (flexion) and return (extension) trunk movements. Analysis was performed using the time series recorded by three inertial sensors attached to the participants. It was found that SampEn was significantly lower in CLBP patients, indicating a loss of movement complexity due to LBP. Gaussian Naive Bayes ML proved to be the best of the various tested algorithms, achieving 79% accuracy in identifying CLBP patients. Angular velocity of flexion movement was the most discriminative feature in the ML analysis. This study demonstrated that: supervised ML and a complexity assessment of trunk movement variability are useful in the identification of CLBP condition, and that simple kinematic indicators are sensitive to this condition. Therefore, ML could be progressively adopted by clinicians in the assessment of CLBP patients.

Sample Entropy as a Tool to Assess Lumbo-Pelvic Movements in a Clinical Test for Low-Back-Pain Patients.

Low back pain (LBP) obviously reduces the quality of life but is also the world's leading cause of years lived with disability. Alterations in motor response and changes in movement patterns are expected in LBP patients when compared to healthy people. Such changes in dynamics may be assessed by the nonlinear analysis of kinematical time series recorded from one patient's motion. Since sample entropy (SampEn) has emerged as a relevant index measuring the complexity of a given time series, we propose the development of a clinical test based on SampEn of a time series recorded by a wearable inertial measurement unit for repeated bending and returns (b and r) of the trunk. Twenty-three healthy participants were asked to perform, in random order, 50 repetitions of this movement by touching a stool and another 50 repetitions by touching a box on the floor. The angular amplitude of the b and r movement and the sample entropy of the three components of the angular velocity and acceleration were computed. We showed that the repetitive b and r "touch the stool" test could indeed be the basis of a clinical test for the evaluation of low-back-pain patients, with an optimal duration of 70 s, acceptable in daily clinical practice.

Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test.

Understanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight into the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several machine learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients.

High Specificity of Single Inertial Sensor-Supplemented Timed Up and Go Test for Assessing Fall Risk in Elderly Nursing Home Residents.

The Timed Up and Go test (TUG) is commonly used to estimate the fall risk in the elderly. Several ways to improve the predictive accuracy of TUG (cameras, multiple sensors, other clinical tests) have already been proposed. Here, we added a single wearable inertial measurement unit (IMU) to capture the residents' body center-of-mass kinematics in view of improving TUG's predictive accuracy. The aim is to find out which kinematic variables and residents' characteristics are relevant for distinguishing faller from non-faller patients. Data were collected in 73 nursing home residents with the IMU placed on the lower back. Acceleration and angular velocity time series were analyzed during different subtasks of the TUG. Multiple logistic regressions showed that total time required, maximum angular velocity at the first half-turn, gender, and use of a walking aid were the parameters leading to the best predictive abilities of fall risk. The predictive accuracy of the proposed new test, called i + TUG, reached a value of 74.0%, with a specificity of 95.9% and a sensitivity of 29.2%. By adding a single wearable IMU to TUG, an accurate and highly specific test is therefore obtained. This method is quick, easy to perform and inexpensive. We recommend to integrate it into daily clinical practice in nursing homes.

Perceived Usefulness of Telerehabilitation of Musculoskeletal Disorders: A Belgium-France Pilot Study during Second Wave of COVID-19 Pandemic.

BACKGROUND: COVID-19 has affected the practice of physiotherapy, and telerehabilitation (TR) may be seen as an alternative model of care if it is accepted by patients and physiotherapists. This study investigates the perceived usefulness of TR and the intention to use it among physiotherapists and patients from Belgium and France concerned with musculoskeletal disorders (MSDs) during the pandemic period. METHODS: An online questionnaire based on the technology-acceptance model was designed. Sociodemographic data were collected and Likert scales were proposed to assess perceived ease-of-use, perceived usefulness and intention to use TR. Data were collected between 17 January and 17 March 2021; 68 patients and 107 physiotherapists answered. RESULTS: In total, 88% of patients and 76% physiotherapists had not used TR at the time they answered. Only 12% of patients and 1% of physiotherapists are willing to use TR, and 50% of physiotherapists think they will never use TR compared to 25% of patients. A total of 98% of participants agreed that they had a good mastery of the technological tools requested. CONCLUSIONS: Physiotherapists are more reluctant to use TR than patients, regardless of convincing EBM results. This is related to their own representation of proper MSD management, which must include the use of hands-on techniques.

Fine adaptive precision grip control without maximum pinch strength changes after upper limb neurodynamic mobilization.

Before and immediately after passive upper limb neurodynamic mobilizations targeting the median nerve, grip ([Formula: see text]) and load ([Formula: see text]) forces applied by the thumb, index and major fingers (three-jaw chuck pinch) were collected using a manipulandum during three different grip precision tasks: grip-lift-hold-replace (GLHR), vertical oscillations (OSC), and vertical oscillations with up and down collisions (OSC/COLL/u, OSC/COLL/d). Several parameters were collected or computed from [Formula: see text] and [Formula: see text]. Maximum pinch strength and fingertips pressure sensation threshold were also examined. After the mobilizations, [Formula: see text] max changes from 3.2 ± 0.4 to 3.4 ± 0.4 N (p = 0.014), d[Formula: see text] from 89.0 ± 66.6 to 102.2 ± 59.6 [Formula: see text] (p = 0.009), and d[Formula: see text] from 43.6 ± 17.0 to 56.0 ± 17.9 [Formula: see text] ([Formula: see text]0.001) during GLHR. [Formula: see text] SD changes from 0.9 ± 0.3 to 1.0 ± 0.2 N (p = 0.004) during OSC. [Formula: see text] peak changes from 17.4 ± 8.3 to 15.1 ± 7.5 N ([Formula: see text]0.001), [Formula: see text] from 12.4 ± 6.7 to 11.3 ± 6.8 N (p = 0.033), and [Formula: see text] from 2.9 ± 0.4 to 3.00 ± 0.4 N (p = 0.018) during OSC/COLL/u. [Formula: see text] peak changes from 13.5 ± 7.4 to 12.3 ± 7.7 N (p = 0.030) and [Formula: see text] from 14.5 ± 6.0 to 13.6 ± 5.5 N (p = 0.018) during OSC/COLL/d. Sensation thresholds at index and thumb were reduced (p = 0.001, p = 0.008). Precision grip adaptations observed after the mobilizations could be partly explained by changes in cutaneous median-nerve pressure afferents from the thumb and index fingertips.

Kinematics and Esthetics of Grand Battement After Static and Dynamic Hamstrings Stretching in Adolescents.

The objective of this study was to explore the effects of static and dynamic hamstring muscles stretching on kinematics and esthetics of grand battement (high velocity kicks) in adolescent recreational dancers. Sixteen participants were assessed before and immediately after both stretching modalities. Kinematics of movement was measured by an optoelectronic system and esthetics was scored by a jury of professional dancers. Both stretching modalities led to significant kinematic differences compared with without stretching. Significant linear correlations between kinematic parameters and esthetic scores have been observed: improving dancers' physical performances has noticeable impact on the perception of their movements.

Low-Cost Sensors and Biological Signals.

Low-cost sensors, i [...].

Asymptomatic Genu Recurvatum reshapes lower limb sagittal joint and elevation angles during gait at different speeds.

BACKGROUND: Kinematic characteristics of walking with an asymptomatic genu recurvatum are currently unknown. The objective of this study is to characterize the lower limb sagittal joint and elevation angles during walking in participants with asymptomatic genu recurvatum and compare it with control participants without knee deformation at different speeds. METHODS: The spatio-temporal parameters and kinematics of the lower limb were recorded using an optoelectronic motion capture system in 26 participants (n = 13 with genu recurvatum and n = 13 controls). The participants walked on an instrumented treadmill during five minutes at three different speeds: slow, medium and fast. RESULTS: Participants with genu recurvatum showed several significant differences with controls: a narrower step width, a greater maximum hip joint extension angle, a greater knee joint extension angle at mid stance, a lower maximum knee joint flexion angle during the swing phase, and a greater ankle joint extension angle at the end of the gait cycle. Participants with genu recurvatum had a greater minimum thigh elevation angle, a greater maximum foot elevation angle, and a change in the orientation of the covariance plane. Walking speed had a significant effect on nearly all lower limb joint and elevation angles, and covariance plane parameters. CONCLUSION: Our findings show that genu recurvatum reshapes lower limb sagittal joint and elevation angles during walking at different speeds but preserves the covariation of elevation angles along a plane during both stance and swing phases and the rotation of this plane with increasing speed.

Benefits of nonlinear analysis indices of walking stride interval in the evaluation of neurodegenerative diseases.

Indices characterising the long-range temporal structure of walking stride interval (SI) variability such as Hurst exponent (H) and fractal dimension (D) may be used in addition to indices measuring the amount of variability like the coefficient of variation (CV). We assess the added value of the former indices in a clinical neurological context. Our aim is to demonstrate that they provide a clinical significance in aging and in frequent neurodegenerative diseases such as Parkinson's disease, Huntington, and amyotrophic lateral sclerosis. Indices assessing the temporal structure of variability are mainly dependent on SI time series length and algorithms used, making quantitative comparisons between different studies difficult or even impossible. Here, we recompute these indices from available SI time series, either from our lab or from online databases. More precisely, we recompute CV, H, and D in a unified way. The average SI is also added to the measured parameters. We confirm that variability indices are relevant indicators of aging process and neurodegenerative diseases. While CV is sensitive to aging process and pathology, it does not discriminate between specific neurodegenerative diseases. H, which measures predictability of SI, significantly decreases with age but increases in patients suffering from amyotrophic lateral sclerosis. D, catching complexity of SI, is correlated with total functional capacity in patients with Huntington's disease. We conclude that the computation of H complements the clinical diagnosis of walking in patients with neurodegenerative diseases and we recommend it as a relevant supplement to classical CV or averaged SI. Since H and D indices did not lead to the same observations, suggesting the multi-fractal nature of SI dynamics, we recommend to open clinical gait analysis to the evaluation of more parameters.

Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People.

Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall's prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants.

Digital natives and dual task: Handling it but not immune against cognitive-locomotor interferences.

Digital natives developed in an electronic dual tasking world. This paper addresses two questions. Do digital natives respond differently under a cognitive load realized during a locomotor task in a dual-tasking paradigm and how does this address the concept of safety? We investigate the interplay between cognitive (talking and solving Raven's matrices) and locomotor (walking on a treadmill) tasks in a sample of 17 graduate level participants. The costs of dual-tasking on gait were assessed by studying changes in stride interval time and its variability at long-range. A safety index was designed and computed from total relative change between the variability indices in the single walking and dual-task conditions. As expected, results indicate high Raven's scores with gait changes found between the dual task conditions compared to the single walking task. Greater changes are observed in the talking condition compared to solving Raven's matrices, resulting in high safety index values observed in 5 participants. We conclude that, although digital natives are efficient in performing the dual tasks when they are not emotional-based, modification of gait are observable. Due to the variation within participants and the observation of high safety index values in several of them, individuals that responded poorly to low cognitive loads should be encouraged to not perform dual task when executing a primate task of safety to themselves or others.

DYSKIMOT: An Ultra-Low-Cost Inertial Sensor to Assess Head's Rotational Kinematics in Adults during the Didren-Laser Test.

Various noninvasive measurement devices can be used to assess cervical motion. The size, complexity, and cost of gold-standard systems make them not suited to clinical practice, and actually difficult to use outside a dedicated laboratory. Nowadays, ultra-low-cost inertial measurement units are available, but without any packaging or a user-friendly interface. The so-called DYSKIMOT is a home-designed, small-sized, motion sensor based on the latter technology, aiming at being used by clinicians in "real-life situations". DYSKIMOT was compared with a gold-standard optoelectronic system (Elite). Our goal was to evaluate the DYSKIMOT accuracy in assessing fast head rotations kinematics. Kinematics was simultaneously recorded by systems during the execution of the DidRen Laser test and performed by 15 participants and nine patients. Kinematic variables were computed from the position, speed and acceleration time series. Two-way ANOVA, Passing-Bablok regressions, and dynamic time warping analysis showed good to excellent agreement between Elite and DYSKIMOT, both at the qualitative level of the time series shape and at the quantitative level of peculiar kinematical events' measured values. In conclusion, DYSKIMOT sensor is as relevant as a gold-standard system to assess kinematical features during fast head rotations in participants and patients, demonstrating its usefulness in both clinical practice and research environments.