BackgroundBody weight support (BWS) systems have shown promise as rehabilitation tools for neurologically impaired individuals. This paper reviews the experiment-based research on BWS systems with the aim: (1) To investigate the influence of body weight unloading (BWU) on gait characteristics; (2) To study whether the effects of BWS differ between treadmill and overground walking and (3) To investigate if modulated BWU influences gait characteristics less than unmodulated BWU.MethodA systematic literature search was conducted in the following search engines: Pubmed, Scopus, Web of Science and Google Scholar. Statistical analysis was used to quantify the effects of BWU on gait parameters.Results54 studies of experiments with healthy and neurologically impaired individuals walking in a BWS system were included and 32 of these were used for the statistical analysis. Literature was classified using three distinctions: (1) treadmill or overground walking; (2) the type of subjects and (3) the nature of unloading force. Only 27% studies were based on neurologically impaired subjects; a low number considering that they are the primary user group for BWS systems. The studies included BWU from 5% to 100% and the 30% and 50% BWU conditions were the most widely studied. The number of participants varied from 1 to 28, with an average of 12. It was seen that due to the increase in BWU level, joint moments, muscle activity, energy cost of walking and ground reaction forces (GRF) showed higher reduction compared to gait spatio-temporal and joint kinematic parameters. The influence of BWU on kinematic and spatio-temporal gait parameters appeared to be limited up to 30% unloading. 5 gait characteristics presented different behavior in response to BWU for overground and treadmill walking. Remaining 21 gait characteristics showed similar behavior but different magnitude of change for overground and treadmill walking. Modulated unloading force generally led to less difference from the 0% condition than unmodulated unloading.ConclusionThis review has shown that BWU influences all gait characteristics, albeit with important differences between the kinematic, spatio-temporal and kinetic characteristics. BWU showed stronger influence on the kinetic characteristics of gait than on the spatio-temporal parameters and the kinematic characteristics. It was ascertained that treadmill and overground walking can alter the effects of BWU in a different manner. Our results indicate that task-specific gait training is likely to be achievable at a BWU level of 30% and below.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0380-0) contains supplementary material, which is available to authorized users.

Objective: To investigate (i) typical protocols used in research on biomechanical response to running-induced fatigue, (ii) the effect of sport-induced acute fatigue on the biomechanics of running and functional tests, and (iii) the consistency of analyzed parameter trends across different protocols.Methods: Scopus, Web of Science, Pubmed, and IEEE databases were searched using terms identified with the Population, Interest and Context (PiCo) framework. Studies were screened following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and appraised using the methodological index for non-randomized studies MINORS scale. Only experimental studies with at least 10 participants, which evaluated fatigue during and immediately after the fatiguing run were included. Each study was summarized to record information about the protocol and parameter trends. Summary trends were computed for each parameter based on the results found in individual studies.Results: Of the 68 included studies, most were based on in-lab (77.9%) protocols, endpoint measurements (75%), stationary measurement systems (76.5%), and treadmill environment (54.4%) for running. From the 42 parameters identified in response to acute fatigue, flight time, contact time, knee flexion angle at initial contact, trunk flexion angle, peak tibial acceleration, CoP velocity during balance test showed an increasing behavior and cadence, vertical stiffness, knee extension force during MVC, maximum vertical ground reaction forces, and CMJ height showed a decreasing trend across different fatigue protocols.Conclusion: This review presents evidence that running-induced acute fatigue influences almost all the included biomechanical parameters, with crucial influence from the exercise intensity and the testing environment. Results indicate an important gap in literature caused by the lack of field studies with continuous measurement during outdoor running activities. To address this gap, we propose recommendations for the use of wearable inertial sensors.

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Understanding the influence of running-induced acute fatigue on the homeostasis of the body is essential to mitigate the adverse effects and optimize positive adaptations to training. Fatigue is a multifactorial phenomenon, which influences biomechanical, physiological, and psychological facets. This work aimed to assess the evolution of these three facets with acute fatigue during a half-marathon. 13 recreational runners were equipped with one inertial measurement unit (IMU) on each foot, one combined global navigation satellite system-IMU-electrocardiogram sensor on the chest, and an Android smartphone equipped with an audio recording application. Spatio-temporal parameters for the running gait, along with the heart rate, its variability and complexity were computed using validated algorithms. Perceived fatigability was assessed using the rating-of-fatigue (ROF) scale at every 10 min of the race. The data was split into eight equal segments, corresponding to at least one ROF value per segment, and only level running parts were retained for analysis. During the race, contact time, duty factor, and trunk anteroposterior acceleration increased, and the foot strike angle and vertical stiffness decreased significantly. Heart rate showed a progressive increase, while the metrics for heart rate variability and complexity decreased during the race. The biomechanical parameters showed a significant alteration even with a small change in perceived fatigue, whereas the heart rate dynamics altered at higher changes. When divided into two groups, the slower runners presented a higher change in heart rate dynamics throughout the race than the faster runners; they both showed similar trends for the gait parameters. When tested for linear and non-linear correlations, heart rate had the highest association with biomechanical parameters, while the trunk anteroposterior acceleration had the lowest association with heart rate dynamics. These results indicate the ability of faster runners to better judge their physiological limits and hint toward a higher sensitivity of perceived fatigue to neuromuscular changes in the running gait. This study highlights measurable influences of acute fatigue, which can be studied only through concurrent measurement of biomechanical, physiological, and psychological facets of running in real-world conditions.

Power-Force-Velocity profile obtained during a sprint test is crucial for designing personalized training and evaluating injury risks. Estimation of instantaneous velocity is requisite for developing these profiles and the predominant method for this estimation assumes it to have a first order exponential behavior. While this method remains appropriate for maximal sprints, the sprint velocity profile may not always show a first-order exponential behavior. Alternately, velocity profile has been estimated using inertial sensors, with a speed radar, or a smartphone application. Existing methods either relied on the exponential behavior or timing gates for drift removal, or estimated only the mean velocity. Thus, there is a need for a more flexible and appropriate approach, allowing for instantaneous velocity estimation during sprint tests. The proposed method aims to solve this problem using a sensor fusion approach, by combining the signals from wearable Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) sensors. We collected data from nine elite sprinters, equipped with a wearable GNSS-IMU sensor, who ran two trials each of 60 and 30/40 m sprints. We developed an algorithm using a gradient descent-based orientation filter, which simplified our model to a linear one-dimensional model, thus allowing us to use a simple Kalman filter (KF) for velocity estimation. We used two cascaded KFs, to segment the sprint data precisely, and to estimate the velocity and the sprint duration, respectively. We validated the estimated velocity and duration with speed radar and photocell data as reference. The median RMS error for the estimated velocity ranged from 6 to 8%, while that for the estimated sprint duration lied between 0.1 and −6.0%. The Bland–Altman plot showed close agreement between the estimated and the reference values of maximum velocity. Examination of fitting errors indicated a second order exponential behavior for the sprint velocity profile, unlike the first order behavior previously suggested in literature. The proposed sensor-fusion algorithm is valid to compute an accurate velocity profile with respect to the radar; it can compensate for and improve upon the accuracy of the individual IMU and GNSS velocities. This method thus enables the use of wearable sensors in the analysis of sprint test.