Low back pain (LBP) is an understudied condition among runners, and it is unclear what biomechanical features could be targeted for gait retraining to mitigate pain.
How do running biomechanics ...differ between healthy individuals and those with running-related LBP?
This was a case-controlled, comparative study design of community runners: running-related LBP (n=52) and healthy controls (n=52). All runners completed running history forms and performed a 3-dimensional gait analysis. Kinematic data were collected using a motion capture system and normalized to a gait cycle, while participants ran on a level grade at self-selected speed on an instrumented treadmill. Current running volume, temporal-spatial, kinetic and kinematic features were compared between groups.
The LBP group had 39.5 % lower weekly distance and 15.4 % fewer were currently training for a race (all p<.05). Runners with LBP demonstrated lower cadence (166±10 step/min vs. 171±9 step/min; p=.05), greater center of gravity lateral displacement (1.4±0.5 cm vs. 1.2 ±.3 cm; p=.044) and greater stride width variability (1.3±0.4 cm versus 1.0 ± 0.04 cm; p=.008). Runners with LBP had a greater Vertical Average Loading Rate (VALR 67.7±22.2 bodyweights BW/s vs. 62.2±21.5 BW/s; p=.022), and higher joint moments (N*m/(kg*m)) at the knee in the sagittal plane (2.13±0.50 vs. 1.87±0.56; p <.001), frontal plane (1.44±0.39 vs. 1.29±0.29; p=.013), and at the hip in the frontal plane (2.04±0.51 vs. 1.84±0.41; p=.024). No differences were found between groups in the pelvis, hip, knee, and ankle joint excursions in any plane of motion during a typical gait cycle.
These collective motion signature may reflect challenges with control of motion and VALR in the presence of back pain. Cadence training to increase step rate, coupled with core/hip muscle activation, may be an important strategy to reduce motion variability, impact loading rate and pain symptoms while running.
•LBP is associated with higher impact loading rates during running.•Cadence and variability of center of mass motion may relate to LBP in runners.•Retraining for LBP may target cadence, impact dampening and core muscle activation.
•A comprehensive survey of biometric gait recognition based on vision, underfoot pressure, accelerometry, and audio sensory modalities.•A review of the factors that impact gait recognition ...performance (e.g., walking speed, clothing, footwear, etc.) and the influence of time lapse.•A discussion on the future of gait biometrics and the challenges and open problems that are yet to be addressed in the field.
The scientific literature on automated gait analysis for human recognition has grown dramatically over the past 15 years. A number of sensing modalities including those based on vision, sound, pressure, and accelerometry have been used to capture gait information. For each of these modalities, a number of methods have been developed to extract and compare human gait information, resulting in different sets of features. This paper provides an extensive overview of the various types of features that have been utilized for each sensing modality and their relationship to the appearance and biomechanics of gait. The features considered in this work include (a) static and dynamic (temporal) features; (b) model-based and model-free visual features; (c) ground reaction force-based and finely resolved underfoot pressure features; (d) wearable sensor features; and (e) acoustic features. We also review the factors that impact gait recognition, and discuss recent work on gait spoofing and obfuscation. Finally, we enumerate the challenges and open problems in the field of gait recognition.
Background:
Osteoarthritis of the medial tibiofemoral joint (MTFJ) is prevalent among patients undergoing anterior cruciate ligament reconstruction (ACLR). Magnetic resonance T1ρ and T2 relaxation ...times provide noninvasive methods to quantify early cartilage degeneration. Altered sagittal-plane gait biomechanics have been observed after ACLR, but their associations with longitudinal changes in MTFJ cartilage T1ρ and T2 remain unclear.
Hypothesis/Purpose:
To examine whether the peak knee flexion moment (KFM), knee flexion angle (KFA), and vertical ground-reaction force (vGRF) during gait are associated with prospective changes in medial tibiofemoral cartilage T1ρ and T2 in ACL-reconstructed knees and to compare these gait characteristics between patients undergoing ACLR and healthy control participants. We hypothesized that a higher KFM, KFA, and vGRF would be associated with greater increases in cartilage relaxation times and that patients undergoing ACLR would demonstrate altered gait characteristics compared with healthy controls.
Study Design:
Controlled laboratory study.
Methods:
Thirty-three patients undergoing ACLR underwent gait analysis before and 6 months and 1 year after ACLR and knee magnetic resonance imaging (MRI) before and 6 months, 1 year, and 2 years after ACLR. Twelve healthy controls underwent knee MRI and gait analysis at baseline and 1 year. Cartilage T1ρ and T2 were calculated for the medial tibia and medial femoral condyle. Linear regressions were used to evaluate associations between gait characteristics and changes in cartilage relaxation times from before ACLR to follow-up time points. Independent t tests were used to compare differences in gait between patients undergoing ACLR and control participants.
Results:
A higher KFM and KFA before ACLR were related to greater increases in medial femoral condyle T1ρ and T2 at 6 months after ACLR. Similarly, a higher KFM, KFA, and vGRF at 6 months were associated with greater increases in medial tibia and medial femoral condyle T1ρ and T2 at 1 and 2 years after ACLR. Gait characteristics at 1 year were not associated with changes in cartilage relaxation times at 2 years after ACLR. Compared with healthy controls, patients undergoing ACLR demonstrated a lower KFM at 6 months after ACLR.
Conclusion/Clinical Relevance:
The findings of this study revealed that a higher KFM, KFA, and vGRF during gait, especially at 6 months after ACLR, were associated with greater deterioration of MTFJ cartilage health at later time points.
Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact ...with the ground and is given by the so called ground reaction force (GRF). A major challenge in the reconstruction of GRF from kinematic data is the double support phase, referring to the state with multiple ground contacts. In this case, the GRF prediction is not well defined. In this work we present an approach to reconstruct and distribute vertical GRF (vGRF) to each foot separately, using only kinematic data. We propose the biomechanically inspired force shadow method (FSM) to obtain a unique solution for any contact phase, including double support, of an arbitrary motion. We create a kinematic based function, model an anatomical foot shape and mimic the effect of hip muscle activations. We compare our estimations with the measurements of a Zebris pressure plate and obtain correlations of 0.39≤r≤0.94 for double support motions and 0.83≤r≤0.87 for a walking motion. The presented data is based on inertial human motion capture, showing the applicability for scenarios outside the laboratory. The proposed approach has low computational complexity and allows for online vGRF estimation.
Walking is one of the most common daily movements of the human body. Therefore, quantitative evaluation of human walking has been commonly used to assist doctors in grasping the disease degree and ...rehabilitation process of patients in the clinic. Compared with the kinematic characteristics, the ground reaction force (GRF) during walking can directly reflect the dynamic characteristics of human walking. It can further help doctors understand the degree of muscle recovery and joint coordination of patients. This paper proposes a GRF estimation method based on the elastic elements and Newton-Euler equation hybrid driving GRF estimation method. Compared with the existing research, the innovations are as follows. i) The hardware system consists of only two inertial measurement units (IMUs) placed on shanks. The acquisition of the overall motion characteristics of human walking is realized through the simplified four-link walking model and the thigh prediction method. ii) The method was validated not only on 10 healthy subjects but also on 11 Parkinson's patients and 10 stroke patients with normalized mean absolute errors (NMAEs) of 5.95%±1.32%, 6.09%±2.00%, 5.87%±1.59%. iii) This paper proposes a dynamic balance assessment method based on the acquired motion data and the estimated GRF. It evaluates the overall balance ability and fall risk at four key time points for all subjects recruited. Because of the low-cost system, ease of use, low motion interference and environmental constraints, and high estimation accuracy, the proposed GRF estimation method and walking balance automatic assessment have broad clinical value.
As a direct response to robot movements and load distributions, the ground reaction force (GRF) is pivotal for heavy-legged robot (HLR) applications. This study presents a technical framework for GRF ...monitoring in an electric cylinder-driven HLR, eliminating the need for information on body motion, load distributions, and measured servo outputs. Traditional joint space-based dynamics are extended to include servo currents, compensating for the influence of unknown servo outputs. An essential contribution is incorporating the impact of floating bases and unknown loads into a virtual spatial force (VSF) applied to the HLR hip joint. The VSF is obtained through the nonlinear disturbance observer when the HLR is in a stable contact phase. Subsequently, a high-order GRF observer (HOGO), compensated with VSF, enables GRF observations without the pre-acquired body movement and load distribution data. In contrast to conventional GRF observations, the proposed framework could determine virtual payloads acting on the hip joint while ensuring precise GRF monitoring without requiring supplementary sensors. The GRF observations of the proposed framework are experimentally superior to those of the conventional methods with unknown HLR body motion and load information.
•Unknown loads are treated as virtual spatial forces and utilized for observing GRF.•The electrical architectures are considered to solve the unknown servo outputs.•A new contact force observer is constructed based on the centroid dynamics.
The primary goal of this study was to examine changes in peak insole force and cumulative weighted peak force (CWPF)/km with increased step rate in collegiate runners. The secondary goal was to ...determine whether sacral acceleration correlates with insole force when increasing step rate.
Twelve collegiate distance runners ran 1000 m outdoors at 3.83 m·s -1 at preferred and 10% increased step rates while insole force and sacral acceleration were recorded. Cumulative weighted peak force/km was calculated from insole force based on cumulative damage models. The effects of step rate on peak insole force and CWPF·km -1 were tested using paired t tests or Wilcoxon tests. Correlation coefficients between peak axial (approximately vertical) sacral acceleration times body mass and peak insole force were calculated on cohort and individual levels.
Peak insole force and CWPF·km -1 decreased ( P < 0.001) with increased step rate. Peak axial sacral acceleration did not correlate with peak insole force on the cohort level ( r = 0.35, P = 0.109) but did within individuals (mean, r = 0.69-0.78; P < 0.05).
Increasing step rate may reduce peak vGRF and CWPF·km -1 in collegiate runners. Therefore, clinicians should consider step rate interventions to reduce peak and cumulative vGRF in this population. Individual-specific calibrations may be required to assess changes in peak vGRF in response to increasing step rate using wearable accelerometers.
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