Biomechanical parameters for gait analysis: a systematic review of healthy human gait

Roberts, Mary B.; Mongeon, David; François, Patrice

doi:10.7243/2055-2386-4-6

Cited by 77 publications

(56 citation statements)

References 28 publications

(36 reference statements)

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“…This systematic review included the search of only three electronic databases (PubMed, Embase, and Web of Science) and this may be considered a limitation. However, these databases were selected for search because of their broad inclusion of multidisciplinary topics within the Biomedical and Health Sciences domain and because they have been particularly adopted in gait research reviews [18, 22, 64, 77]. In addition, only studies that employed 3D gait analysis instrumentation were included in this review and meta-analysis, which resulted in the majority of included studies being observational in nature.…”

Section: Discussionmentioning

confidence: 99%

“…This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [21] (Additional file 1) and was registered in PROSPERO (ID122769). All studies were identified by three electronic databases (PubMed, Embase, and Web of Science) which comprise the most topics within the Biomedical and Health Sciences area [22]. The specific search strategy is described in Additional file 2: Table S1.…”

Section: Methodsmentioning

confidence: 99%

“…To ensure identification of all relevant studies, the reference lists of relevant systematic reviews were hand-searched [18, 19, 22].…”

Section: Methodsmentioning

confidence: 99%

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Effects of walking speed on gait biomechanics in healthy participants: a systematic review and meta-analysis

2019

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Background Understanding the effects of gait speed on biomechanical variables is fundamental for a proper evaluation of alterations in gait, since pathological individuals tend to walk slower than healthy controls. Therefore, the aim of the study was to perform a systematic review of the effects of gait speed on spatiotemporal parameters, joint kinematics, joint kinetics, and ground reaction forces in healthy children, young adults, and older adults. Methods A systematic electronic search was performed on PubMed, Embase, and Web of Science databases to identify studies published between 1980 and 2019. A modified Quality Index was applied to assess methodological quality, and effect sizes with 95% confidence intervals were calculated as the standardized mean differences. For the meta-analyses, a fixed or random effect model and the statistical heterogeneity were calculated using the I 2 index. Results Twenty original full-length studies were included in the final analyses with a total of 587 healthy individuals evaluated, of which four studies analyzed the gait pattern of 227 children, 16 studies of 310 young adults, and three studies of 59 older adults. In general, gait speed affected the amplitude of spatiotemporal gait parameters, joint kinematics, joint kinetics, and ground reaction forces with a decrease at slow speeds and increase at fast speeds in relation to the comfortable speed. Specifically, moderate-to-large effect sizes were found for each age group and speed: children (slow, − 3.61 to 0.59; fast, − 1.05 to 2.97), young adults (slow, − 3.56 to 4.06; fast, − 4.28 to 4.38), and older adults (slow, − 1.76 to 0.52; fast, − 0.29 to 1.43). Conclusions This review identified that speed affected the gait patterns of different populations with respect to the amplitude of spatiotemporal parameters, joint kinematics, joint kinetics, and ground reaction forces. Specifically, most of the values analyzed decreased at slower speeds and increased at faster speeds. Therefore, the effects of speed on gait patterns should also be considered when comparing the gait analysis of pathological individuals with normal or control ones. Electronic supplementary material The online version of this article (10.1186/s13643-019-1063-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Effects of walking speed on gait biomechanics in healthy participants: a systematic review and meta-analysis

2019

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“…Our set of relevant gait parameters mainly consisted of standard spatial and temporal parameters commonly used in gait analysis such as stride length, gait velocity or cadence (Roberts et al, 2017). Since our gait analysis approach was motivated by the aim to identify people with signs of a walking disorder, we have included additional gait parameters that are indicative of a specific neurodegenerative disease such as NPH.…”

Section: Gait Parametersmentioning

confidence: 99%

Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments

et al. 2020

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Background: Walking patterns can provide important indications of a person's health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differently in lab versus real-world environments and whether age dependencies exist. Methods: The walking patterns of 20 young and 20 elderly healthy subjects were recorded with five wearable inertial measurement units (ZurichMOVE sensors) attached to both ankles, both wrists and the chest. An automated detection process based on dynamic time warping was developed to efficiently identify the relevant sequences. From the ZurichMOVE recordings, 15 spatio-temporal gait parameters were extracted, analyzed and compared between motion patterns captured in a controlled lab environment (10 m walking test) and the non-controlled ecologically valid real-world environment (72 h recording) in both groups. Results: Several parameters (Cluster A) showed significant differences between the two environments for both groups, including an increased outward foot rotation, step width, number of steps per 180 • turn, stance to swing ratio, and cycle time deviation in the real-world. A number of parameters (Cluster B) showed only significant differences between the two environments for elderly subjects, including a decreased gait velocity (p = 0.0072), decreased cadence (p = 0.0051) and increased cycle time (p = 0.0051) in real-world settings. Importantly, the real-world environment increased the differences in several parameters between the young and elderly groups. Conclusion: Elderly test subjects walked differently in controlled lab settings compared to their real-world environments, which indicates the need to better understand

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“…Kinetic and kinematics information obtained from this walking task would allow the participant's gait to be analysed. Clinically, gait analysis has been used to diagnose pathology and evaluate the effectiveness of intervention 43 . The participant was required to walk a total distance of 10 metres, which was mapped out with a demarcated start and finish line along the walkway.…”

Section: M Walkmentioning

confidence: 99%

An Asian-centric human movement database capturing activities of daily living

et al. 2020

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Assessment of human movement performance in activities of daily living (ADL) is a key component in clinical and rehabilitation settings. Motion capture technology is an effective method for objective assessment of human movement. Existing databases capture human movement and ADL performance primarily in the Western population, and there are no Asian databases to date. This is despite the fact that Asian anthropometrics influence movement kinematics and kinetics. This paper details the protocol in the first phase of the largest Asian normative human movement database. Data collection has commenced, and this paper reports 10 healthy participants. Twelve tasks were performed and data was collected using Qualisys motion capture system, force plates and instrumented table and chair. In phase two, human movement of individuals with stroke and knee osteoarthritis will be captured. This can have great potential for benchmarking with the normative human movement captured in phase one and predicting recovery and progression of movement for patients. With individualised progression, it will offer the development of personalised therapy protocols in rehabilitation.

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Biomechanical parameters for gait analysis: a systematic review of healthy human gait

Cited by 77 publications

References 28 publications

Effects of walking speed on gait biomechanics in healthy participants: a systematic review and meta-analysis

Effects of walking speed on gait biomechanics in healthy participants: a systematic review and meta-analysis

Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments

An Asian-centric human movement database capturing activities of daily living

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