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Methodological factors affecting joint moments estimation in clinical gait analysis: a systematic review.

ABSTRACT: Quantitative gait analysis can provide a description of joint kinematics and dynamics, and it is recognized as a clinically useful tool for functional assessment, diagnosis and intervention planning. Clinically interpretable parameters are estimated from quantitative measures (i.e. ground reaction forces, skin marker trajectories, etc.) through biomechanical modelling. In particular, the estimation of joint moments during motion is grounded on several modelling assumptions: (1) body segmental and joint kinematics is derived from the trajectories of markers and by modelling the human body as a kinematic chain; (2) joint resultant (net) loads are, usually, derived from force plate measurements through a model of segmental dynamics. Therefore, both measurement errors and modelling assumptions can affect the results, to an extent that also depends on the characteristics of the motor task analysed (i.e. gait speed). Errors affecting the trajectories of joint centres, the orientation of joint functional axes, the joint angular velocities, the accuracy of inertial parameters and force measurements (concurring to the definition of the dynamic model), can weigh differently in the estimation of clinically interpretable joint moments. Numerous studies addressed all these methodological aspects separately, but a critical analysis of how these aspects may affect the clinical interpretation of joint dynamics is still missing. This article aims at filling this gap through a systematic review of the literature, conducted on Web of Science, Scopus and PubMed. The final objective is hence to provide clear take-home messages to guide laboratories in the estimation of joint moments for the clinical practice.

SUBMITTER: Camomilla V

PROVIDER: S-EPMC5563001 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Project description:BackgroundGait disorders and as a consequence, robotic rehabilitation techniques are becoming increasingly prevalent as the population ages. In the area of rehabilitation robotics, using lightweight single hip joint exoskeletons are of significance. Considering no prior systematic review article on clinical outcomes, we aim to systematically review powered hip exoskeletons in terms of gait parameters and metabolic expenditure effects.MethodsThree databases of PubMed, Scopus, and Web of science were searched for clinical articles comparing outcomes of gait rehabilitation using hip motorized exoskeleton with conventional methods, on patients with gait disorder or healthy individuals. Of total number of 37 reviewed articles, 14 trials were quantitatively analyzed. Analyses performed in terms of gait spatiotemporal parameters like speed (self-speed and maximum speed), step length, stride length, cadence, and oxygen consumption.ResultsImproved clinical outcomes of gait spatiotemporal parameters with hip joint exoskeletons are what our review's findings show. In terms of gait values, meta-analysis indicates that rehabilitation with single hip joint exoskeleton enhanced parameters of maximum speed by 0.13 m/s (0.10-0.17) and step length by 0.06 m (0.05-0.07). For the remaining investigated gait parameters, no statistically significant difference was observed. Regarding metabolic parameters, oxygen consumption was lower in individuals treated with hip exoskeleton (- 1.23 ml/min/kg; range - 2.13 to - 0.32).ConclusionAlthough the analysis demonstrated improvement with just specific gait measures utilizing powered hip exoskeletons, the lack of improvement in all parameters is likely caused by the high patient condition heterogeneity among the evaluated articles. We also noted in patients who rehabilitated with the hip exoskeleton, the oxygen cost was lower. More randomized controlled trials are needed to verify both the short- and long-term clinical outcomes, including patient-reported measures.Level of evidenceLevel I (systematic review and meta-analysis).

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Methodological factors affecting joint moments estimation in clinical gait analysis: a systematic review.

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