The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review

Benson, Lauren C.; Clermont, Christian A.; Bošnjak, Eva; Ferber, Reed

doi:10.1016/j.gaitpost.2018.04.047

Cited by 182 publications

(139 citation statements)

References 77 publications

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“…Lastly, the data for the present study were collected in a laboratory setting while running on a treadmill. Previous studies have suggested that a laboratory‐based setting limits our ability to study the multifactorial nature of RRIs. Therefore, future studies should utilize inertial measurement units (IMUs) to quantify running gait patterns in real‐world environments and determine whether these homogenous subgroups exist.…”

Section: Discussionmentioning

confidence: 99%

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Jauhiainen

Pohl

Äyrämö

et al. 2020

Scandinavian Med Sci Sports

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Previous studies have suggested that runners can be subgrouped based on homogeneous gait patterns; however, no previous study has assessed the presence of such subgroups in a population of individuals across a wide variety of injuries. Therefore, the purpose of this study was to assess whether distinct subgroups with homogeneous running patterns can be identified among a large group of injured and healthy runners and whether identified subgroups are associated with specific injury location. Three‐dimensional kinematic data from 291 injured and healthy runners, representing both sexes and a wide range of ages (10‐66 years), were clustered using hierarchical cluster analysis. Cluster analysis revealed five distinct subgroups from the data. Kinematic differences between the subgroups were compared using one‐way analysis of variance (ANOVA). Against our hypothesis, runners with the same injury types did not cluster together, but the distribution of different injuries within subgroups was similar across the entire sample. These results suggest that homogeneous gait patterns exist independent of injury location and that it is important to consider these underlying patterns when planning injury prevention or rehabilitation strategies.

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Section: Discussionmentioning

confidence: 99%

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Jauhiainen

Pohl

Äyrämö

et al. 2020

Scandinavian Med Sci Sports

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“…Evaluating kinematical characteristics is crucial for a correct clinical understanding of complex functional movements such as gait [1], a forward lunge and other tasks requiring optimal motor control [2]. Studying kinematics can help in the assessment of the patients' functionality and progression in their rehabilitation period.…”

Section: Introductionmentioning

confidence: 99%

“…Previously conducted reviews highlighted the growing interest for inertial sensors in clinical practice [13]. Benson et al [1] reported the need for gait analysis over longer time periods, with larger number of participants, in natural environments. O'Reilly et al [14] pointed towards the use of machine learning techniques for lower limb exercise detection and classification with IMUs.…”

Section: Introductionmentioning

confidence: 99%

Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review

Weygers

Kok

Konings

et al. 2020

Sensors

103

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The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be applicable in a clinical setting, and to suggest future research directions. Studies within the PubMed, Web Of Science and EMBASE databases were screened for eligibility, based on the following inclusion criteria: (1) studies must include a methodological description of how kinematic variables were obtained for the lower limb, (2) kinematic data must have been acquired by means of IMUs, (3) studies must have validated the implemented method against a golden standard reference system. Information on study characteristics, signal processing characteristics and study results was assessed and discussed. This review shows that methods for lower limb joint kinematics are inherently application dependent. Sensor restrictions are generally compensated with biomechanically inspired assumptions and prior information. Awareness of the possible adaptations in the IMU-based kinematic estimates by incorporating such prior information and assumptions is necessary, before drawing clinical decisions. Future research should focus on alternative validation methods, subject-specific IMU-based biomechanical joint models and disturbed movement patterns in real-world settings.

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“…currently known as the golden standard in biomechanical analysis [3]. Unlike artificial laboratory situations, inertial sensor-based methods can provide a way to measure kinematics in comfortable outdoor settings, omitting restrictions in physical space [4]. In this work we propose a novel tightly coupled sensor fusion algorithm for joint kinematic estimation (e.g.…”

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confidence: 99%

Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

et al. 2020

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The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is known to drift over time, resulting in drifting joint kinematics. This study proposes a novel joint kinematic estimation method that tightly incorporates the connection between adjacent segments within a sensor fusion algorithm, to obtain drift-free joint kinematics. Drift in the joint kinematics is eliminated solely by utilizing common information in the accelerometer and gyroscope measurements of sensors placed on connecting segments. Both an optimization-based smoothing and a filtering approach were implemented. Validity was assessed on a robotic manipulator under varying measurement durations and movement excitations. Standard deviations of the estimated relative sensor orientations were below 0.89 • in an optimization-based smoothing implementation for all robot trials. The filtering implementation yielded similar results after convergence. The method is proven to be applicable in biomechanics, with a prolonged gait trial of 7 minutes on 11 healthy subjects. Three-dimensional knee joint angles were estimated, with mean RMS errors of 2.14 • , 1.85 • , 3.66 • in an optimization-based smoothing implementation and mean RMS errors of 3.08 • , 2.42 • , 4.47 • in a filtering implementation, with respect to a golden standard optical motion capture reference system. Tommy Verbeerst received the M.Sc. degree in electrical engineering from KHBO, Ostend, Belgium, in 2008. Since 2013, he has been working with KU Leuven and UC Vives. He is affiliated with the Department of Electrical Engineering (ESAT), KU Leuven Campus Bruges, Belgium. His current research interest includes the fields of engineering education, robotics, and machine-vision. Mark Versteyhe received the M.Sc. degree in mechanical engineering and the Ph.D. degree in applied sciences, from KU Leuven in 1995 and 2000, respetively.He has worked 16 years in industry in various functions linked to research and innovation. Since October 2016, he has been a Professor with KU Leuven's Faculty of Engineering Technology, Technology Campus Brugge, where he co-ordinates the research effort on connected mechatronics. His research focus lies in studying and applying the holistic paradigm of mechatronic system design. His special interest goes to "Dependability" which encompasses reliabilityavailability-robustness and security of a system and "Distributed Systems" which are treated as a complex ecosystem of machines and humans that are connected within the Industry 4.0 paradigm shift.Kurt Claeys received the M.Sc. degree in musculoskeletal rehabilitation sciences and physiotherapy from the University of Ghent, Belgium, in 1993, and the Ph.D. degree in orthopedic manual therapist from the IRSK-WINGS institute Ieper, Belgium, in 2005, and the Ph.D. degree from KU Leuven, Belgium,...

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The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review

Cited by 182 publications

References 77 publications

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review

Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

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