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IEEE Reviews in Biomedical Engineering 2018Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities... (Review)
Review
Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.
Topics: Accelerometry; Gait; Gait Analysis; Humans; Monitoring, Ambulatory; Torso
PubMed: 29994786
DOI: 10.1109/RBME.2018.2807182 -
Sensors (Basel, Switzerland) Apr 2021Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have... (Review)
Review
Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.
Topics: Activities of Daily Living; Biomechanical Phenomena; Gait; Gait Analysis; Humans; Wearable Electronic Devices
PubMed: 33924403
DOI: 10.3390/s21082727 -
Systematic Reviews Jun 2019Understanding the effects of gait speed on biomechanical variables is fundamental for a proper evaluation of alterations in gait, since pathological individuals tend to... (Meta-Analysis)
Meta-Analysis
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 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.
Topics: Adolescent; Adult; Age Factors; Aged; Aged, 80 and over; Biomechanical Phenomena; Child; Child, Preschool; Gait; Gait Analysis; Humans; Middle Aged; Walking; Walking Speed; Young Adult
PubMed: 31248456
DOI: 10.1186/s13643-019-1063-z -
Revista Da Escola de Enfermagem Da U S P Dec 2018To identify the outcomes of studies on gait speed and its use as a marker of physical frailty in community elderly.
OBJECTIVE
To identify the outcomes of studies on gait speed and its use as a marker of physical frailty in community elderly.
METHOD
Systematic review of the literature performed in the following databases: LILACS, SciELO, MEDLINE/PubMed, ScienceDirect, Scopus and ProQuest. The studies were evaluated by STROBE statement, and the PRISMA recommendations were adopted.
RESULTS
There were 6,303 studies, and 49 of them met the inclusion criteria. Of the total number of studies, 91.8% described the way of measuring gait speed. Of these, 28.6% used the distance of 4.6 meters, and 34.7% adopted values below 20% as cutoff points for reduced gait speed, procedures in accordance with the frailty phenotype. Regarding the outcomes, in 30.6% of studies, there was an association between gait speed and variables of disability, frailty, sedentary lifestyle, falls, muscular weakness, diseases, body fat, cognitive impairment, mortality, stress, lower life satisfaction, lower quality of life, napping duration, and poor performance in quantitative parameters of gait in community elderly.
CONCLUSION
The results reinforce the association between gait speed, physical frailty and health indicator variables in community elderly.
Topics: Aged; Disability Evaluation; Frail Elderly; Frailty; Gait; Geriatric Assessment; Humans; Quality of Life; Walking Speed
PubMed: 30570081
DOI: 10.1590/S1980-220X2017028703392 -
Gait & Posture Feb 2018Cerebellar Ataxias are a group of gait disorders resulting from dysfunction of the cerebellum, commonly characterised by slowly progressing incoordination that manifests... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Cerebellar Ataxias are a group of gait disorders resulting from dysfunction of the cerebellum, commonly characterised by slowly progressing incoordination that manifests as problems with balance and walking leading to considerable disability. There is increasing acceptance of gait analysis techniques to quantify subtle gait characteristics that are unmeasurable by current clinical methods This systematic review aims to identify the gait characteristics able to differentiate between Cerebellar Ataxia and healthy controls.
METHODS
Following systematic search and critical appraisal of the literature, gait data relating to preferred paced walking in Cerebellar Ataxia was extracted from 21 studies. A random-effect model meta-analysis was performed for 14 spatiotemporal parameters. Quality assessment was completed to detect risk of bias.
RESULTS
There is strong evidence that compared with healthy controls, Cerebellar Ataxia patients walk with a reduced walking speed and cadence, reduced step length, stride length, and swing phase, increased walking base width, stride time, step time, stance phase and double limb support phase with increased variability of step length, stride length, and stride time.
CONCLUSION
The consensus description provided here, clarifies the gait pattern associated with ataxic gait disturbance in a large cohort of participants. High quality research and reporting is needed to explore specific genetic diagnoses and identify biomarkers for disease progression in order to develop well-evidenced clinical guidelines and interventions for Cerebellar Ataxia.
Topics: Gait; Gait Ataxia; Gait Disorders, Neurologic; Humans; Walking; Walking Speed
PubMed: 29220753
DOI: 10.1016/j.gaitpost.2017.11.024 -
Sensors (Basel, Switzerland) Jun 2020The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid... (Review)
Review
The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid diagnosis and symptom monitoring in Parkinson's disease (PD) patients. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. We selected studies that have either used technologies to distinguish PD patients from healthy subjects or stratified PD patients according to motor status or disease stages. Only those studies that reported at least 80% sensitivity and specificity were included. Gait analysis algorithms used for diagnosis showed a balanced accuracy range of 83.5-100%, sensitivity of 83.3-100% and specificity of 82-100%. For motor status discrimination the gait analysis algorithms showed a balanced accuracy range of 90.8-100%, sensitivity of 92.5-100% and specificity of 88-100%. Despite a large number of studies on the topic of objective gait analysis in PD, only a limited number of studies reported algorithms that were accurate enough deemed to be useful for diagnosis and symptoms monitoring. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies.
Topics: Algorithms; Gait; Gait Analysis; Humans; Parkinson Disease; Sensitivity and Specificity
PubMed: 32580330
DOI: 10.3390/s20123529 -
PloS One 2019Human gait has been shown to be an effective biometric measure for person identification at a distance. On the other hand, changes in the view angle pose a major...
Human gait has been shown to be an effective biometric measure for person identification at a distance. On the other hand, changes in the view angle pose a major challenge for gait recognition as human gait silhouettes are usually different from different view angles. Traditionally, such a multi-view gait recognition problem can be tackled by View Transformation Model (VTM) which transforms gait features from multiple gallery views to the probe view so as to evaluate the gait similarity. In the real-world environment, however, gait sequences may be captured from an uncontrolled scene and the view angle is often unknown, dynamically changing, or does not belong to any predefined views (thus VTM becomes inapplicable). To address this free-view gait recognition problem, we propose an innovative view-adaptive mapping (VAM) approach. The VAM employs a novel walking trajectory fitting (WTF) to estimate the view angles of a gait sequence, and a joint gait manifold (JGM) to find the optimal manifold between the probe data and relevant gallery data for gait similarity evaluation. Additionally, a RankSVM-based algorithm is developed to supplement the gallery data for subjects whose gallery features are only available in predefined views. Extensive experiments on both indoor and outdoor datasets demonstrate that the VAM outperforms several reference methods remarkably in free-view gait recognition.
Topics: Algorithms; Biometry; Facial Recognition; Gait; Gait Analysis; Humans; Pattern Recognition, Automated; Walking
PubMed: 30990804
DOI: 10.1371/journal.pone.0214389 -
Aging Jun 2023
Topics: Walking Speed; Interleukin-6; Exercise; Gait
PubMed: 37286191
DOI: 10.18632/aging.204797 -
The Israel Medical Association Journal... Nov 2021
Topics: Disability Evaluation; Gait; Gait Analysis; Humans; Malingering; Walking
PubMed: 34811997
DOI: No ID Found -
Scientific Data Oct 2022Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user's progression throughout time. This work...
Monitoring gait and posture while using assisting robotic devices is relevant to attain effective assistance and assess the user's progression throughout time. This work presents a multi-camera, multimodal, and detailed dataset involving 14 healthy participants walking with a wheeled robotic walker equipped with a pair of affordable cameras. Depth data were acquired at 30 fps and synchronized with inertial data from Xsens MTw Awinda sensors and kinematic data from the segments of the Xsens biomechanical model, acquired at 60 Hz. Participants walked with the robotic walker at 3 different gait speeds, across 3 different walking scenarios/paths at 3 different locations. In total, this dataset provides approximately 92 minutes of total recording time, which corresponds to nearly 166.000 samples of synchronized data. This dataset may contribute to the scientific research by allowing the development and evaluation of: (i) vision-based pose estimation algorithms, exploring classic or deep learning approaches; (ii) human detection and tracking algorithms; (iii) movement forecasting; and (iv) biomechanical analysis of gait/posture when using a rehabilitation device.
Topics: Gait; Gait Analysis; Humans; Posture; Walkers; Walking
PubMed: 36202855
DOI: 10.1038/s41597-022-01722-7