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Journal ArticleDOI

Gait analysis: systems, technologies, and importance

07 Nov 2016-Journal of Mechanics in Medicine and Biology (World Scientific Publishing Company)-Vol. 16, Iss: 07, pp 1630003
TL;DR: Significance of gait analysis in robotic research is also illustrated in this part where the study focuses on robot assisted systems and its possible applicability in clinical rehabilitation and sports training.
Abstract: Human gait is the identity of a person's style and quality of life. Reliable cognition of gait properties over time, continuous monitoring, accuracy of evaluation, and proper analysis of human gait characteristics have demonstrated their importance not only in clinical and medical studies, but also in the field of sports, rehabilitation, training, and robotics research. Focusing on walking gait, this study presents an overview on gait mechanisms, common technologies used in gait analysis, and importance of this particular field of research. Firstly, available technologies that involved in gait analysis are briefly introduced in this paper by concentrating on the usability and limitations of the systems. Secondly, key gait parameters and motion characteristics are elucidated from four angles of views; one: gait phases and gait properties; two: center of mass and center of pressure (CoM-CoP) tracking profile; three: Ground Reaction Force (GRF) and impact, and four: muscle activation. Thirdly, the study focuses on the clinical observations of gait patterns in diagnosing gait abnormalities of impaired patients. The presentation also shows the importance of gait analysis in sports to improve performance as well as to avoid risk of injuries of sports personnel. Significance of gait analysis in robotic research is also illustrated in this part where the study focuses on robot assisted systems and its possible applicability in clinical rehabilitation and sports training.
Citations
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Journal ArticleDOI
TL;DR: This review discusses various wearable tTENG devices that have been adapted for constant cardiovascular and respiratory monitoring, pertinent to those suffering from diseases in these organ systems, and discusses the future research directions for the field, in particular regarding personalized healthcare propelled by tTengs.
Abstract: Biomonitoring has played an increasingly important role in improving the quality of healthcare in recent years, but limitations in power supply and wearability, as well as the rise of the Internet of Things (IoT) have called for the development of a new type of device to provide biomonitoring on a daily basis. While the introduction of triboelectric nanogenerators (TENGs) has begun to solve these issues by providing sustainably powered biomonitoring, textile-based TENGs (tTENGs) take a more pervasive approach by integrating this technology into commonly worn textiles. tTENGs are particularly unique as they offer an inexpensive alternative for biomonitoring; the high breathability, comfort, and scalability inherent to tTENGs' woven structures have made them increasingly convenient for human application. This review begins by highlighting novel material configurations of tTENGs and their advantages for biomonitoring. We then discuss various wearable tTENG devices that have been adapted for constant cardiovascular and respiratory monitoring, pertinent to those suffering from diseases in these organ systems. Transitioning into the biomechanical aspect of the human body, we explore tTENG configurations integrated for upper body and gait motion sensing. At the same time, with many people suffering from sleep disorders, we examine tTENGs that monitor the quality of sleep of an individual. Lastly, on a more molecular level, we examine the application of tTENGs for monitoring biochemical fluctuations, such as sweat. Finally, we discuss the future research directions for the field, in particular regarding personalized healthcare propelled by tTENGs.

41 citations

Journal ArticleDOI
16 Oct 2018-Sensors
TL;DR: The method, as presented here, provides a unique solution to the problem of identifying the optimal covariance matrices values for Kalman filtering, and should be improved in order to reduce the duration of the whole process.
Abstract: Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by means of a Kalman filter that fuses acceleration, angular velocity, and magnetic field measures. A critical point when implementing a Kalman filter is the initialization of the covariance matrices that characterize mismodelling and input error from noisy sensors. The present study proposes a methodology to identify the initial values of these covariance matrices that optimize orientation estimation in the context of human motion analysis. The approach used was to apply motion to the sensor manually, and to compare the orientation obtained via the Kalman filter to a measurement from an optoelectronic system acting as a reference. Testing different sets of values for each parameter of the covariance matrices, and comparing each MIMU measurement with the reference measurement, enabled identification of the most effective values. Moreover, with these optimized initial covariance matrices, the orientation estimation was greatly improved. The method, as presented here, provides a unique solution to the problem of identifying the optimal covariance matrices values for Kalman filtering. However, the methodology should be improved in order to reduce the duration of the whole process.

16 citations


Cites methods from "Gait analysis: systems, technologie..."

  • ...To perform this analysis, optoelectronic systems that measure the 3D coordinates of reflective markers remain the “gold standard” [1,2]....

    [...]

Journal ArticleDOI
TL;DR: An in-depth investigation of efforts directed towards vision-based, sensor-based and hybrid KOA identification and an up-to-date review of machine learning techniques for classification of KOA and healthy subjects are provided.
Abstract: In today’s era of new advancements, diagnosing a pathology at an early stage has given rise to the development of automated diagnostic systems. Knee Osteoarthritis (KOA) being among one of the most painful joint disorders is the root cause for disability, particularly in elderly population. Gait based recognition of KOA is a prominent area that requires deliberations from the end of researchers, academicians and scientists to develop more automated systems that not only offer reliability and accuracy but are also affordable for common man. This article aims to provide an in-depth investigation of efforts directed towards vision-based, sensor-based and hybrid KOA identification. The study is based on the historical data gathered and background obtained viz-a-viz clinical gait analysis. An extensive survey of KOA gait acquisition modalities and feature representation approaches for the purpose of critically examining them are also presented. The study surveys the statistical metrics used for evaluating KOA, considering relevant articles. Based on the survey, this article aims to provide an up-to-date review of machine learning techniques for classification of KOA and healthy subjects. Furthermore, this article also identifies open research challenges existing in the literature that could be explored further for providing more effective KOA analysis. Finally, this article presents the future perspectives and provides an outline of the proposed work for efficient KOA diagnosis based on vision-based gait.

12 citations

Proceedings ArticleDOI
01 Jul 2019
TL;DR: This paper proposes an automatic and privacy-considered way to analyze gait using a non-contact sensor, a stereo camera that applies a cutting-edge deep learning technology to detect a human subject in 2D images and then combining 3D sensing data to measure gait features, such as step length and walking speed.
Abstract: Gait is one of the important features to assess physical or mental conditions of the elderly which is directly related to their health status. The changes or abnormalities in gait may reflect health risks. Screening tests like TUG (Timed Up and Go) and POMA (Performance Oriented Mobility Assessment) include gait assessment. However, analyzing gait is a complex process usually handled by professionals at clinical facilities. With the development of IoT (Internet of Things) sensors and computer vision technologies, more automatic and accurate approaches are demanded to measure gait at nursing facilities or at-home in the daily environment. In this paper, we propose an automatic and privacy-considered way to analyze gait using a non-contact sensor, a stereo camera. This approach applies a cutting-edge deep learning technology to detect a human subject in 2D images and then combining 3D sensing data to measure gait features, such as step length and walking speed. Compared to Kinect or a single 2D camera, our approach is not only accurate for various walking patterns but also robust to camera setting environment. Experiments at a daycare facility in Tianjin, China showed that our approach is suitable for assessing TUG or POMA tests in the daily environment.

12 citations


Cites background from "Gait analysis: systems, technologie..."

  • ...For example, weak or abnormal gait features may be caused by physical problems, such as back pain, joint pain or muscle strain [6]....

    [...]

Journal ArticleDOI
01 Jun 2022-Sensors
TL;DR: This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community.
Abstract: Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

10 citations

References
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Journal ArticleDOI
TL;DR: The relatively small number of body surface markers used in the VICON system render it easy to implement for use in routine clinical gait evaluations and should be a useful reference for describing and comparing pathologic gait patterns.

2,953 citations

Journal ArticleDOI
TL;DR: The inverted pendulum model permitted us to understand the separate roles of the two mechanisms during these critical unbalancing and rebalancing periods and confirmed the critical importance of the hip abductors/adductors in balance during all phases of standing and walking.

2,940 citations

Journal ArticleDOI
TL;DR: The gait analysis laboratory provides quantified assessments of human locomotion which assist in the orthopaedic management of various pediatric gait pathologies by utilizing a video-based data collection strategy similar to commercially available systems for motion data collection.

2,684 citations

Journal ArticleDOI
TL;DR: A simple and inexpensive photographic method has been developed whereby many kinematic components of the walking act in the sagittal, frontal, and transverse planes can be measured and related temporally.
Abstract: A simple and inexpensive photographic method has been developed whereby many kinematic components of the walking act in the sagittal, frontal, and transverse planes can be measured and related temporally. A factorial design was used to study the displacement patterns of sixty normal men who ranged i

1,152 citations

Journal Article
TL;DR: With the DGO the legs of patients with different degrees of paresis and spasticity could be trained for more than half an hour, and physiological gait patterns were obtained.
Abstract: Recent studies have confirmed that regular treadmill training can improve walking capabilities in incomplete spinal cord-injured subjects. At the beginning of this training the leg movements of the patients have to be assisted by physiotherapists during gait on the moving treadmill. The physical capabilities and the individual experience of the therapists usually limit this training. A driven gait orthosis (DGO) has been developed that can move the legs of a patient in a physiological way on the moving treadmill. The orthosis is adjustable in size so different patients can use it. Actuators at the knee and hip joints are controlled by a position controller. With the DGO the legs of patients with different degrees of paresis and spasticity could be trained for more than half an hour, and physiological gait patterns were obtained.

1,100 citations