Relevant bibliographies by topics / Clinical Gait Analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Clinical Gait Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Clinical Gait Analysis"

1

Davis, R. B. "Clinical gait analysis." IEEE Engineering in Medicine and Biology Magazine 7, no. 3 (September 1988): 35–40. http://dx.doi.org/10.1109/51.7933.

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Davis, Roy B. "Reflections on clinical gait analysis." Journal of Electromyography and Kinesiology 7, no. 4 (December 1997): 251–57. http://dx.doi.org/10.1016/s1050-6411(97)00008-4.

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Ellis, Malcolm, and Adrian Howe. "A Clinical Gait Analysis System." Engineering in Medicine 16, no. 4 (October 1987): 217–20. http://dx.doi.org/10.1243/emed_jour_1987_016_049_02.

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A gait analysis system has been devised that is not only relatively inexpensive, but is also quick to use, requires no expertise to run, and does not need any special laboratory facilities. The system monitors the subject's knee and hip movements during ambulation using electro-goniometers. Foot contact data are obtained using lightweight, flexible, foot switches. The data are sent to a computer via an eight channel telemetry system carried by the subject on a waist belt. The software is designed to simplify analysis and be ‘user friendly’. After a simple calibration routine, the system prompts the operator to ask the subject to walk a number of steps. The computer ignores the initial steps taken as these are not typical of normal gait. It then collects data from the consequent steps, averages the data and then displays them in both graphical and numerical form. A patient can be tested and a printout provided for insertion in the patient's notes within ten minutes. Only one hours training is required to learn to operate the system. A patient can be tested in a physiotherapy department, corridor, or in any area where a few consecutive steps can be taken.
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Andriacchi, T. P. "Clinical applications of gait analysis." Journal of Biomechanics 26, no. 3 (March 1993): 324. http://dx.doi.org/10.1016/0021-9290(93)90484-v.

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Olney, Sandra J. "Clinical information in gait analysis." Journal of Biomechanics 26, no. 3 (March 1993): 325. http://dx.doi.org/10.1016/0021-9290(93)90486-x.

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Baumann, J. U. "Requirements of clinical gait analysis." Human Movement Science 10, no. 5 (October 1991): 535–42. http://dx.doi.org/10.1016/0167-9457(91)90042-v.

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Whittle, Michael W. "Clinical gait analysis: A review." Human Movement Science 15, no. 3 (June 1996): 369–87. http://dx.doi.org/10.1016/0167-9457(96)00006-1.

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Watelain, Éric. "Human gait: From clinical gait analysis to diagnosis assistance." Movement & Sport Sciences 98, no. 4 (2017): 3. http://dx.doi.org/10.3917/sm.098.0003.

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Sutherland, D. H. "The evolution of clinical gait analysis." Gait & Posture 16, no. 2 (October 2002): 159–79. http://dx.doi.org/10.1016/s0966-6362(02)00004-8.

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Walsh, M. "Gait Analysis – A Paediatric Clinical Perspective." Physiotherapy Practice and Research 32, no. 1 (2011): 24–27. http://dx.doi.org/10.3233/ppr-2011-32105.

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Dissertations / Theses on the topic "Clinical Gait Analysis"

1

Trinler, Ursula Kathinka. "Muscle force estimation in clinical gait analysis." Thesis, University of Salford, 2016. http://usir.salford.ac.uk/39257/.

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Neuro-musculoskeletal impairments are a substantial burden on our health care system as a consequence of disease, injury or aging. A better understanding of how such impairments influence the skeletal system through muscle force production is needed. Clinical gait analysis lacks in a sufficient estimation of individual muscle forces. To date, joint moments and EMG measurements are used to deduce on the characteristics of muscle forces, however, known limitations restrain a satisfying analysis of muscle force production. Recent developed musculoskeletal models make it possible to estimate individual muscle forces using experimental kinematic and kinetic data as input, however, are not yet implemented into a clinical gait analysis due to a wide range of different methods and models and a lack of standardised protocols which could be easily applied by clinicians in a routine processing. This PhD thesis assessed the state of the art of mathematical modelling which enables the estimation of muscle force production during walking. This led into devising a standardised protocol which could be used to incorporate muscle force estimation into routine clinical practice. Especially the input of clinical science knowledge led to an improvement of the protocol. Static optimisation and computed muscle control, two mathematical models to estimate muscle forces, have been found to be the most suitable models for clinical purposes. OpenSim, a free available simulation tool, has been chosen as its musculoskeletal models have been already frequently used and tested. Furthermore, OpenSim provides a straight forward pipeline called SimTrack including both mathematical models. Minor and major adjustments were needed to adapt the standard pipeline for the purposes of a clinical gait analysis to be able to create a standardised protocol for gait analyses. The developed protocol was tested on ten healthy participants walking at five different walking speeds and captured by a standard motion capture system. Muscle forces were estimated and compared to surface EMG measurements regarding activation and shape as well as their dependence on walking speed. The results showed a general agreement between static optimisation, computed muscle control and the EMG excitations. Compared to the literature, these results show a good consistency between the modelling methods and surface EMG. However, some differences were shown between mathematical models and between models and EMG, especially fast walking speeds. Additionally, high estimated activation peaks and uncertainties within the estimation process point out that more research needs to be undertaken to understand the mechanisms of mathematical models and the influence of different modelling parameters better (e.g. characteristics of muscle-tendon units, uncertainties of dynamic inconsistency). In conclusion, muscle force estimation with mathematical models is not yet robust enough to be able to include the protocol into a clinical gait analysis routine. It is, however, on a good way, especially slow walking speeds showed reasonable good results. Understanding the limitations and influencing factors of these models, however, may make this possible. Further steps may be the inclusion of patients to see the influence of health conditions.
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Tan, Junjay. "Advancing clinical gait analysis through technology and policy." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53326.

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Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 161-165).
Quantitatively analyzing human gait biomechanics will improve our ability to diagnose and treat disability and to measure the effectiveness of assistive devices. Gait analysis is one technology used to analyze walking, but technical as well as economic, social, and policy issues hinder its clinical adoption. This thesis is divided into two parts that address some of these issues. Part I focuses on the role public policies have in advancing gait analysis. Through an analysis of gait analysis technologies, case studies of MRI and CT Angiography, and a high-level analysis of data standards used in gait analysis, it concludes that policies cannot directly create the institutional structures and the data standards required to advance gait analysis as a clinical diagnostic tool. Only through indirect means, such as research funding, can policies support the development of organizations to take ownership of gait analysis technologies. Part I also concludes that policies should not fund development of gait technologies but instead should fund research units working on data standards and accurate human body models. Part II focuses on a technical issue in gait analysis, namely, how to address uncertainties in joint moment calculations that occur from using different body segment inertial parameter estimation models. This is identified as a technical issue needing attention from our broader policy analysis in Part I. Using sensitivity studies of forward dynamics computer simulations coupled with an analysis of the dynamical equations of motion, Part II shows that joint moment variations resulting from different segment inertial parameters are significant at some parts of the gait cycle, particularly heel strike and leg swing.
(cont.) It provides recommendations about which segment inertial parameters one should estimate more accurately depending on which joints and phases of the gait cycle one is interested in analyzing.
by Junjay Tan.
S.M.
S.M.in Technology and Policy
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Jenkins, Sian M. "Lower limb modelling for children in clinical gait analysis." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400179.

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Barton, Gabor J. "Visualisation of clinical gait analysis data using neural networks." Thesis, Liverpool John Moores University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436553.

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Arendse, Regan Emile. "The application of clinical gait analysis to running injuries." Doctoral thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/8643.

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Clinical evaluation of the injured runner requires identification and correction of the multiple factors commonly implicated in running injury. Effective management of running injuries requires that the concerned health professional identify all implicated factors. In this regard it is essential that the relationship between running style and injury he determined, because if there is an association between the gait analysis variables descriptive of running style and injury, these would be important in the management of the injured runner. The Gait Analysis Laboratory at the Sports Science institute of South Africa and the University of Cape Town with its three-dimensional Vicon 370 motion analysis system and Advanced Medical Technology industry® strain gauge force platform is appropriate for collecting data to study running style. These data include the movement patterns and estimated mechanical power and work required to effect the observed movement of the ankle and knee. The forces applied by the supporting surfaces on the runners were collected. The data captured with the Workstation® programme (Oxford Metric, Oxford, England), was processed with GaitLab® (Kiboho Publishers, Cape Town, South Africa), collated in Excel ® (Microsoft Corporation, Redmond, USA) and statistically analysed (StatSoft, Inc. (2000). STATISTICA for Windows [Computer program manual]. Tulsa, OK, USA). In this thesis a series of studies are presented with the aim of determining the relevance of running style to the assessment of the injured runner and the conventional treatment methods used to treat common running injuries.
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Cloete, Teunis. "Benchmarking full-body inertial motion capture for clinical gait analysis." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/2922.

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MScEng
Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2009.
Clinical gait analysis has been proven to greatly improve treatment planning and monitoring of patients suffering from neuromuscular disorders. Despite this fact, it was found that gait analysis is still largely underutilised in general patient-care due to limitations of gait measurement equipment. Inertial motion capture (IMC) is able to overcome many of these limitations, but this technology is relatively untested and is therefore viewed as adolescent. This study addresses this problem by evaluating the validity and repeatability of gait parameters measured with a commercially available, full-body IMC system by comparing the results to those obtained with alternative methods of motion capture. The IMC system’s results were compared to a trusted optical motion capture (OMC) system’s results to evaluate validity. The results show that the measurements for the hip and knee obtained with IMC compares well with those obtained using OMC – with coefficient-of-correlation (R) values as high as 0.99. Some discrepancies were identified in the ankle-joint validity results. These were attributed to differences between the two systems with regard to the definition of ankle joint and to non-ideal IMC system foot-sensor design. The repeatability, using the IMC system, was quantified using the coefficient of variance (CV), the coefficient of multiple determination (CMD) and the coefficient of multiple correlation (CMC). Results show that IMC-recorded gait patterns have high repeatability for within-day tests (CMD: 0.786-0.984; CMC: 0.881-0.992) and between-day tests (CMD: 0.771-0.991; CMC: 0.872-0.995). These results compare well with those from similar studies done using OMC and electromagnetic motion capture (EMC), especially when comparing between-day results. Finally, to evaluate the measurements from the IMC system in a clinically useful application, a neural network was employed to distinguish between gait strides of stroke patients and those of able-bodied controls. The network proved to be very successful with a repeatable accuracy of 99.4% (1/166 misclassified). The study concluded that the full-body IMC system produces sufficiently valid and repeatable gait data to be used in clinical gait analysis, but that further refinement of the ankle-joint definition and improvements to the foot sensor are required.
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Bruening, Dustin A. "A kinetic multi-segment foot model with preliminary applications in clinical gait analysis." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 221 p, 2009. http://proquest.umi.com/pqdweb?did=1896923151&sid=3&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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NAGA, SOUMYA. "AN EFFICIENT ALGORITHM FOR CLINICAL MASS CENTER LOCATION OF HUMAN BODY." University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1131323551.

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Lindberg, Lena. "Usability in a clinical context: Redesigning the user interface of a gait analysis system." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-135946.

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Gait analysis can be described as a study of human walking patterns. This is very useful in health care, since gait analysis can reveal important information about a patient, and be an aid in diagnosis and rehabilitation. Today gait analysis is done either by qualitative visual observation of the patient, or in resource demanding and advanced laboratory settings. Many studies have been done in the search for new technical solutions that enables quantitative gait analysis outside of the laboratory. The goal of this thesis was to evaluate the usability of a new gait analysis system and to find out how the user interface could be better adapted to the end user’s needs and goals. This was done by defining and using suitable methods for learning about the users, evaluating the system and by defining usability in a clinical setting. A redesigned prototype was then developed and tested. It was found that the original user interface had many usability issues and was in need of better adaption to the intended user group. Through user research personas and key user needs could be determined that became the basis for the design work, along with guidelines from previous studies within the field. The redesigned prototype was tested on potential end users. It was in this study determined that semi-structured interviews are suitable for learning about the users. Usability evaluation should preferably be done using a combination of evaluation that involves real end users and evaluation by usability experts. Guidelines for usability in clinical systems could also be defined. Considering the redesigned prototype, the users saw a great potential, and could see themselves using it in the future. The testing determined that the redesigned prototype managed to solve many of the usability issues found in the original design.
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Akhlaghi, Florin. "An in-shoe biaxial shear force transducer utilising piezoelectric copolymer film and the clinical assessment of in-shoe forces." Thesis, University of Kent, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259679.

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Books on the topic "Clinical Gait Analysis"

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Clinical gait analysis: Theory and practice. Edinburgh: Elsevier, 2005.

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Bruckner, Jan. Gait workbook: A practical guide to clinical gait analysis. Thorofare, NJ: SLACK, Inc., 1998.

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Bruckner, Jan. The Gait workbook: A practical guide to clinical gait analysis. Thorofare, NJ: SLACK, 1998.

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Bruckner, Jan. The gait workbook: A practical guide to clinical gait analysis. Thorofare, NJ: SLACK Inc., 1998.

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Baker, Richard. Measuring walking: A handbook of clinical gait analysis. London: Mac Keith Press, 2013.

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Clinical Gait Analysis. Elsevier, 2006. http://dx.doi.org/10.1016/b978-0-443-10009-3.x5001-2.

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Kirtley, Christopher. Clinical Gait Analysis: Theory and Practice. Churchill Livingstone, 2006.

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(Editor), Sandro Giannini, F. Catani (Editor), M. G. Benedetti (Editor), and A. Leardini (Editor), eds. Gait Analysis, Methodologies and Clinical Applications. IOS Press, 1997.

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Sandro, Giannini, ed. Gait analysis: Methodologies and clinical applications. Amsterdam: IOS Press for BTS, Bioengineering Technology & Systems, 1994.

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Modern Methods for Affordable Clinical Gait Analysis. Elsevier, 2021. http://dx.doi.org/10.1016/c2020-0-02456-4.

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Book chapters on the topic "Clinical Gait Analysis"

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Lara, Sergio Lerma, Ana Ramírez Barragán, Ma Teresa Vara Arias, Álvaro-Pérez-Somarriba, and Estér Márquez Sánchez. "Gait Analysis Worshop." In Converging Clinical and Engineering Research on Neurorehabilitation, 1111–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34546-3_182.

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Gallow, Amanda, and Bryan Heiderscheit. "Clinical Aspects of Running Gait Analysis." In Endurance Sports Medicine, 201–13. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32982-6_15.

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Tecante, Karelia, Frank Seehaus, Bastian Welke, Gavin Olender, Michael Schwarze, Sean Lynch, and Christoph Hurschler. "Clinical Gait Analysis and Musculoskeletal Modeling." In 3D Multiscale Physiological Human, 165–87. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-6275-9_7.

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Lois, Juan Manuel Belda, María José Vivas Broseta, Silvia Mena del Horno, María-Luz Sánchez-Sánchez, Miguel Matas, and Enrique Viosca. "Functional Data Analysis for Gait Analysis after Stroke." In Converging Clinical and Engineering Research on Neurorehabilitation, 775–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34546-3_126.

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Hattori, Tomokazu, Shirou Hirose, Kazuhiko Sawai, and Shigeo Niwa. "—Overview— Clinical Gait Analysis in Hip Patients." In Hip Biomechanics, 105–14. Tokyo: Springer Japan, 1993. http://dx.doi.org/10.1007/978-4-431-68237-0_10.

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Komura, Takashi, Nobuhiro Tsumura, Seishi Sawamura, Masahiro Kurosaka, and Kosaku Mizuno. "Gait Analysis of Patients with Varus Osteoarthritic Knees." In Clinical Biomechanics and Related Research, 392–400. Tokyo: Springer Japan, 1994. http://dx.doi.org/10.1007/978-4-431-66859-6_35.

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Chin, Michael. "Clinical Use of Gait Analysis for the Athlete." In Athletic Footwear and Orthoses in Sports Medicine, 55–65. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52136-7_4.

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Hatanaka, Yasuhiko, Shinro Takai, Hitoshi Hase, Shuichi Kubo, and Yasasuke Hirasawa. "Kinematic Gait Analysis of Patients After Total Knee Arthroplasty." In Clinical Biomechanics and Related Research, 401–11. Tokyo: Springer Japan, 1994. http://dx.doi.org/10.1007/978-4-431-66859-6_36.

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Petrarca, M. "The Development of Gait Analysis in Developmental Age." In Converging Clinical and Engineering Research on Neurorehabilitation III, 606–9. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01845-0_121.

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Şen Köktaş, Nigar, and Robert P. W. Duin. "Statistical Analysis of Gait Data to Assist Clinical Decision Making." In Medical Content-Based Retrieval for Clinical Decision Support, 61–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11769-5_6.

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Conference papers on the topic "Clinical Gait Analysis"

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Prajapati, Nikita, Amandeep Kaur, and Dimple Sethi. "A Review on Clinical Gait Analysis." In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2021. http://dx.doi.org/10.1109/icoei51242.2021.9452951.

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Urban, M., J. Olson, J. Vega, and G. Harris. "Juvenile rheumatoid arthritis: clinical aspects and new rehabilitation treatment options." In Pediatric Gait: A New Millennium in Clinical Care and Motion Analysis Technology. IEEE, 2000. http://dx.doi.org/10.1109/pg.2000.858874.

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Pochappan, Smita S., D. K. Arvind, Jennifer Walsh, Alison M. Richardson, and Jan Herman. "Mobile Clinical Gait Analysis Using Orient Specks." In 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2012. http://dx.doi.org/10.1109/bsn.2012.34.

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"Pediatric Gait: A New Millennium in Clinical Care and Motion Analysis Technology [front matter]." In Pediatric Gait: A New Millennium in Clinical Care and Motion Analysis Technology. IEEE, 2000. http://dx.doi.org/10.1109/pg.2000.858866.

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"Author index." In Pediatric Gait: A New Millennium in Clinical Care and Motion Analysis Technology. IEEE, 2000. http://dx.doi.org/10.1109/pg.2000.858902.

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Evans, R. L., and D. K. Arvind. "Detection of Gait Phases Using Orient Specks for Mobile Clinical Gait Analysis." In 2014 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2014. http://dx.doi.org/10.1109/bsn.2014.22.

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Soda, P., A. Carta, D. Formica, and E. Guglielmelli. "A low-cost video-based tool for clinical gait analysis." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5333623.

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Shaji, Reshma, and Anshuman Singh. "Stridalyzer Insight Smart Insoles: a Clinical Grade Gait Analysis System." In 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). IEEE, 2019. http://dx.doi.org/10.1109/iot-siu.2019.8777489.

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Hellmers, Sandra, Sebastian Fudickar, Eugen Lange, Christian Lins, and Andreas Hein. "Validation of a motion capture suit for clinical gait analysis." In PervasiveHealth '17: 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3154862.3154884.

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Freed, Adam, Adrian D. C. Chan, Edward D. Lemaire, and Avi Parush. "Wearable EMG analysis for Rehabilitation (WEAR) - Surface electromyography in clinical gait analysis." In 2011 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2011. http://dx.doi.org/10.1109/memea.2011.5966728.

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