A Study on Gait-Based Gender Classification
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Gender is an important cue in social activities. In this correspondence, we present a study and analysis of gender classification based on human gait. Psychological experiments were carried out. These experiments showed that humans can recognize gender based on gait information, and that contributions of different body components vary. The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy. The proposed method which combines human knowledge achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head and hair, back, chest and thigh are more discriminative than other components. We also did challenging cross-race experiments that used Asian gait data to classify the gender of Europeans, and vice versa. Encouraging results were obtained. All the above prove that gait-based gender classification is feasible in controlled environments. In real applications, it still suffers from many difficulties, such as view variation, clothing and shoes changes, or carrying objects. We analyze the difficulties and suggest some possible solutions.Keywords:
Discriminative model
Variation (astronomy)
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Hip osteoarthritis (OA), or the degeneration of cartilage in the hip joint, is a common and chronic condition that is growing in prevalence around the world. OA typically causes significant joint pain, lack of mobility, and abnormal gait patterns in affected individuals. Total hip arthroplasty (THA) is used to treat OA, and of the many postoperative methods of assessing success of the procedure, one that is particularly useful is gait analysis. Gait analysis provides a quantitative view of patient gait biomechanics by examining many relevant gait parameters and is very useful to evaluate sequelae following THA. The present paper synthesizes the recent literature surrounding post-THA gait analysis to gain a deeper understanding of how gait analysis may be used to improve THA and its corresponding patient outcomes.
Biomechanics
Hip pain
Gait cycle
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Spastic cerebral palsy
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Three-dimensional gait analysis is a systematic measurement, description, and assessment of human gait. Gait analysis is established as a useful diagnostic tool in patients with gait problems, as it is not possible to obtain an adequate and detailed understanding of such a complex mechanism as gait in a conventional clinical examination. The method has provided a better understanding of both normal gait and abnormal gait patterns; it is a suitable instrument for evaluation of treatment results as well as for scientific work. The first gait laboratory for clinical use in Norway was established in 2002 in the Section for child neurology at Rikshospitalet University Hospital in Oslo, Norway. In this article the procedure for gait analysis is described and the clinical value is indicated by a case record of a child with cerebral palsy. Gait analysis has entailed a change of policy with regard to surgical treatment in this patient group. Previously, operative intervention at a single level was usual, whereas current practice involves simultaneous interventions at several levels of both lower extremities. After three years' experience we recommend gait analysis in routine diagnostics, particularly as a preoperative evaluation, in all children with gait problems and in the follow up after surgery or other treatment.
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Modern gait analysis is a powerful non-invasive tool for calculating the mechanical factors involved in pathological processes such as knee osteoarthritis (OA). Although very accurate measurements can be made, the clinical applicability and widespread use of gait analysis have been hindered by a lack of appropriate data analysis techniques for reducing and analysing the resulting large volumes of highly correlated gait data. This paper introduces a multidimensional galt data analysis technique that simultaneously considers multiple time-varying and discrete measures, exploiting the correlation structure between and within the measures. The multidimensional analysis technique was used to detect discriminatory mechanical features of knee OA gait patterns that involved interacting changes in several gait measures, at specific time portions of the gait cycle. The two most discriminatory features described a dynamic alignment difference and a loading response difference with knee OA.
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Purpose of review The literature was reviewed to describe the role of gait analysis in the orthopaedic management of ambulatory children with cerebral palsy and examine the current best evidence to support these roles. Recent findings Gait laboratory analysis is superior to visual or observational analysis of gait because it provides an objective record of gait that is able to quantify the magnitude of deviations of pathologic gait from normal and also explain these abnormalities. Recognizable gait patterns can be classified and used for making treatment decisions, the effectiveness of which can be assessed using gait analysis as a measure of gait outcomes. There are many sources of variability, however, including patients themselves, the gait laboratories and testing processes, interpretation of data and surgeons' surgical recommendations. Summary Although gait analysis has been shown to alter decision making, there is little evidence that the decisions based on gait analysis lead to better outcomes. Consequently, clinical gait analysis remains controversial, with wide variation in the rates of utilization of gait analysis in the management of children with ambulatory cerebral palsy. The time is ripe for clinical trials and cohort studies to provide the evidence to establish the appropriate utilization of this technology.
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Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Suhana Abdul Rahim, Hamzah Sakeran, Ahmad Faizal Salleh, Mohammad Shahril Salim, Wan Zuki Azman Wan Muhamad, Mohamad Azlan Mohamed Shapie; Statistical analysis in clinical gait analysis using Kinovea between normal and simulated abnormal gaits. AIP Conf. Proc. 21 February 2023; 2562 (1): 050005. https://doi.org/10.1063/5.0114628 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
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Gait Disturbance
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