536 research outputs found
Covariate conscious approach for Gait recognition based upon Zernike moment invariants
Gait recognition i.e. identification of an individual from his/her walking
pattern is an emerging field. While existing gait recognition techniques
perform satisfactorily in normal walking conditions, there performance tend to
suffer drastically with variations in clothing and carrying conditions. In this
work, we propose a novel covariate cognizant framework to deal with the
presence of such covariates. We describe gait motion by forming a single 2D
spatio-temporal template from video sequence, called Average Energy Silhouette
image (AESI). Zernike moment invariants (ZMIs) are then computed to screen the
parts of AESI infected with covariates. Following this, features are extracted
from Spatial Distribution of Oriented Gradients (SDOGs) and novel Mean of
Directional Pixels (MDPs) methods. The obtained features are fused together to
form the final well-endowed feature set. Experimental evaluation of the
proposed framework on three publicly available datasets i.e. CASIA dataset B,
OU-ISIR Treadmill dataset B and USF Human-ID challenge dataset with recently
published gait recognition approaches, prove its superior performance.Comment: 11 page
Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding
Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness
Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review
Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications
Robust arbitrary-view gait recognition based on 3D partial similarity matching
Existing view-invariant gait recognition methods encounter difficulties due to limited number of available gait views and varying conditions during training. This paper proposes gait partial similarity matching that assumes a 3-dimensional (3D) object shares common view surfaces in significantly different views. Detecting such surfaces aids the extraction of gait features from multiple views. 3D parametric body models are morphed by pose and shape deformation from a template model using 2-dimensional (2D) gait silhouette as observation. The gait pose is estimated by a level set energy cost function from silhouettes including incomplete ones. Body shape deformation is achieved via Laplacian deformation energy function associated with inpainting gait silhouettes. Partial gait silhouettes in different views are extracted by gait partial region of interest elements selection and re-projected onto 2D space to construct partial gait energy images. A synthetic database with destination views and multi-linear subspace classifier fused with majority voting are used to achieve arbitrary view gait recognition that is robust to varying conditions. Experimental results on CMU, CASIA B, TUM-IITKGP, AVAMVG and KY4D datasets show the efficacy of the propose method
Kvantitativna analiza pokreta u rehabilitaciji neuroloških poremećaja korišćenjem vizuelnih i nosivih senzora.
Neuroloska oboljenja, kao sto su Parkinsonova bolest i slog, dovode do ozbiljnih motornih
poremecaja, smanjuju kvalitet zivota pacijenata i mogu da uzrokuju smrt.
Rana dijagnoza i adekvatno lecenje su krucijalni faktori za drzanje bolesti pod kontrolom,
kako bi se omogucio normalan svakodnevni zivot pacijenata. Lecenje neurolo
skih bolesti obicno ukljucuje rehabilitacionu terapiju i terapiju lekovima, koje se
prilagodavaju u skladu sa stanjem pacijenta tokom vremena. Tradicionalne tehnike
evaluacije u dijagnozi i monitoringu neuroloskih bolesti oslanjaju se na klinicke evaluacione
alate, tacnije specijalno dizajnirane klinicke testove i skale. Medutim, iako su
korisne i najcesce koriscene, klinicke skale su sklone subjektivnim ocenama i nepreciznoj
interpretaciji performanse pacijenta...Neurological disorders, such as Parkinson's disease (PD) and stroke, lead to serious
motor disabilities, decrease the patients' quality of life and can cause the mortality.
Early diagnosis and adequate disease treatment are thus crucial factors towards keeping
the disease under control in order to enable the normal every-day life of patients.
The treatment of neurological disorders usually includes the rehabilitation therapy
and drug treatment, that are adapted based on the evaluation of the patient state over
time. Conventional evaluation techniques for diagnosis and monitoring in neurological
disorders rely on the clinical assessment tools i.e. specially designed clinical tests and
scales. However, although benecial and commonly used, those scales are descriptive
(qualitative), primarily intended to be carried out by a trained neurologist, and are
prone to subjective rating and imprecise interpretation of patient's performance..
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