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Associated Spatio-Temporal Capsule Network for Gait Recognition
It is a challenging task to identify a person based on her/his gait patt...
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Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait
Diagnosing Parkinson's disease is a complex task that requires the evalu...
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Comparison of user models based on GMM-UBM and i-vectors for speech, handwriting, and gait assessment of Parkinson's disease patients
Parkinson's disease is a neurodegenerative disorder characterized by the...
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Development of a sensory-neural network for medical diagnosing
Performance of a sensory-neural network developed for diagnosing of dise...
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Simultaneous Energy Harvesting and Gait Recognition using Piezoelectric Energy Harvester
Piezoelectric energy harvester, which generates electricity from stress ...
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Scattering Features for Multimodal Gait Recognition
We consider the problem of identifying people on the basis of their walk...
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Gait Recognition using Multi-Scale Partial Representation Transformation with Capsules
Gait recognition, referring to the identification of individuals based o...
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Multimodal Gait Recognition for Neurodegenerative Diseases
In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses. As an important motor symptom, gait disturbance is usually used for diagnosis and evaluation of diseases; moreover, the use of multi-modality analysis of the patient's walking pattern compensates for the one-sidedness of single modality gait recognition methods that only learn gait changes in a single measurement dimension. The fusion of multiple measurement resources has demonstrated promising performance in the identification of gait patterns associated with individual diseases. In this paper, as a useful tool, we propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases, between patients with different severity levels of Parkinson's disease and between healthy individuals and patients, by fusing and aggregating data from multiple sensors. A spatial feature extractor (SFE) is applied to generating representative features of images or signals. In order to capture temporal information from the two modality data, a new correlative memory neural network (CorrMNN) architecture is designed for extracting temporal features. Afterwards, we embed a multi-switch discriminator to associate the observations with individual state estimations. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.
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