A Review on Human Gait Detection

The human gait is the identification of human locomotive based on limbs position or action The tracking of human gait can help in various applications like normal and abnormal gait fall detection gender detection age detection biometrics and in some terrorist and criminal activity detection The present work carried out is a review of various methodologies employed in human gait detection The analysis describes that the different feature extraction and machine learning techniques to be adopted for the identification of human gait based on the purpose of the applicatio

Human gait identification using Kinect sensor

This study investigates a novel three-dimension gait recognition approach based on skeleton representation of motion by the cheap consumer level camera Kinect sensor. In this work, a new exemplification of human gait signature is proposed using the spatio-temporal variations in relative angles among various skeletal joints and changing of measured distance between limbs and land. These measurements are computed during one gait cycle. Further, we have created our own dataset based on Kinect sensor and extract two sets of dynamic features. Nearest Neighbors and Linear Discriminant Classifier (LDC) are used for classification. The results of the experiments show the proposed approach as an effective and human gait recognizer in comparison with current Kinect-based gait recognition methods

HUMAN GENDER CLASSIFICATION USING KINECT SENSOR: A REVIEW

Human Gender Classification using Kinect sensor aims to classifying people’s gender based on their outward appearance. Application areas of Kinect sensor technology includes security, marketing, healthcare, and gaming. However, because of the changes in pose, attire, and illumination, gender determination with the Kinect sensor is not a trivial task. It is based on a variety of characteristics, including biological, social network, face, and body aspects. In recent years, gender classification that utilizes the Kinect sensor became a popular and essential way for accurate gender classification. A variety of methods and approaches, like machine learning, convolutional neural networks, sport vector machine (SVM), etc., have been used for gender classification using a Kinect sensor. This paper presents the state of the art for gender classification, with a focus on the features, databases, procedures, and algorithms used in it. A review of recent studies on this subject using the Kinect sensor and other technologies is provided, together with information on the variables that affect the classification\u27s accuracy. In addition, several publicly accessible databases or datasets are used by researchers to classify people by gender are covered. Finlay, this overview offers insightful information about the potential future avenues for research on Kinect-based human gender classification

Model-based 3d gait biometric using quadruple fusion classifier

The area of gait biometrics has received significant interest in the last few years, largely due to the unique suitability and reliability of gait pattern as a human recognition technique. The advantage of gait over other biometrics is that it can perform non-intrusive data acquisition and can be captured from a distance. Current gait analysis approach can be divided into model-free and model-based approach. The gait data extracted for identification process may be influenced by ambient noise conditions, occlusion, changes in backgrounds and illumination when model-free 2D silhouette data is considered. In addition, the performance in gait biometric recognition is often affected by covariate factors such as walking condition and footwear. These are often related to low performance of personal verification and identification. While body biometrics constituted of both static and dynamics features of gait motion, they can complement one another when used jointly to maximise recognition performance. Therefore, this research proposes a model-based technique that can overcome the above limitations. The proposed technique covers the process of extracting a set of 3D static and dynamic gait features from the 3D skeleton data in different covariate factors such as different footwear and walking condition. A skeleton model from forty subjects was acquired using Kinect which was able to provide human skeleton and 3D joints and the features were extracted and categorized into static and dynamic data. Normalization process was performed to scale down the features into a specific range of structure, followed by feature selection process to select the most significant features to be used in classification. The features were classified separately using five classification algorithms for static and dynamic features. A new fusion framework is proposed based on score level fusion called Quadruple Fusion Framework (QFF) in order to combine the static and dynamic features obtained from the classification model. The experimental result of static and dynamic fusion achieved the accuracy of 99.5% for footwear covariates and 97% for walking condition covariates. The result of the experimental validation demonstrated the viability of gait as biometrics in human recognition

Proficient ambient intelligence based on the wisely selection of data sources in heterogeneous environments

Nowadays society tends to be more and more technologically evolved, with that a lot of personal information is shared with computational systems through online records; microphones that perform voice recognition in which it is processed remotely; application of filters on person portraits or recognition of the age at which the image is saved on a server to improve the standard of an Artificial Intelligence (AI). The devices we currently interact with end up compiling information about our consumption habits, routines, collected photographs, videos or personal content. Sometimes exposing our privacy to third parties, it is not noticeable where that data is been saved, or that is being stored. And if we could have a simpler device to perform some tasks, in which there was no camera, microphone or storage. No personal data will be processed locally or remotely, so the regulation European Union (EU) law on data protection and privacy, General Data Protection Regulation (GDPR), will not affect this kind of device. Furthermore, if a device of this nature was located in a public place, there would be no need for any requirement, consent or authorization to the intervening users or only those who approach the device, because would not differentiate the user or even recognize him, since the collection of gesture commands is performed through non-evasive sensors. Recently the world was surprised by the cases of a new coronavirus which caused a pandemic on a planetary scale. New rules had to be applied, to the way we interact with objects in order not to spread the disease. It was created a prototype discriminated in this study, using a grid of ultrasonic sensors, capable of recognize trained gestures. This interaction can be made from a distance of the prototype, and do not require contact, in which it solves a human interaction with the computer avoiding that the user has to use the touch on a component, thus avoiding the contagion of Corona virus disease of 2019 (COVID-19). The early stage results evidence that the proposed system is suitable to create a new input type of Human Interface Device (HID), and may replace devices as a remote control of television, to a new way to interact with information panels in public places, like a shopping mall, airports, train and bus stations.Hoje em dia a sociedade tende a estar cada vez mais evoluída tecnologicamente, consequentemente muitas informações pessoais são compartilhadas com sistemas computacionais através de registros online; microfones que realizam reconhecimento de voz, em que é processado remotamente; aplicação de filtros em retratos de pessoas ou reconhecimento da idade, em que a foto é guardada em um servidor para melhorar o padrão de uma Inteligência Artificial (AI). Atualmente os dispositivos com os quais interagimos acabam compilando informações sobre os nossos hábitos de consumo, rotinas, fotos e vídeos armazenados ou conteúdos pessoais. Por vezes, expondo nossa privacidade a terceiros, não é perceptível onde os dados foram guardados ou onde estão a ser armazenados. E se pudéssemos ter um dispositivo mais simples para realizar algumas tarefas, em que não possuísse câmera, microfone ou armazenamento. Nenhum dado pessoal será processado local ou remotamente, portanto, a legislação da União Europeia (EU) sobre proteção e privacidade de dados, Regulamento Geral de Proteção de Dados (GDPR), não afetará este tipo de dispositivo. Além disso, se um dispositivo desta natureza estivesse localizado em local público, não haveria necessidade de qualquer exigência, consentimento ou autorização aos utilizadores intervenientes ou até mesmo aos que apenas se aproximam do dispositivo, pois não distinguiria o utilizador e também não o reconheceria, já que a recolha de comandos por gestos é realizada por via de sensores não evasivos. Recentemente, o mundo foi surpreendido por vários casos de um novo coronavírus que causou uma pandemia em escala mundial. Novas regras tiveram que ser aplicadas, à forma como interagimos com os objetos para não espalhar a doença. Foi criado um protótipo discriminado neste estudo, utilizando uma grelha de sensores ultrassônicos, capaz de reconhecer gestos treinados. Esta interação pode ser feita à distância do protótipo, e não requer contato, no qual se resolve uma interação humana com o computador evitando que o utilizador use o tato no dispositivo, evitando assim o contágio de (COVID-19). Os resultados da fase inicial evidenciam que o sistema proposto é adequado para criar um novo tipo de Dispositivo de Interface Humana (HID), podendo substituir dispositivos como um controle remoto de televisão, ou uma nova forma de interagir com painéis de informação em locais públicos, como um shopping center, aeroportos, estações de comboios e de autocarros