Similarity K-d tree method for sparse point pattern matching with underlying non-rigidity

We propose a method for matching non-affinely related sparse model and data point-sets of identical cardinality, similar spatial distribution and orientation. To establish a one-to-one match, we introduce a new similarity K-dimensional tree. We construct the tree for the model set using spatial sparsity priority order. A corresponding tree for the data set is then constructed, following the sparsity information embedded in the model tree. A matching sequence between the two point sets is generated by traversing the identically structured trees. Experiments on synthetic and real data confirm that this method is applicable to robust spatial matching of sparse point-sets under moderate non-rigid distortion and arbitrary scaling, thus contributing to non-rigid point-pattern matching. © 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved

GESTURE RECOGNITION FOR PENCAK SILAT TAPAK SUCI REAL-TIME ANIMATION

The main target in this research is a design of a virtual martial arts training system in real-time and as a tool in learning martial arts independently using genetic algorithm methods and dynamic time warping. In this paper, it is still in the initial stages, which is focused on taking data sets of martial arts warriors using 3D animation and the Kinect sensor cameras, there are 2 warriors x 8 moves x 596 cases/gesture = 9,536 cases. Gesture Recognition Studies are usually distinguished: body gesture and hand and arm gesture, head and face gesture, and, all three can be studied simultaneously in martial arts pencak silat, using martial arts stance detection with scoring methods. Silat movement data is recorded in the form of oni files using the OpenNI ™ (OFW) framework and BVH (Bio Vision Hierarchical) files as well as plug-in support software on Mocap devices. Responsiveness is a measure of time responding to interruptions, and is critical because the system must be able to meet the demand

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation

Real-time Assessment and Visual Feedback for Patient Rehabilitation Using Inertial Sensors

Rehabilitation exercises needs have been continuously increasing and have been projected to increase in future as well based on its demand for aging population, recovering from surgery, injury and illness and the living and working lifestyle of the people. This research aims to tackle one of the most critical issues faced by the exercise administers-Adherence or Non-Adherence to Home Exercise problems especially has been a significant issue resulting in extensive research on the psychological analysis of people involved. In this research, a solution is provided to increase the adherence of such programs through an automated real-time assessment with constant visual feedback providing a game like an environment and recording the same for analysis purposes. Inertial sensors like Accelerometer and Gyroscope has been used to implement a rule-based framework for human activity recognition for measuring the ankle joint angle. This system is also secure as it contains only the recordings of the data and the avatar that could be live fed or recorded for the treatment analysis purposes which could save time and cost. The results obtained after testing on four healthy human subjects shows that with proper implementation of rule parameters, good quality and quantity of the exercises can be assessed in real time

Recognizing specific errors in human physical exercise performance with Microsoft Kinect

The automatic assessment of human physical activity performance is useful for a number of beneficial systems including in-home rehabilitation monitoring systems and Reactive Virtual Trainers (RVTs). RVTs have the potential to replace expensive personal trainers to promote healthy activity and help teach correct form to prevent injury. Additionally, unobtrusive sensor technologies for human tracking, especially those that incorporate depth sensing such as Microsoft Kinect, have become effective, affordable, and commonplace. The work of this thesis contributes towards the development of RVT systems by using RGB-D and tracked skeletal data collected with Microsoft Kinect to assess human performance of physical exercises. I collected data from eight volunteers performing three exercises: jumping jacks, arm circles, and arm curls. I labeled each exercise repetition as either correct or one or more of a select number of predefined erroneous forms. I trained a statistical model using the labeled samples and developed a system that recognizes specific structural and temporal errors in a test set of unlabeled samples. I obtained classification accuracies for multiple implementations and assess the effectiveness of the use of various features of the skeletal data as well as various prediction models

Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures

International audienceThis paper presents a cognitive vision approach to recognize a set of interesting activities of daily living (ADLs) for elderly at home. The proposed approach is composed of a video analysis component and an activity recognition component. A video analysis component contains person detection, person tracking and human posture recognition. A human posture recognition is composed of a set of postures models and a dedicated human posture recognition algorithm. Activity recognition component contains a set of video event models and a dedicated video event recognition algorithm. In this study, we collaborate with medical experts (gerontologists from Nice hospital) to define and model a set of scenarios related to the interesting activities of elderly. In our approach, we propose ten 3D key human postures usefull to recognize a set of interesting human activities regardless of the environment. The novelty of our approach is the proposed 3D key postures and the set of activity models of elderly person living alone in her/his own home. To validate our proposed models, we have performed a set of experiments in the Gerhome laboratory which is a realistic site reproducing the environment of a typical apartment. For these experiments, we have acquired and processed ten video sequences with one actor. The duration of each video sequence is about ten minute

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Reconhecimento de interações cliente-produto em espaços de vendas

O reconhecimento de atividades humanas baseado em visão por computadores é uma área de investigação desafiante com crescente interesse entre os investigadores e empresas. Com a introdução de sensores RGB-D, que adiciona a dimensão de profundidade às câmeras convencionais, é possível gerar modelos de esqueletos em tempo real. Com base em atributos extraídos do esqueleto e em modelos de aprendizagem automática treinados é possível reconhecer as atividades humanas. Nesta dissertação, propõe-se um modelo para reconhecer interações de clientes com produtos em prateleiras de lojas com base em informação do esqueleto e RGB-D, assim como algoritmos existentes para deteção de objetos e gestos. Estes algoritmos são interligados num único sistema e testados num ambiente de loja simulado, caracterizado por interações humano-objeto, necessidade de acompanhar simultaneamente diferentes atividades de clientes em tempo real e um ângulo de visão típico de câmeras em lojas (vista superior) que potencia oclusões entre sujeitos ou partes do corpo deste. As principais contribuições deste estudo são a introdução de um novo modelo que combina reconhecimento de objetos e gestos e a análise detalhada dos resultados sobre diversas perspetivas consideradas pertinentes. Acresce o conjunto de dados recolhido que está disponível para fins de investigação, como o desenvolvimento, melhoria e comparação de desempenho de modelos destinados a este contexto aplicacional. Três cenários com quatro tipos de produto e graus de complexidade distintos são avaliados - um único cliente a interagir com duas prateleiras, dois clientes e uma prateleira para cada e dois clientes disputando duas prateleiras. No modelo desenvolvido, o reconhecimento de interações com a prateleira passa pela deteção de extensões e flexões do braço trama-a-trama, que posteriormente são generalizadas em gestos e interações para um intervalo de tramas. O modelo desenvolvido apresenta um f1-score médio de 69,78% para deteção da extensão/flexão do braço e 66,46% para deteção do tipo de produto. Com base na agregação de informações de deteção de objetos e gestos, são reconhecidas 53.97% das interações de prateleira testadas (recall) e detetadas corretamente 30.47% das vezes (precision).Computer vision-based human activities recognition is a challenging research area with increasing interest amongst researchers and companies. The introduction of RGB-D sensors. which add the depth dimension to the conventional colored 2D cameras, allows real-time skeleton model generation of humans. This skeleton data provides meaningful information that enabled researchers to model human activities by training machine learning models and later utilize them to recognize activities. In this dissertation, we propose a model to recognize customer interactions with products in store’s shelves based on RGB-D and skeleton data, as well as existing algorithms for gesture and object detection. We demonstrate how those existing algorithms perform in an integrated system tested in a simulated retail store context, particularly characterized by human-object interactions, the capacity to simultaneously track in real-time different customer’s activities and a field of view captured by the sensor that is typical in retail environments (top view), which makes it prone to occlusions between subjects and body parts. The main contributions of our study are the introduction of a novel model that combines object and gesture recognition as well as detailed performance metrics regarding different analytical perspectives. The collected dataset is available for researching purposes, namely to allow different model’s development, improvement and performance comparison in this specific research area. Three scenarios with four types of products and different recognition complexities are evaluated – a single customer interacting with two shelves, two customers interacting with a one shelf each and two customers disputing two shelves. In the developed model, recognizing shelf interactions is done through the generalization of frame by frame arm extension/flexion detections in gestures and interactions regarding specific frame intervals. The developed model has a f1-score of 69.78% for arm extension/flexion detection and 66.46% for product type detection. Based on the aggregation of gesture and object detection information we recognize 53.97% of the existing shelf interactions (recall) with a precision of 30.47%

Assessment of human kinematic performance with non-contact measurements for tele-rehabilitation

 Aging of population is challenging the traditional rehabilitation services for various movement disorders. In the foreseeable future, tele-rehabilitation will be a contributive factor for the well-being of the older generation. This research has tackled a series of problems in developing an automated assessment tool for human kinematic performance in tele-rehabilitation with optoelectronic bio-kinematic motion capture devices and preliminarily confirmed its applicability