Estimation of posture and prediction of the elderly getting out of bed using a body pressure sensor

We propose an IoT support system for estimating the posture of the care recipient on the bed from the body pressure of the care recipient measured by a sheet-type body pressure sensor, and detecting the posture related to leaving the bed in real time. In addition, we propose a method that predicts getting out of the bed before the care recipient takes a posture related to getting out of the bed by considering the state transition. Intervention experiment showed that using body pressure features as an explanatory variable and applying machine learning, 16 types of postures on the bed of care recipients with an F value of 0.7 or more could be identified. From the experiment without intervention, by applying the hidden Markov model, we calculated the transition probability to each hidden state when the care recipient getting out of the bed and the transition probability to each hidden state when the care recipient not getting out of the bed. As a result, there was a difference of about 0.1 in the transition probability of the state related to raising upper body

RGB-D datasets using microsoft kinect or similar sensors: a survey

RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms

Hazards and Risks at Rotary Screen Printing (Part 2/6): Analysis of Machine-operators’ Posture via Rapid-Upper-Limb-Assessment (RULA)

Musculoskeletal-disorders (MSDs) are one of the-most-noticeable global-problems, ergonomists come- across, in the-workplace. To-prevent MSDs, their-root-causes, particularly, poor/awkward-postures, should be identified, first. This-study examined such-postures, at-printing-section of finishing-department, at textile-mill, via numerical-rating ergonomic-assessment-tool, namely Rapid-Upper-Limb-Assessment (RULA). In-addition, ISO 11226: Ergonomics, evaluation of static-working-postures (2000); and EN-1005- 4: Safety of machinery, human-physical-perfor­mance, and evaluation of working-postures, in relation to-machinery, were-used, as a-reference. The-RULA-analysis, on the-two-chosen highest-risk-postures (#1 and #2), identified 2nd and 3rd action-level of danger of musculoskeletal-injury (MSI), necessitating further-investigation, and possible-change/correction. These-investigations revealed the-following-risks of MSDs or MSIs, for the-posture #1: (1) awkward-back posture--trunk-bending-forward, at the-waist, with 46 degrees deviation, from neutral-posture; (2) visually-demanding-operation (risk of eye-strain); (3) contact-pressure; (4) stress on lower-extremities; and (5) standing-static-posture. For the-posture #2, the-risks were: (1) awkward-neck, and head-posture, with 38 degrees-deviation, from neutral-posture; (2) risk of eye-strain; (3) stress on lower-extremities; and (5) standing static-posture. Several-tailored recommendations, to-control, or prevent, the-identified-hazards, were-offered, including: engineering, work-practice/administrative, and PPE-approaches. In-addition, 3 areas, for-further-research, were identified. Moreover, informative-synopsis on relevant-issues were-also-given, such-as on: Work-related MSDs (WRMSDs) and their-prevalence; Working in neutral-posture; Awkward-posture, its-effects, and relevance to WRMSDSs; Upper-limb-MSDs; RULA; Spine and awkward-back-posture; Visually demanding-operation and eye-strain; Printing-defects; Digital-image-processing-techniques; Contact pressure; Standing-static-posture; Stress on lower-extremities; and GSE- automatic-dispensing systems, among-others. The-study is important, for textile-printing-industry, particularly the-management of the-textile-printing-section, at the-textile-mill, as it provides specific-recommendations, for consideration to-implementation, to-reduce and control the-risks of WRMSDs. It-also-adds (in its-small-way) to-the-body of knowledge on WRMSDs. Keywords: textile industry, MSDs, WRMSDs, awkward-posture, printing defects, machine operator

Possibilities of man-machine interaction through the perception of human gestures

A mesura que les màquines s'utilitzen interaccionant cada cop més amb les persones, la necessitat d'interfícies més amigables esdevé una necessitat creixent. La comunicació oral persona-màquina com una forma d'interacció utilitzant el llenguatge natural és cada vegada més usual. La interpretació dels gestos humans pot, en certes aplicacions, complementar aquesta comunicació oral. Aquest article descriu un sistema d'interpretació dels gestos basat en la visió per computador. El procés d'interpretació realitza la detecció i seguiment d'un operador humà, i a partir dels seus moviments interpreta un conjunt específic d'ordres gestuals, en temps real.As man-machine interaction grows there is an increasing need for friendly interfaces. Human-machine oral communication as a means of natural language interaction is becoming quite common. Interpretation of human gestures can, in some applications, complement such communication. This article describes an interpretation of gestures procedure. The system is based on a computer vision system for the detection and tracking of a human operator and the interpretation of a specific set of human gestures in real time

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Human Pose Tracking from Monocular Image Sequences

This thesis proposes various novel approaches for improving the performance of automatic 2D human pose tracking system including multi-scale strategy, mid-level spatial dependencies to constrain more relations of multiple body parts, additional constraints between symmetric body parts and the left/right confusion correction by a head orientation estimator. These proposed approaches are employed to develop a complete human pose tracking system. The experimental results demonstrate significant improvements of all the proposed approaches towards accuracy and efficiency