Unsupervised Video Understanding by Reconciliation of Posture Similarities

Understanding human activity and being able to explain it in detail surpasses mere action classification by far in both complexity and value. The challenge is thus to describe an activity on the basis of its most fundamental constituents, the individual postures and their distinctive transitions. Supervised learning of such a fine-grained representation based on elementary poses is very tedious and does not scale. Therefore, we propose a completely unsupervised deep learning procedure based solely on video sequences, which starts from scratch without requiring pre-trained networks, predefined body models, or keypoints. A combinatorial sequence matching algorithm proposes relations between frames from subsets of the training data, while a CNN is reconciling the transitivity conflicts of the different subsets to learn a single concerted pose embedding despite changes in appearance across sequences. Without any manual annotation, the model learns a structured representation of postures and their temporal development. The model not only enables retrieval of similar postures but also temporal super-resolution. Additionally, based on a recurrent formulation, next frames can be synthesized.Comment: Accepted by ICCV 201

A study to trial the use of inertial non-optical motion capture for ergonomic analysis of manufacturing work

It is going to be increasingly important for manufacturing system designers to incorporate human activity data and ergonomic analysis with other performance data in digital design modelling and system monitoring. However, traditional methods of capturing human activity data are not sufficiently accurate to meet the needs of digitised data analysis; qualitative data are subject to bias and imprecision, and optically derived data are hindered by occlusions caused by structures or other people in a working environment. Therefore, to meet contemporary needs for more accurate and objective data, inertial non-optical methods of measurement appear to offer a solution. This article describes a case study conducted within the aerospace manufacturing industry, where data on the human activities involved in aircraft wing system installations was first collected via traditional ethnographic methods and found to have limited accuracy and suitability for digital modelling, but similar human activity data subsequently collected using an automatic non-optical motion capture system in a more controlled environment showed better suitability. Results demonstrate the potential benefits of applying not only the inertial non-optical method in future digital modelling and performance monitoring but also the value of continuing to include qualitative analysis for richer interpretation of important explanatory factors

Detection of postural transitions using machine learning

The purpose of this project is to study the nature of human activity recognition and prepare a dataset from volunteers doing various activities which can be used for constructing the various parts of a machine learning model which is used to identify each volunteers posture transitions accurately. This report presents the problem definition, equipment used, previous work in this area of human activity recognition and the resolution of the problem along with results. Also this report sheds light on the process and the steps taken to undertake this endeavour of human activity recognition such as building of a dataset, pre-processing the data by applying filters and various windowing length techniques, splitting the data into training and testing data, performance of feature selection and feature extraction and finally selecting the model for training and testing which provides maximum accuracy and least misclassification rates. The tools used for this project includes a laptop equipped with MATLAB and EXCEL and MEDIA PLAYER CLASSIC respectively which have been used for data processing, model training and feature selection and Labelling respectively. The data has been collected using an Inertial Measurement Unit contains 3 tri-axial Accelerometers, 1 Gyroscope, 1 Magnetometer and 1 Pressure sensor. For this project only the Accelerometers, Gyroscope and the Pressure sensor is used. The sensor is made by the members of the lab named ‘The Technical Research Centre for Dependency Care and Autonomous Living (CETpD) at the UPC-ETSEIB campus. The results obtained have been satisfactory, and the objectives set have been fulfilled. There is room for possible improvements through expanding the scope of the project such as detection of chronic disorders or providing posture based statistics to the end user or even just achieving a higher rate of sensitivity of transitions of posture by using better features and increasing the dataset size by increasing the number of volunteers.Incomin

Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home

Frequency based Classification of Activities using Accelerometer Data

This work presents, the classification of user activities such as Rest, Walk and Run, on the basis of frequency component present in the acceleration data in a wireless sensor network environment. As the frequencies of the above mentioned activities differ slightly for different person, so it gives a more accurate result. The algorithm uses just one parameter i.e. the frequency of the body acceleration data of the three axes for classifying the activities in a set of data. The algorithm includes a normalization step and hence there is no need to set a different value of threshold value for magnitude for different test person. The classification is automatic and done on a block by block basis.Comment: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008. MFI 200

Performance anxiety in actors: symptoms, explanations and an Indian approach to treatment

There are numerous examples of renowned performers across the arts (actors and musicians) and in sports, which become news items in the media due to their performance anxiety (also called stage fright in English, or Lampenfieber in German). Given the number of celebrity actors suffering from stage fright, the number of those actors who do not make the news headlines in relation to their stage fright but nevertheless suffer from it must be even higher. In t his essay we provide an up to date account of the symptoms of stage fright, possible explanations for it and a range of known approaches to treatment. This is followed by an original approach to treating stage fright, based on Indian performance techniques, using details of a study undertaken in 2005.This multi-author journal article provides an in-depth analysis into the nature and treatment available for performance anxiety. The article offers examples of numerous artists and singers, including Sir Laurence Olivier, who had experienced stage fright for the duration of his performances of the title role in Ibsen’s The Master Builder (1965). The article run a clear analysis of the symptoms of stage fright and explain the nature of this psychophysical anxiety using clinical evidences and therapeutic methods. The key focus of the article is to compare and contrast two therapeutic methods for deducing stage anxiety: NLP, a well-established method, and SIT, which is an emerging method developed by Sreenath Nair using South Indian Bodily traditions. The article is based on a project carried out by Emerita Elizabeth Valentine and Daniel Meyer-Dinkgräfe in 2005, funded by the British Academy and the University of Wales Aberystwyth. The project compared two distinct methods of reducing stage fright in stage actors (Valentine et.al. 2006), one of them based on Indian approaches (South Indian Techniques, SIT) and the other Neuro Linguistic Programming (NLP). The SIT approach makes use of a range of psychophysical approaches deriving from the martial and performance traditions of Kerala. The study concludes that although many of the results were not statistically significant, ten of the eleven main effects were in the predicted direction, i.e. a greater effect for SIT than NLP. This multi-author journal article provides an in-depth analysis into the nature and treatment available for performance anxiety. The article offers examples of numerous artists and singers, including Sir Laurence Olivier, who had experienced stage fright for the duration of his performances of the title role in Ibsen’s The Master Builder (1965). The article run a clear analysis of the symptoms of stage fright and explain the nature of this psychophysical anxiety using clinical evidences and therapeutic methods. The key focus of the article is to compare and contrast two therapeutic methods for deducing stage anxiety: NLP, a well-established method, and SIT, which is an emerging method developed by Sreenath Nair using South Indian Bodily traditions. The article is based on a project carried out by Emerita Elizabeth Valentine and Daniel Meyer-Dinkgräfe in 2005, funded by the British Academy and the University of Wales Aberystwyth. The project compared two distinct methods of reducing stage fright in stage actors (Valentine et.al. 2006), one of them based on Indian approaches (South Indian Techniques, SIT) and the other Neuro Linguistic Programming (NLP). The SIT approach makes use of a range of psychophysical approaches deriving from the martial and performance traditions of Kerala. The study concludes that although many of the results were not statistically significant, ten of the eleven main effects were in the predicted direction, i.e. a greater effect for SIT than NLP. The study is a practice-based research demonstrating a highly relevant contribution to a therapeutic practice reducing stage fright. The research combines science and humanities indicating direct and wider impact

Virtual Meeting Rooms: From Observation to Simulation

Virtual meeting rooms are used for simulation of real meeting behavior and can show how people behave, how they gesture, move their heads, bodies, their gaze behavior during conversations. They are used for visualising models of meeting behavior, and they can be used for the evaluation of these models. They are also used to show the effects of controlling certain parameters on the behavior and in experiments to see what the effect is on communication when various channels of information - speech, gaze, gesture, posture - are switched off or manipulated in other ways. The paper presents the various stages in the development of a virtual meeting room as well and illustrates its uses by presenting some results of experiments to see whether human judges can induce conversational roles in a virtual meeting situation when they only see the head movements of participants in the meeting

An evaluation of the IDEEA™ activity monitor for estimating energy expenditure

Peer reviewedPublisher PD