Methods for engineering symbolic human behaviour models for activity recognition

This work investigates the ability of symbolic models to encode context information that is later used for generating probabilistic models for activity recognition. The contributions of the work are as follows: it shows that it is possible to successfully use symbolic models for activity recognition; it provides a modelling toolkit that contains patterns for reducing the model complexity; it proposes a structured development process for building and evaluating computational causal behaviour models

Application of materials used in everyday life to create radiological models of human tissues

Radiological physical models (phantoms) are used for quality control, for evaluation and analysis of a given X-ray device. They are easily available, providing the X-ray technicians with consistent results and safety compared to using a live subject. Phantoms must respond in the same or similar way to human tissues and organs when exposed to radiation, and therefore must be manufactured from materials with the same or similar X-ray properties. The purpose of this report is to study materials from everyday life as suitable substitutes for human tissues in quality control tasks. In daily life, everything we come in contact with could be used as a material for a physical phantom: plastic, wood, glass, water, salt, sugar, gelatin, paraffin, and others. Due to the advantages of plastic—cheap, flexible, waterproof, and easy to manufacture, it becomes a reliable material in many fields, including 3D printing. After the physical model is printed, it should be checked whether or not it is the same or similar to the real human tissues. All results are processed by using the DICOM processing program (Digital Imaging and Communication in Medicine) by comparing the density using the Hounsfield units scal

What's cooking and why? Behaviour recognition during unscripted cooking tasks for health monitoring

Nutrition related health conditions can seriously decrease quality of life; a system able to monitor the kitchen activities and eating behaviour of patients could provide clinicians with important indicators for improving a patient’s condition. To achieve this, the system has to reason about the person’s actions and goals. To address this challenge, we present a behaviour recognition approach that relies on symbolic behaviour repre- sentation and probabilistic reasoning to recognise the person’s actions, the type of meal being prepared and its potential impact on a patient’s health. We test our approach on a cooking dataset containing unscripted kitchen activities recorded with various sensors in a real kitchen. The results show that the approach is able to recognise the sequence of executed actions and the prepared meal, to determine whether it is healthy, and to reason about the possibility of depression based on the type of meal

Development of an inkjet calibration phantom for x-ray imaging studies

Introduction: 3D anthropomorphic models of human tissues have become a requirement for conducting realistic virtual studies. One of the current directions in the research of X-ray imaging is the development of physical models with 3D printing techniques using specific materials aiming to obtain replica of the human body tissues with similar radiological characteristics.Aim: The aim of this study is to create a calibration phantom for establishing the X-ray properties of different cartridge infills and their suitability to represent the X-ray properties of different breast types.Materials and Methods: A physical calibration model consisting of 22 objects was designed and printed by using an inkjet printer. A mixture was obtained from 5 mL printer ink and 3 g of potassium iodide (KI), which was used to fill the printer’s cartridge and to print the model on a set of plain office paper. Experimental X-ray images of the physical model were acquired on radiographic system SEDECAL X PLUS LP+. The obtained attenuation coefficient of the printing mixture was evaluated and compared to the breast tissue coefficients corresponding to the used X-ray energy.Results and Discussion: The physical model was printed on ten office sheets and stacked above one another. The obtained attenuation coefficient of the printing mixture was found very similar to that of the glandular tissue of the breast for the used X-ray energy.Conclusion: The obtained printer ink-KI mixture is suitable for representing the glandular part of breast tissue. The method has the potential to be used for creation of a realistic physical breast model

Semantic Annotation for the CMU-MMAC Dataset: [research data]

Objective: To create semantic annotation of the Carnegie Mellon University Multi-Modal Activity Database (CMU-MMAC) grand challenge kitchen dataset, which is often cited but, due to missing and incomplete annotation, almost never used

Semantic Annotation for the CMU-MMAC Dataset (Version 2): [research data]

Objective: To create semantic annotation of the Carnegie Mellon University Multi-Modal Activity Database (CMU-MMAC) grand challenge kitchen dataset, which is often cited but, due to missing and incomplete annotation, almost never used. Changes to Earlier Version: (1) Changes to the starting times and the actual plan sequences. The previous version contains errors in the plan sequences. (2) The dataset now also contains the models that were used to validate the annotation plan sequences. The models were also used for semantic reasoning on the environment properties

Kitchen Task Assessment Dataset for Measuring Errors due to Cognitive Impairments: [research data]

The dataset contains different types of sensor data: acceleration data from different objects and from body-worn sensors as well as annotation. The dataset consists of 12 normal runs and 12 erroneous runs, where the participants simulated typical errors due to dementia. The annotation consists of both action annotation in the form “action_object_object” as well es annotation about the object being manipulated and the hand that is manipulating it

Challenges in Annotation of useR Data for UbiquitOUs Systems: Results from the 1st ARDUOUS Workshop

Labelling user data is a central part of the design and evaluation of pervasive systems that aim to support the user through situation-aware reasoning. It is essential both in designing and training the system to recognise and reason about the situation, either through the definition of a suitable situation model in knowledge-driven applications, or through the preparation of training data for learning tasks in data-driven models. Hence, the quality of annotations can have a significant impact on the performance of the derived systems. Labelling is also vital for validating and quantifying the performance of applications. In particular, comparative evaluations require the production of benchmark datasets based on high-quality and consistent annotations. With pervasive systems relying increasingly on large datasets for designing and testing models of users' activities, the process of data labelling is becoming a major concern for the community. In this work we present a qualitative and quantitative analysis of the challenges associated with annotation of user data and possible strategies towards addressing these challenges. The analysis was based on the data gathered during the 1st International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS) and consisted of brainstorming as well as annotation and questionnaire data gathered during the talks, poster session, live annotation session, and discussion session