A qualitative approach for online activity recognition

We present a novel qualitative, dynamic length sliding window method which enables a mobile robot to temporally segment activities taking place in live RGB-D video. We demonstrate how activities can be learned from observations by encoding qualitative spatio-temporal relationships between entities in the scene. We also show how a Nearest Neighbour model can recognise activities taking place even if they temporally co-occur. Our system is validated on a challenging dataset of daily living activities

Grounding of Human Environments and Activities for Autonomous Robots

With the recent proliferation of robotic applications in domestic and industrial scenarios, it is vital for robots to continually learn about their environments and about the humans they share their environments with. In this paper, we present a framework for autonomous, unsupervised learning from various sensory sources of useful human ‘concepts’; including colours, people names, usable objects and simple activities. This is achieved by integrating state-of-the-art object segmentation, pose estimation, activity analysis and language grounding into a continual learning framework. Learned concepts are grounded to natural language if commentary is available, allowing the robot to communicate in a human-understandable way. We show, using a challenging, real-world dataset of human activities, that our framework is able to extract useful concepts, ground natural language descriptions to them, and, as a proof-of-concept, to generate simple sentences from templates to describe people and activities

A multilevel model for movement rehabilitation in Traumatic Brain Injury (TBI) using virtual environments

This paper presents a conceptual model for movement rehabilitation of traumatic brain injury (TBI) using virtual environments. This hybrid model integrates principles from ecological systems theory with recent advances in cognitive neuroscience, and supports a multilevel approach to both assessment and treatment. Performance outcomes at any stage of recovery are determined by the interplay of task, individual, and environmental/contextual factors. We argue that any system of rehabilitation should provide enough flexibility for task and context factors to be varied systematically, based on the current neuromotor and biomechanical capabilities of the performer or patient. Thus, in order to understand how treatment modalities are to be designed and implemented, there is a need to understand the function of brain systems that support learning at a given stage of recovery, and the inherent plasticity of the system. We know that virtual reality (VR) systems allow training environments to be presented in a highly automated, reliable, and scalable way. Presentation of these virtual environments (VEs) should permit movement analysis at three fundamental levels of behaviour: (i) neurocognitive bases of performance (we focus in particular on the development and use of internal models for action which support adaptive, on-line control); (ii) movement forms and patterns that describe the patients' movement signature at a given stage of recovery (i.e, kinetic and kinematic markers of movement proficiency), (iii) functional outcomes of the movement. Each level of analysis can also map quite seamlessly to different modes of treatment. At the neurocognitive level, for example, semi-immersive VEs can help retrain internal modeling processes by reinforcing the patients' sense of multimodal space (via augmented feedback), their position within it, and the ability to predict and control actions flexibly (via movement simulation and imagery training). More specifically, we derive four - key therapeutic environment concepts (or Elements) presented using VR technologies: Embodiment (simulation and imagery), Spatial Sense (augmenting position sense), Procedural (automaticity and dual-task control), and Participatory (self-initiated action). The use of tangible media/objects, force transduction, and vision-based tracking systems for the augmentation of gestures and physical presence will be discussed in this context

Epithelial cell shedding and barrier function: a matter of life and death at the small intestinal villus tip

The intestinal epithelium is a critical component of the gut barrier. Composed of a single layer of intestinal epithelial cells (IECs) held together by tight junctions, this delicate structure prevents the transfer of harmful microorganisms, antigens, and toxins from the gut lumen into the circulation. The equilibrium between the rate of apoptosis and shedding of senescent epithelial cells at the villus tip, and the generation of new cells in the crypt, is key to maintaining tissue homeostasis. However, in both localized and systemic inflammation, this balance may be disturbed as a result of pathological IEC shedding. Shedding of IECs from the epithelial monolayer may cause transient gaps or microerosions in the epithelial barrier, resulting in increased intestinal permeability. Although pathological IEC shedding has been observed in mouse models of inflammation and human intestinal conditions such as inflammatory bowel disease, understanding of the underlying mechanisms remains limited. This process may also be an important contributor to systemic and intestinal inflammatory diseases and gut barrier dysfunction in domestic animal species. This review aims to summarize current knowledge about intestinal epithelial cell shedding, its significance in gut barrier dysfunction and host-microbial interactions, and where research in this field is directed

Dispersion strengthening in vanadium microalloyed steels processed by simulated thin slab casting and direct charging. Part 2 - chemical characterisation of dispersion strengthening precipitates

The composition of the sub-15 nm particles in six related vanadium high strength low alloy steels, made by simulated thin slab direct charged casting, has been determined using electron energy loss spectroscopy (EELS). Such particles are considered to be responsible for dispersion hardening. For the first time, particles down to 4 nm in size have had their composition fully determined. In all the steels, the particles were nitrogen and vanadium rich and possibly slightly sub-stoichiometric carbonitrides. Equilibrium thermodynamics predicted much higher carbon to metal atomic ratios than observed in all cases so that kinetics and mechanical deformation clearly control the precipitation process. Thus it is important to formulate the steel with this in mind

QSRlib: a software library for online acquisition of qualitative spatial relations from video

There is increasing interest in using Qualitative Spatial Relations as a formalism to abstract from noisy and large amounts of video data in order to form high level conceptualisations, e.g. of activities present in video. We present a library to support such work. It is compatible with the Robot Operating System (ROS) but can also be used stand alone. A number of QSRs are built in; others can be easily added

Assessing recombinant vaccinia virus as a delivery system for fertility control vaccines in the brushtail possum (Trichosurus vulpecula)

Duckworth, J., Cross, M., Fleming, S., Scobie, S., Whelan, E., Prada, D., Mercer, A., Cowan, P

Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations

BACKGROUND: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer term control. METHODS: We introduce explainable machine learning to make predictions of hypoglycemia (270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user. RESULTS: Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model. CONCLUSIONS: Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user’s glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications