Personalised meta-learning for human activity recognition with few-data.

State-of-the-art methods of Human Activity Recognition(HAR) rely on a considerable amount of labelled data to train deep architectures. This becomes prohibitive when tasked with creating models that are sensitive to personal nuances in human movement, explicitly present when performing exercises and when it is infeasible to collect training data to cover the whole target population. Accordingly, learning personalised models with few data remains an open challenge in HAR research. We present a meta-learning methodology for learning-to-learn personalised models for HAR; with the expectation that the end-user only need to provide a few labelled data. These personalised HAR models benefit from the rapid adaptation of a generic meta-model using provided few end-user data. We implement the personalised meta-learning methodology with two algorithms, Personalised MAML and Personalised Relation Networks. A comparative study shows significant performance improvements against state-of-the-art deep learning algorithms and other personalisation algorithms in multiple HAR domains. Also, we show how personalisation improved meta-model training, to learn a generic meta-model suited for a wider population while using a shallow parametric model

Learning to compare with few data for personalised human activity recognition.

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist view of learning-to-learn, such that models are rapidly transferable to related (but different) new tasks. Unlike task-specific model training, a meta-learner’s training instance - referred to as a meta-instance - is a composite of two sets: a support set and a query set of instances. In our work, we introduce learning-to-learn personalised models from few data. We motivate our contribution through an application where personalisation plays an important role, mainly that of human activity recognition for self-management of chronic diseases. We extend the meta-instance creation process where random sampling of support and query sets is carried out on a reduced sample conditioned by a domain-specific attribute; namely the person or user, in order to create meta-instances for personalised HAR. Our meta-learning for personalisation is compared with several state-of-the-art meta-learning strategies: 1) matching network (MN), which learns an embedding for a metric function; 2) relation network (RN) that learns to predict similarity between paired instances; and 3) MAML, a model-agnostic machine-learning algorithm that optimizes the model parameters for rapid adaptation. Results confirm that personalised meta-learning significantly improves performance over non personalised meta-learners

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

The snowflake effect: the future of mashups and learning

Emerging technologies for learning report - Article exploring web mashups and their potential for educatio

Personalised Learning: Developing a Vygotskian Framework for E-learning

Personalisation has emerged as a central feature of recent educational strategies in the UK and abroad. At the heart of this is a vision to empower learners to take more ownership of their learning and develop autonomy. While the introduction of digital technologies is not enough to effect this change, embedding the affordances of new technologies is expected to offer new routes for creating personalised learning environments. The approach is not unique to education, with consumer technologies offering a 'personalised' relationship which is both engaging and dynamic, however the challenge remains for learning providers to capture and transpose this to educational contexts. As learners begin to utilise a range of tools to pursue communicative and collaborative actions, the first part of this paper will use analysis of activity logs to uncover interesting trends for maturing e-learning platforms across over 100 UK learning providers. While personalisation appeals to marketing theories this paper will argue that if learning is to become personalised one must ask what the optimal instruction for any particular learner is? For Vygotsky this is based in the zone of proximal development, a way of understanding the causal-dynamics of development that allow appropriate pedagogical interventions. The second part of this paper will interpret personalised learning as the organising principle for a sense-making framework for e-learning. In this approach personalised learning provides the context for assessing the capabilities of e-learning using Vygotsky’s zone of proximal development as the framework for assessing learner potential and development