A knowledge-light approach to personalised and open-ended human activity recognition.

Human Activity Recognition (HAR) is a core component of clinical decision support systems that rely on activity monitoring for self-management of chronic conditions such as Musculoskeletal Disorders. Deployment success of such applications in part depend on their ability to adapt to individual variations in human movement and to facilitate a range of human activity classes. Research in personalised HAR aims to learn models that are sensitive to the subtle nuances in human movement whilst Open-ended HAR learns models that can recognise activity classes out of the pre-defined set available at training. Current approaches to personalised HAR impose a data collection burden on the end user; whilst Open-ended HAR algorithms are heavily reliant on intermediary-level class descriptions. Instead of these 'knowledge-intensive' HAR algorithms; in this article, we propose a 'knowledge-light' method. Specifically, we show how by using a few seconds of raw sensor data, obtained through micro-interactions with the end-user, we can effectively personalise HAR models and transfer recognition functionality to new activities with zero re-training of the model after deployment. We introduce a Personalised Open-ended HAR algorithm, MNZ, a user context aware Matching Network architecture and evaluate on 3 HAR data sources. Performance results show up to 48.9% improvement with personalisation and up to 18.3% improvement compared to the most common 'knowledge-intensive' Open-ended HAR algorithms

Personalised exercise recognition towards improved self-management of musculoskeletal disorders.

Musculoskeletal Disorders (MSD) have been the primary contributor to the global disease burden, with increased years lived with disability. Such chronic conditions require self-management, typically in the form of maintaining an active lifestyle while adhering to prescribed exercises. Today, exercise monitoring in fitness applications wholly relies on user input. Effective digital intervention for self-managing MSD should be capable of monitoring, recognising and assessing performance quality of exercises in real-time. Exercise Recognition (ExRec) is the machine learning problem that investigates the automation of exercise monitoring. Multiple challenges arise when implementing high performing ExRec algorithms for a wide range of exercises performed by people from different demographics. In this thesis, we explore three personalisation challenges. Different sensor combinations can be used to capture exercises, to improve usability and deployability in restricted settings. Accordingly, a recognition algorithm should be adaptable to different sensor combinations. To address this challenge, we investigate the best feature learners for individual sensors, and effective fusion methods that minimise the need for data and very deep architectures. We implement a modular hybrid attention fusion architecture that emphasises significant features and understates noisy features from multiple sensors for each exercise. Persons perform exercises differently when not supervised; they incorporate personal rhythms and nuances. Accordingly, a recognition algorithm should be able to adapt to different persons. To address the personalised recognition challenge, we investigate how to adapt learned models to new, unseen persons. Key to achieving effective personalisation is the ability to personalise with few data instances. Accordingly, we bring together personalisation methods and advances in meta-learning to introduce personalised meta-learning methodology. The resulting personalised meta-learners are learning to adapt to new end-users with only few data instances. It is infeasible to design algorithms to recognise all expected exercises a physiotherapist would prescribe. Accordingly, the ability to integrate new exercises after deployment is another challenge in ExRec. The challenge of adapting to unseen exercises is known as open-ended recognition. We extend the personalised meta-learning methodology to the open-ended domain, such that an end-user can introduce a new exercise to the model with only a few data instances. Finally, we address the lack of publicly available data and collaborate with health science researchers to curate a heterogeneous multi-modal physiotherapy exercise dataset, MEx. We conduct comprehensive evaluations of the proposed methods using MEx to demonstrate that our methods successfully address the three ExRec challenges. We also show that our contributions are not restricted to the domain of ExRec, but are applicable in a wide range of activity recognition tasks by extending the evaluation to other human activity recognition domains

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

Innovating Pedagogy 2015: Open University Innovation Report 4

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation

Evaluating the transferability of personalised exercise recognition models.

Exercise Recognition (ExR) is relevant in many high impact domains, from health care to recreational activities to sports sciences. Like Human Activity Recognition (HAR), ExR faces many challenges when deployed in the real-world. For instance, typical lab performances of Machine Learning models, are hard to replicate, due to differences in personal nuances, traits and ambulatory rhythms. Thus effective transferability of a trained ExR model, depends on its ability to adapt and personalise to new users or user groups. This calls for new experimental design strategies that are also person-aware, and able to organise train and test data differently from standard ML practice. Speciffically, we look at person-agnostic and person-aware methods of train-test data creation, and compare them to identify best practices on a comparative study of personalised ExR model transfer. Our findings show that ExR when compared to results with other HAR tasks, to be a far more challenging personalisation problem and also confirms the utility of metric learning algorithms for personalised model transfer

Learning Dimensions: Lessons from Field Studies

In this paper, we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies involving a range of age groups to explore how programming tasks could best be framed to motivate learners. Our empirical findings from these four studies, described here, contributed to the design of a set of programming "Learning Dimensions" (LDs). The LDs provide educators with insights to support key design decisions for the creation of engaging programming learning experiences. This paper describes the background to the identification of these LDs and how they could address the design and delivery of highly engaging programming learning tasks. A web application has been authored to support educators in the application of the LDs to their lesson design

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