Efficient Deep Clustering of Human Activities and How to Improve Evaluation

There has been much recent research on human activity re\-cog\-ni\-tion (HAR), due to the proliferation of wearable sensors in watches and phones, and the advances of deep learning methods, which avoid the need to manually extract features from raw sensor signals. A significant disadvantage of deep learning applied to HAR is the need for manually labelled training data, which is especially difficult to obtain for HAR datasets. Progress is starting to be made in the unsupervised setting, in the form of deep HAR clustering models, which can assign labels to data without having been given any labels to train on, but there are problems with evaluating deep HAR clustering models, which makes assessing the field and devising new methods difficult. In this paper, we highlight several distinct problems with how deep HAR clustering models are evaluated, describing these problems in detail and conducting careful experiments to explicate the effect that they can have on results. We then discuss solutions to these problems, and suggest standard evaluation settings for future deep HAR clustering models. Additionally, we present a new deep clustering model for HAR. When tested under our proposed settings, our model performs better than (or on par with) existing models, while also being more efficient and better able to scale to more complex datasets by avoiding the need for an autoencoder

Activity-Based Recommendations for Demand Response in Smart Sustainable Buildings

The energy consumption of private households amounts to approximately 30% of the total global energy consumption, causing a large share of the CO2 emissions through energy production. An intelligent demand response via load shifting increases the energy efficiency of residential buildings by nudging residents to change their energy consumption behavior. This paper introduces an activity prediction-based framework for the utility-based context-aware multi-agent recommendation system that generates an activity shifting schedule for a 24-hour time horizon to either focus on CO2 emissions or energy cost savings. In particular, we design and implement an Activity Agent that uses hourly energy consumption data. It does not require further sensorial data or activity labels which reduces implementation costs and the need for extensive user input. Moreover, the system enhances the utility option of saving energy costs by saving CO2 emissions and provides the possibility to focus on both dimensions. The empirical results show that while setting the focus on CO2 emissions savings, the system provides an average of 12% of emissions savings and 7% of cost savings. When focusing on energy cost savings, 20% of energy costs and 6% of emissions savings are possible for the studied households in case of accepting all recommendations. Recommending an activity schedule, the system uses the same terms residents describe their domestic life. Therefore, recommendations can be more easily integrated into daily life supporting the acceptance of the system in a long-term perspective

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions

Activity Recognition with Evolving Data Streams: A Review

Activity recognition aims to provide accurate and opportune information on people’s activities by leveraging sensory data available in today’s sensory rich environments. Nowadays, activity recognition has become an emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or ambient sensors. This paper surveys the two overlapped areas of research of activity recognition and data stream mining. The perspective of this paper is to review the adaptation capabilities of activity recognition techniques in streaming environment. Categories of techniques are identified based on different features in both data streams and activity recognition. The pros and cons of the algorithms in each category are analysed and the possible directions of future research are indicated

Context Awareness in Swarm Systems

Recent swarms of Uncrewed Systems (UxS) require substantial human input to support their operation. The little 'intelligence' on these platforms limits their potential value and increases their overall cost. Artificial Intelligence (AI) solutions are needed to allow a single human to guide swarms of larger sizes. Shepherding is a bio-inspired swarm guidance approach with one or a few sheepdogs guiding a larger number of sheep. By designing AI-agents playing the role of sheepdogs, humans can guide the swarm by using these AI agents in the same manner that a farmer uses biological sheepdogs to muster sheep. A context-aware AI-sheepdog offers human operators a smarter command and control system. It overcomes the current limiting assumption in the literature of swarm homogeneity to manage heterogeneous swarms and allows the AI agents to better team with human operators. This thesis aims to demonstrate the use of an ontology-guided architecture to deliver enhanced contextual awareness for swarm control agents. The proposed architecture increases the contextual awareness of AI-sheepdogs to improve swarm guidance and control, enabling individual and collective UxS to characterise and respond to ambiguous swarm behavioural patterns. The architecture, associated methods, and algorithms advance the swarm literature by allowing improved contextual awareness to guide heterogeneous swarms. Metrics and methods are developed to identify the sources of influence in the swarm, recognise and discriminate the behavioural traits of heterogeneous influencing agents, and design AI algorithms to recognise activities and behaviours. The proposed contributions will enable the next generation of UxS with higher levels of autonomy to generate more effective Human-Swarm Teams (HSTs)