Loneliness is a growing public health concern, particularly among older adults, and has been linked to adverse physical and mental health outcomes. This study presents a machine learning approach to predict levels of loneliness using behavioural and emotional data collected from 124 participants through a mobile phone application over a 71-day period. The dataset includes 27 features derived from self-logged information such as wellbeing scores, mood fluctuations, and time spent in various home locations. Feature selection was applied to identify the most discriminative indicators, with classification and regression models evaluated using both Support Vector Machine (SVM), and Random Forest (RF). We applied feature selection to identify the most discriminative indicators and evaluated both Support Vector Machine (SVM) and Random Forest (RF) models for classification and regression. The highest classification accuracy—69.19% on a 7-point loneliness scale—was achieved using a five-fold SVM with the top 13 features. In the regression task, the best performance was observed using 26 features, resulting in a minimum Mean Squared Error (MSE) of 0.6752. These findings indicate that a selected subset of behavioural and emotional features can offer a meaningful estimation of loneliness levels. This has potential to inform the design of real-time, personalised digital tools aimed at identifying and supporting individuals at risk of loneliness.

There are many design techniques to support the co-design of tangible technologies. However, few of these design methods allow the involvement of users at scale and across diverse geographic locations. While popular in psychology, the story completion method (SCM) has only recently started to be adopted within the HCI community. We explore whether SCM can generate meaningful design insights from large, diverse study populations for the design of Tangible User Interfaces (TUIs). Based on the results of two questionnaire studies using SCM, we conclude that the method can be used to generate meaningful design insights. Drawing on a systematic review of 870 TUI papers, we then contextualise the strengths and weaknesses of SCM against commonly used design methods, before reflecting on our experience of using the method across two distinct domains. We discuss the advantages of the method (particularly in terms of the scale and diversity of participation) and the challenges (particularly around constructing meaningful story stems, and developing the correct level of scaffolding to support creativity). We conclude that SCM is particularly suitable to be used in the early stages of the design process to understand the socio-cultural context of deployment.

The global population is ageing, leading to shifts in healthcare needs. Home healthcare monitoring systems currently focus on physical health, but there is an increasing recognition that psychological wellbeing also needs support. This raises the question of how to design devices that older adults can interact with to log their feelings. We designed three tangible prototypes, based on existing paper-based scales of affect. We report findings from a lab study in which participants used the prototypes to log the emotion from standardised emotional vignettes. We found that the prototypes allowed participants to accurately record identified emotions in a reasonable time. Our participants expressed a perceived need to record emotions, either to share with family/carers or for self-reflection. We conclude that our work demonstrates the potential for in-home tangible devices for recording the emotions of older adults to support wellbeing.

The world is facing an ageing population phenomenon, coupled with health and social problems, which affect older people’s ability to live independently. This situation challenges the viability of health and social services. Smart home technology can play a significant role in easing the pressure on caregivers, as well as reduce the financial costs of health and social services. Activity of Daily Living (ADL) recognition is an essential step to translate sensor data into activities at high semantic levels. Supervised Machine Learning (ML) algorithms are the most commonly used techniques for this application. However, a common problem is a lack of availability of enough annotated data to train these algorithms. Collecting annotated data is expensive, time consuming, and may violate people’s privacy. Intra- and inter-personal variation in performing complex activities is another challenge for an ML-based activity recognition approach. In this paper, a multi-layered knowledge-based architecture for recognising ADL in real-time is proposed. At the first stage, sensor data is pre-processed; events that describe changes in the environment are detected at the second stage, in which the sequence of events is used to recognise more semantically complex activities at the third stage. A new ADL ontology is proposed to model the knowledge related to the sensor platform and the targeted activities as the previously proposed ontologies were either designed to deal with specific sensor data, or they ignored the context environment information which is important in recognising complex activities.

Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.