Wheelchair users often face significant barriers to maintaining and adapting their chairs, from resource constraints to limited access to professional services. In response, many turn to social media platforms such as YouTube to share and learn practical knowledge. However, little is known about how wheelchair users document and exchange repair, maintenance, and customization practices online. To address this gap, we analyzed 290 YouTube videos alongside 800 sampled comments using thematic coding and statistical analysis. Our findings revealed diverse user needs, from enhancing mobility to expressing identity, which were addressed through a spectrum of DIY acts, such as accessorizing, bricolage, and major modifications. Engagement analysis further reveals how styling videos attract a broad audience, while custom-built “chair tours” become hubs of knowledge and solidarity. We reflect on using YouTube as a research source and call for a design approach grounded in solidarity that supports the full spectrum of DIY practices.

Adolescence is often associated with emotional upheaval and teens themselves value support with their emotions. HCI research on emotion regulation has focused on lab-based interventions for those with the greatest needs. This paper explores how adolescents use commercially available videogames in their daily environments and how these practices relate to emotion regulation. We conducted a 2-week diary and interview study with eleven teens asking them to reflect on their videogame practices and emotions. We deployed a multimodal diary to encourage authentic teen voice on factors not typically considered in intervention studies. Our findings indicate that teens use videogames to regulate their emotions and to recover from stress in diverse ways. These processes are often intertwined with adolescents’ social relationships and can be mediated through game affordances. We argue that traditional approaches to emotion regulation may be too individualistic to recognise or support the social dynamics that define teens’ emotional lives.

Mobile Crowd Sensing (MCS) has emerged a promising paradigm for large-scale data collection, which recruits truthful workers to enhance data quality, thereby maximizing the platform's profit. However, most existing work has the assumption that workers' trust remains stable once verified and truthful workers can consistently provide high-quality data. However, in real-world scenarios, there exist workers whose trust may change dynamically over time, which makes it difficult to ensure high-quality data collection due to the current lack of sustainable trust verification research, thereby damaging the platform's profit. To tackle the above challenge, we propose a novel Truthful Workers Recruitment with Sustainable Trust Verification (TWR-STV) scheme. The proposed TWR-STV scheme identifies the dynamic trust of workers and assesses the data value based on the quality of collected data, thereby selecting high-value workers to maximize the profit. First, we establish a dynamic trust model to capture the change in workers' trust. Second, we propose a stable and trustworthy worker identification method based on a two-layer sustainable data verification to identify workers' trust and the stability of workers' trust. Third, a multi-armed bandit-based stable and trustworthy worker recruitment scheme is proposed, in which high-value workers are recruited to increase platform profit and trustworthy workers who can be effectively verified are prioritized for recruitment to maintain sustainable trust verification. Furthermore, the proposed TWR-STV scheme takes into account the decay effect of time and trust on data value, which is more practical. Finally, we theoretically analyze the regret bound of TWR-STV and conduct experiments on a real dataset. Experimental results show that our proposed solution is superior to existing solutions in terms of total profit.

While AI chatbots have been proposed to support wheelchair provision services in low- and middle-income countries (LMICs), the perception of physical therapists regarding how they could be integrated into their service workflow remains unclear. We conducted semi-structured interviews with 11 professionals from Africa and South Asia, using two design probes to investigate the potential and limitations of using chatbots in their everyday wheelchair assessment services. Our findings revealed 13 tensions that arise when the envisioned chatbot use misaligns with three interconnected domains - professional values, practice structures, and contextual readiness, such as conflicts in professional autonomy, evolving responsibilities, and confidence in AI. To guide more situated chatbot design, we proposed a tension-informed design framework that centers professional practice and surfaces tensions as opportunities rather than barriers. We discuss how introducing chatbots in LMICs should aim to amplify professionals’ capacity and align with the nature of assistive technology services.

Flexible working, including in resource-constrained settings such as long-haul flights, poses considerable challenges. While research in mixed reality (MR) offers the potential for enhancing experiences of working on airplanes (WoA), what input modalities and interactions might be suitable for such workspaces is under-explored. To address this gap, we created four design probes and demonstrated their interactions within MR for WoA using a branching decision-based interactive video. 24 participants engaged in the think-aloud study with the video where they selected which probe to watch based on personal preference and order. The follow-up interview further solidified their general preferences for intuitive and friction-free inputs, revealed strong concerns about reliability, discreetness, and portability, and showed high enthusiasm for interactive video. Based on these findings, we contribute a context-driven design space of input modalities for WoA in MR, extending the understanding of design opportunities for inputs with MR in confined spaces.

To tackle the lack of wheelchair service information and training in low and middle-income countries (LMICs), we deployed Wheelpedia, a WhatsApp chatbot powered by a large language model (LLM) as a design probe for 2 months to concretely explore how it can support wheelchair users and professionals in Nigeria and Kenya. Through 18 semi-structured interviews and analysis of 471 messages, we focused on not only Wheelpedia's acceptability and usability but also how users orient themselves with the probe, integrate its information, and manage trust with it. The findings revealed participants' overwhelming enthusiasm towards the chatbot's potential in education, fostering empowerment, and reducing social stigma. We discuss challenges like users' difficulty in formulating questions, unfamiliarity with the concept of chatbots, and requests for image output. This paper contributes valuable insights into the design implications and research opportunities for deploying LLM chatbots in low-resourced settings with complex accessibility needs.

Current approaches to designing technologies for stroke rehabilitation at home show great promise using either mindfulness-based interventions or embodied tangible interactions. However, there is an untapped potential in integrating these approaches and a lack of understanding of how to embody aspects of mindfulness in tangible interactions for stroke rehabilitation. We report the first explicit effort to explore this dimension by conducting semi-structured interviews and co-design sessions involving four physiotherapists and four mindfulness experts. The major themes ‘Awareness – The essence of mindfulness’ and ‘Tactile sensations – A pathway to mindfulness’ point us towards new ways to embody mindfulness in tangible interactions to address stroke rehabilitation challenges. This work introduces a novel approach to designing technology called ‘Mindfulness-based Embodied Tangible Interactions’ (MBETI). We present five key design principles such as 'Design to support mindful awareness’ and ‘Design for Comfort’ while discussing the future research opportunities of assistive technologies for stroke rehabilitation.

Mobile Crowd Sensing (MCS) has emerged as a novel paradigm in massive data collection, which leverages many individual mobile devices (called workers) to collect data. MCS platform utilizes the collected data to construct various services for service requesters, thus obtaining profit based on the data values contributed by workers. However, untrustworthy data would greatly reduce the data value, leading to a decline in platform profit, so it is crucial for the platform to recruit high-trust workers and collect truthful data, thereby providing high-quality service and obtaining high profit. To address this problem, we propose a Maximize Profit Scheme, called MPS, for MCS platforms, which consider that the data value declines as data trust decreases and discounts over time. MPS scheme is the first work that systematically addresses the impact of untruthful data on the platform profit, which is not well addressed in previous research. First, we utilize historical data of trusted workers as truthful data to identify the truth of data, which is a low-cost method. Then, a trust-discounting and time-discounting value model is proposed, which is more practical than previous methods. Based on the proposed value model, we propose a novel worker recruitment strategy combined with a trust-related and time-dependent reward threshold, which prioritizes workers with high trust and low latency, thereby promoting the data value of workers and maximizing the platform's profit. By comparing the MPS with existing schemes, the experimental results show that our MPS can achieve better performance in terms of total profit.

Large number of Internet of Things (IoT) applications with intelligent sensing devices (ISDs) are combined with cloud computing to collect and process data more efficiently. However, ISDs are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud, causing damage to IoT applications. Therefore, it is a critical issue to design an effective mechanism to ensure the security of data collection in cloud computing. In this article, a cloud-assisted reliable trust computing (CRTC) scheme is proposed to identify the trust of ISDs at low cost, providing high-quality data for IoT applications. The CRTC scheme mainly includes the following parts: First, a reliable approach of obtaining the real data of ISDs at a low cost is proposed to identify the trust of ISDs. In the proposed method, ISDs fits the forwarding packets information to form inspection information (II) with a small amount of data and then sends II to inspection nodes, effectively reducing the cost of routing II. Second, an effective method of trust computing is given to evaluate the trustworthiness of ISDs based on the data and II collected by unmanned aerial vehicles (UAV). Then, the aggregators are selected from high-trust ISDs to ensure secure data collection. Third, to obtain more reliable trust at lower cost, a trajectory optimization algorithm for UAV is proposed to collect as much II as possible and reduce the moving distance. Theoretical analysis and experimental results show that the proposed CRTC scheme is superior to previous strategies in terms of the success rate of data collection, the speed of identifying the trust of ISDs, and the UAV's trajectory distance.