The majority of activity recognition systems in wearable computing rely on a set of statistical measures, such as means and moments, extracted from short frames of continuous sensor measurements to perform recognition. These features implicitly quantify the distribution of data observed in each frame. However, feature selection remains challenging and labour intensive, rendering a more generic method to quantify distributions in accelerometer data much desired. In this paper we present the ECDF representation, a novel approach to preserve characteristics of arbitrary distributions for feature extraction, which is particularly suitable for embedded applications. In extensive experiments on six publicly available datasets we demonstrate that it outperforms common approaches to feature extraction across a wide variety of tasks.

In this forum we celebrate research that helps to successfully bring the benefits of computing technologies to children, older adults, people with disabilities, and other populations that are often ignored in the design of mass-marketed products. --- Juan Pablo Hourcade, Editor

We then operationalised these qualities as a set of design goals-Assured Anonymity, Constructive Moderation, Adequate Slowness and Controlled Access-in the design and development of a secure anonymous employee voice system. Our novel take on the Enterprise Social Network aims to foster good citizenship whilst also promoting frank yet constructive discussion. We reflect on a two-week deployment of our system, the diverse range of candid discussions that emerged around important workplace issues and the potential for change within the host organization. We conclude by reflecting on the ways in which our approach shaped discourse and supported the creation of a trusted environment for employee voice.

Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

This paper explores 'A to B' routing tools designed to chart accessible routes for wheelchair users. We develop and present a novel measurement framework based upon cost-benefit analysis in order to evaluate the real-world utility of routing systems for wheelchair users. Using this framework, we compare proposed routes generated by accessibility tools with the pedestrian routes generated by Google Maps by means of conducting expert assessments of the situation on the ground. Relative to tools aimed at pedestrians, we find that these tools are not significantly more likely to produce an accessible route, and more often than not, they present longer routes that arise from imaginary barriers that do not exist in the real world. This analysis indicates how future routing tools for wheelchair users should be designed to ensure that they genuinely ameliorate the effects of accessibility barriers in the built environment.

Extraversion and neuroticism scores were related to the accident and violation records of a representative sample of 113 young male drivers. A significant positive correlation was found between extraversion and accidents, particularly non-intersection accidents, A similar significant positive correlation between extraversion and violations may have been confounded with exposure. Neuroticism appeared to be related to accidents only in extreme subjects and when considered simultaneously with extraversion.

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