Enabling the Evaluation of Driver Physiology Via Vehicle Dynamics

Driving is a daily routine for many individuals across the globe. This paper presents the configuration and methodologies used to transform a vehicle into a connected ecosystem capable of assessing driver physiology. We integrated an array of commercial sensors from the automotive and digital health sectors along with driver inputs from the vehicle itself. This amalgamation of sensors allows for meticulous recording of the external conditions and driving maneuvers. These data streams are processed to extract key parameters, providing insights into driver behavior in relation to their external environment and illuminating vital physiological responses. This innovative driver evaluation system holds the potential to amplify road safety. Moreover, when paired with data from conventional health settings, it may enhance early detection of health-related complications.Comment: 7 pages, 11 figures, 2023 IEEE International Conference on Digital Health (ICDH

Designing the club of the future with data: A case study on collaboration of creative industries

This paper reflects on the development of a multi-sensory clubbing experience which was deployed during a two-day event within the context of the Amsterdam Dance Event in October 2016 in Amsterdam. We present how the entire experience was developed end-to-end and deployed at the event through the collaboration of several project partners from industries such as art and design, music, food, technology and research. Central to the system are smart textiles, namely wristbands equipped with Bluetooth LE sensors which were used to sense people attending the dance event. We describe the components of the system, the development process, the collaboration between the involved entities and the event itself. To conclude the paper, we highlight insights gained from conducting a real world research deployment across many collaborators and stakeholders with different backgrounds

Alcan Aluminium Limited v. Franchise Tax Board: State Unitary Apportionment of Foreign Parent Income Taxation Will Have to Go to State Court

Viewers using HTTP Adaptive Streaming (HAS) without sufficient bandwidth undergo frequent quality switches that hinder their watching experience. This situation, known as instability, is produced when HAS players are unable to accurately estimate the available bandwidth. Moreover, when several players stream over a bottleneck link, their individual adaptation techniques may result in an unfair share of the channel. These are two detrimental issues in HAS technology, which is otherwise very attractive. To overcome them, a group of solutions are proposed in the literature that can be classified as network-assisted HAS. Solving stability and fairness only in the player is difficult, because a player has a limited view of the network. Using information from network devices can help players in making better adaptation decisions. In this paper we describe our implementation in the form of an HTTP prox

Adaptive Streaming: A subjective catalog to assess the performance of objective QoE metrics

Scalable streaming has emerged as a feasible solution to resolve users' heterogeneity problems. SVC is the technology that has served as the definitive impulse for the growth of streaming adaptive systems. Systems seek to improve layer switching efficiency from the network point of view but, with increasing importance, without jeopardizing user perceived video quality, i.e., QoE. We have performed extensive subjective experiments to corroborate the preference towards adaptive systems when compared to traditional non-adaptive systems. The resulting subjective scores are correlated with most relevant Full Reference (FR) objective metrics. We obtain an exponential relationship between human decisions and the same decisions expressed as a difference of objective metrics. A strong correlation with subjective scores validates objective metrics to be used as aid in the adaptive decision taking algorithms to improve overall systems performance. Results show that, among the evaluated objective metrics, PSNR is the metric that provide worse results in terms of reproducing the human decision

AI at the disco: Low sample frequency human activity recognition for night club experiences

Human activity recognition (HAR) has grown in popularity as sensors have become more ubiquitous. Beyond standard health applications, there exists a need for embedded low cost, low power, accurate activity sensing for entertainment experiences. We present a system and method of using a deep neural net for HAR using low-cost accelerometer-only sensor running at 0.8Hz to preserve battery power. Despite these limitations, we demonstrate an accuracy at 94.79% over 6 activity classes with an order of magnitude less data. This sensing system conserves power further by using a connectionless reading - -embedding accelerometer data in the Bluetooth Low Energy broadcast packet - -which can deliver over a year of human-activity recognition data on a single coin cell battery. Finally, we discuss the integration of our HAR system in a smart-fashion wearable for a live two night deployment in an instrumented night club

The club of the future: Participatory clubbing experiences

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The co-creation space: Supporting asynchronous artistic co-creation dynamics

Artistic co-creation empowers communities to shape their narratives, however HCI research does not support this multifaceted discussion and reflection process. In the context of community opera, we consider how to support co-creation through the design, implementation, and initial evaluation of the Co-Creation Space (CCS) to help community artists 1) generate raw artistic ideas, and 2) discuss and reflect on the shared meaning of those ideas. This work describes our user-centered process to gather requirements and design the tool, and validates its' usability with 6 community opera participants. Our findings support the value of our tool for group discussion and personal reflection during the creative process