With the rise of smart systems, ubiquitous computing and cyber-physical systems, information-intensiveness of products increases and users become challenged—possibly even overloaded—with expanding options and possible interactions. The number of possible variations of user-operation sequences can rapidly escalate and for designers it becomes difficult to foresee all possible outcomes,which might include unacceptable performance, failure, and even fatalities.With the objective to reduce the risk of unwanted cognitive effects and to realize a more symbiotic relationship between users and systems, we showhowtwo model-based theories from cognitive science, i.e., cognitive architectures and mental models, can be deployed in the design of these systems.We argue that the deployment of such models requires a transdisciplinary approach in which designers intensively cooperate with cognitive scientists and end users.@en

This paper presents an embarking and disembarking process for the hyperloop, a future high-speed transportation of passengers and goods in tubes. A concept of the (dis)embarking process has been designed and tested with two experiments. The first experiment was performed to compare the new concept to one that is more similar to the current embarking setup of trains on the aspects of efficiency and experience. Participants were asked to (dis)embark in the test settings that simulate the new concept and the conventional situation with luggage. As a result, new passenger flow saves 40% of the time for vehicles to stay on the platform. Follow-up questionnaires and interviews with the participants show that the proposed passenger flow gives a better experience in terms of efficiency, seamlessness and friendliness. The new solution increases the number of doors, which increases the manufacturing complexity and the chance of failure. Narrowing the door size minimizes this effect. Subsequently, a second experiment has been carried out to study the influence of door width on (dis)embarking efficiency and passenger experience following a similar method. It turns out that narrowing the door width does not noticeably influence the embarking time, but the disembarking time does increase. Interviews show that half of the participants sense a negative experience with narrower doors, while the other half do not notice a difference.

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The real-life use of a product is often hard to foresee during its development. Fortunately, today's connective products offer the opportunity to collect information about user actions, which enables companies to investigate the actual use for the benefit of next-generation products. A promising application opportunity is to input the information to engineering simulations and increase their realism to (i) reveal how use-related phenomena influence product performance and (ii) to evaluate design variations on how they succeed in coping with real users and their behaviors. In this article, we explore time-stamped usage data from connected fridge-freezers by investigating energy losses caused by door openings and by evaluating control-related design variations aimed at mitigating these effects. By using a fast-executing simulation setup, we could simulate much faster than real time and investigate usage over a longer time. We showed that a simple, single-cycle load pattern based on aggregated input data can be simulated even faster but only produce rough estimates of the outcomes. Our model was devised to explore application potential rather than producing the most accurate predictions. Subject to this reservation, our outcomes indicate that door openings do not affect energy consumption as much as some literature suggests. Through what-if studies we could evaluate three design variations and nevertheless point out that particular solution elements resulted in more energy-efficient ways of dealing with door openings. Based on our findings, we discuss possible impacts on product design practice for companies seeking to collect and exploit usage data from connected products in combination with simulations.

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By applying data analytics to product usage information (PUI) from combinations of different channels, companies can get a more complete picture of their products' and services' Mid-Of-Life. All data, which is gathered within the usage phase of a product and which relates to a more comprehensive understanding of the usability of the product itself, can become valuable input. Nevertheless, an efficient use of such knowledge requires to setup related analysis capabilities enabling users not only to visualize relevant data, but providing development related knowledge e.g. to predict product behaviours not yet reflected by initial requirements. The paper elaborates on explorations to support product development with analytics to improve anticipation of future usage of products and related services. The discussed descriptive, predictive and prescriptive analytics in given research context share the idea and overarching process of getting knowledge out of PUI data. By implementation of corresponding features into an open software platform, the application of advanced analytics for white goods product development has been explored as a reference scenario for PUI exploitation.

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Today’s connected products increasingly allow us to collect and analyze information on how they are actually used. An engineering activity where usage data can prove particularly useful, and be converted to actionable engineering knowledge, is simulation: user behavior is often hard to model, and collected data representing real user interactions as simulation input can increase realism of simulations. This is especially useful for (i) investigating use-related phenomena that influence the product’s performance and (ii) evaluating design variations on how they succeed in coping with real users and their behaviors. In this paper we explored time-stamped usage data from connected refrigerators, investigating the influence of door openings on energy consumption and evaluating control-related design variations envisaged to mitigate negative effects of door openings. We used a fast-executing simulation setup that allowed us to simulate much faster than real time and investigate usage over a longer time. According to our first outcomes, door openings do not affect energy consumption as much as some literature suggests. Through what-if studies we could evaluate three design variations and nevertheless point out that particular solution elements resulted in better ways of dealing with door openings in terms of energy consumption.@en

There are many real-life processes whose smart control requires processing context information. Though processing time-varied context information is addressed in the literature, domain-independent solutions for reasoning about time-varying process scenarios are scarce. This paper proposes a method for dynamic context computation concerning spatial and attributive information. Context is interpreted as a body of information dynamically created by a pattern of entities and relationships over a history of situations. Time is conceived as a causative force capable of changing situations and acting on people and objects. The invariant and variant spatial information is captured by a two-dimensional spatial feature representation matrix (SFR-matrix). The time-dependent changes in the context information are computed based on a dynamic context information (DCI) management hyper-matrix. This humble but powerful representation lends itself to a quasi-real time computing and is able to provide information about foreseeable happenings over multiple situations. Based on this, the reasoning mechanism proposed in this paper is able to provide informative instructions for users who needed to be informed in a dynamically changing situation. This paper uses the practical case of evacuation of a building in fire both as an explorative case for conceptualization of the functionality of the computational mechanism and as a demonstrative and testing application. Our intention is to use the dynamic context computation mechanism as a kernel component of a reasoning platform for informing cyber-physical systems (I-CPSs). Our future research will address the issue of context information management for multiple interrelated spaces.@en

A product’s value is increasingly determined by the services supporting it. Complex Product-Service Systems (PSS) – combinations of services and products – are in demand. Product usage information (PUI), that is, information about how an individual product is used, is rapidly becoming a valuable asset to industry to help inform service offers throughout the product lifecycle. Conventionally, information about how a product is used is gathered in customer relations and MRO (maintenance, repair and overhaul) processes via channels such as repair logs, call centres or helpdesks. Today, new channels of PUI are emerging from the digitalization of products, the Internet of Things, Cyber-Physical Systems, Internet PUI sources and social media. However, there is a lack of a methodological approach which companies use to select the right PUI source for the design of services, and vice-versa, to understand which value-added services can be offered using information already generated by sensors embedded into their products or available on the Internet or in social media. This contribution is intended to show an avenue of research towards such a methodology.@en

Speeding because of time pressure is a leading contributor to traffic accidents. Previous research indicates that people respond to time pressure through increased physiological activity and by adapting their task strategy in order to mitigate task demands. In the present driving simulator study, we investigated effects of time pressure on measures of eye movement, pupil diameter, cardiovascular and respiratory activity, driving performance, vehicle control, limb movement, head position, and self-reported state. Based on existing theories of human behavior under time pressure, we distinguished three categories of results: (1) driving speed, (2) physiological measures, and (3) driving strategies. Fifty-four participants drove a 6.9-km urban track with overtaking, car following, and intersection scenarios, first with no time pressure (NTP) and subsequently with time pressure (TP) induced by a time constraint and a virtual passenger urging to hurry up. The results showed that under TP in comparison to NTP, participants (1) drove significantly faster, an effect that was also reflected in auxiliary measures such as maximum brake position, throttle activity, and lane keeping precision, (2) exhibited increased physiological activity, such as increased heart rate, increased respiration rate, increased pupil diameter, and reduced blink rate, and (3) adopted scenario-specific strategies for effective task completion, such as driving to the left of the lane during car following, and early visual lookout when approaching intersections. The effects of TP relative to NTP were generally large and statistically significant. However, individual differences in absolute values were large. Hence, we recommend that real-time driver feedback technologies use relative instead of absolute criteria for assessing the driver’s state.@en

Brain signal and eye tracking technology have been intensively applied in cognitive science in order to study reading, listening and learning processes. Though promising results have been found in laboratory experiments, there are no smart reading aids that are capable to estimate difficulty during normal reading. This paper presents a new concept that aims to tackle this challenge. Based on a literature study and an experiment, we have identified several indicators for characterizing word processing difficulty by interpreting electroencelography (EEG) and electrooculography (EOG) signals. We have defined a computational model based on fuzzy set theory, which estimates the probability of word processing and comprehension difficulty during normal reading. The paper also presents a concept and functional prototype of a smart reading aid, which is used to demonstrate the feasibility of our solution. The results of our research proves that it is possible to implement a smart reading aid that is capable to detect reading difficulty in real time. We show that the most reliable indicators are related to eye movement (i.e. fixation and regression), while brain signals are less dependable sources for indicating word processing difficulty during continuous reading.@en

Now that all kinds of products are increasingly getting connected to the Internet, it is expected that it will become easier to collect data on how they are actually used during the middle-of-life stage of their product lifecycles. At the same time, a growing number of data analytics technologies offers opportunities to transform this data into actionable knowledge. Over the years, such knowledge extracted from usage data has already become a reliable input for managing maintenance and related services, but other uses such as feedback to design – where product data management systems have started to offer support for data collection practices – and providing advice to end users are now also being considered. Most data from sensors and other product-embedded information devices are collected in batches and analyzed retrospectively. In order for companies to further benefit from data collection in terms of efficacy and acceptance in society, two key challenges are (i) finding ways to effectively use data analytics techniques – which currently do not seem to be used to their full potential, and (ii) finding a good trade-off between respecting privacy and yet producing useful knowledge.@en

There are many real life processes whose smart control requires processing context information. Though the issue of processing varying context information has been addressed in the literature, domain independent solutions that can support reasoning and decision making according to time-varying process scenarios in multiple application fields are scarce. This paper proposes a method for dynamic context computation concerning spatial and attributive information. Context is interpreted as a body of information dynamically created by a pattern of entities and relationships over a history of situations. Time is conceived as a causative force capable of changing situations, and acting on people and objects. The invariant and variant spatial information is captured by a two-dimensional spatial feature representation matrix. The time-dependent changes in the context information are computed based on a dynamic context information management hyper-matrix. This humble but powerful representation lends itself to a quasi-real time computing and is able to provide information about foreseeable happenings over multiple situations. The paper uses the practical case of evacuation of a building in fire both as an explorative case for conceptualization of the functionality of the computational mechanism and as a demonstrative and testing application. Our intention is to use the dynamic context computation mechanism as a kernel component of a reasoning platform for informing cyber physical systems.@en

Informing cyber-physical systems (I-CPSs) are designed to accomplish sensing, reasoning and informing activities in dynamic context. In order to simplify and accelerate the design and implementation process of multiple context-aware ICPSs, we are developing an information sensing, computing and actuating (SCA) platform that can be used as a central module of these systems. This paper presents the concept of a SCA platform. The functionality of the platform includes development of context-dependent strategies to adapt the sensing, reasoning and informing behaviors of the platform to various dynamic contexts. There are four constituents of the platform: (1) a generic kernel, (2) built-in elements, (3) add-on components, and (4) system interfaces. The paper also discusses both the internal and external integration mechanism of the SCA platform, which can be customized according to the needs of specific I-CPS applications by extending the generic kernel with various functional built-in elements and add-on components. The feasibility and applicability of the platform have been tested through a case study: an indoor fire evacuation guiding system. The proposed platform provides a useful package of functionalities, alleviates the burden of developers, and speeds up the development of applications specific context-aware I-CPS.@en