Diversey BV, a major player of professional hygiene product manufacturing, is facing challenges with agile manufacturing of hygienic products with changeover process consuming most of production time. They are collaborating with the EU-Horizon 2020 COALA project to develop a cognitive intelligent assistant for the production line. They expect to standardize activities in the production line to reduce the gap in activity performance between experienced and novice operators. In order to set up the cognitive assistant, an operator location tracking system was needed to identify issue hotspots and sequence of activities in the production line. In this project, a suitable motion capture system was explored and deployed at Diversey Enschede 5L/ 10L production line. A literature study was performed to compare the state of the art motion capture and motion analysis methods. From the literature study results, the project decided to deploy a markerless motion capture method using Zed 2 camera.
The data collection method was tested at Enschede with Zed 2 camera which has in-built object tracking algorithms. The project applied an ethical approach to operator tracking, giving due respect to operators’ privacy concerns and anonymity. The Value Sensitive Design method was applied in this project to identify the stakeholders, their values, and the project’s future speculation. The data collection, storage and upload to cloud server was conducted using indefinitely running Python codes. The tracking was anonymized by allocating random identification numbers to denote objects and thereby, no personal data that can identify the operator were being stored. The data was captured and stored in spreadsheet format and processed using Python. The project concludes with the implementation of Z-Dash, an interactive tool that visualizes the data in various meaningful representations. Z-Dash offers graphs such as the Spaghetti chart for visualizing operator location and movements, Heat map of operator location concentration and Pareto chart that visualizes time and frequency of visited stations. The tool was evaluated with participants from Diversey to estimate the usability, interactivity and effectiveness for process improvement. The project proposes this tool for identifying the sequence of operator activities during events like changeover or stoppages, identifying issue hotspots and comparing best practices for similar events.

Last mile delivery is often seen as bottleneck for delivery service growth: it is the most costly and highest polluting segment of the delivery supply chain. By integrating delivery robots in society, last mile delivery can be automatize to create a more cost and time efficient and sustainable delivery service. This also applies to the grocery delivery service. However, no grocery delivery robot, that can operate on the sidewalk, is designed yet. How should such a grocery delivery robot look like? And how do we want the delivery robot to interact with the user?
This thesis provides an embodiment design for a grocery delivery robot, with the focus on the human-robot interaction at the doorstep. A desired human-robot interaction can be achieved by creating functional, ergonomic, intuitive, functionally looking, trustworthy, aesthetically pleasing and environmentally friendly design that will empower the customer to feel in control of the delivery process. To realize a functioning delivery robot, a prototype and design has been created according to a form follows function approach. A prototype of the embodiment design has been integrated with the Husky platform at the AMS Institute in Amsterdam to test and validate the human-robot interaction.
The design enables, a cool, temperature-controlled delivery with user-friendly hand-over. The user-friendly hand-over is envisioned by an automatic vertical crate hand-over movement, which mimics the current almost effortless interaction of retrieving crates from a grocery delivery service. This hand-over will to not only make the experience of retrieving groceries more joyful and ergonomic, but it will also enable a better future integration with entire delivery process. Last mile delivery will only be more efficient, sustainable, and able to offer customers more control of the delivery process, if the delivery robots are integrated within the entire delivery process.

This thesis describes the design and development of a conversational assistant (CA) for standardized issue descriptions in manufacturing. The CA was specifically targeted at the acquisition of data on standardized issue descriptions and at the same time reduce operator friction with the system. CAs can be used to handle and process repetitive data, such as gathering data at scale. For Diversey specifically, the conversational assistant can gather data on issues and in the future on root causes, solutions and best practices. At Diversey, agile manufacturing is applied to the production process. Due to the complexity of this approach, the production process has many stoppages. Context analysis at Diversey showed that operators primarily resort to their own intuition and experience to resolve stoppages. Evaluations with operators showed that the system for data acquisition on stoppages currently in place, is an inconvenience and a time sink. The data that was captured with the system was often ambiguous, incomplete and non-descriptive. However, such data can be of significant value to the company as it can be used for process improvement of the production line. A Wizard-of-Oz-like experiment was conducted with the operators where the researcher role-played a CA for acquiring issue description, which resulted in 71 issue capturing dialogues. Even though these dialogues were filled with implicit and explicit knowledge, without proper understanding of the context, the dialogues were too chaotic and unstructured for a machine learning (ML) algorithm to understand and process. For a complete issue description, that a ML algorithm can process, several key entities were identified: machine location, machine component, machine component state, product component and product component state. Furthermore, several challenges were found related to the speech patterns of the operators. Two of these were tackled in the prototype: Usage of synonyms and pronouns. With the open-source framework Rasa, a prototype was developed for a CA that would capture product related issues through a form structure into a database. The form re-quires the following entities: machine location, product component and product component state. The form is used to guide the user to capture good, structured data, while al-lowing some flexibility in the input. With the synonym and pronoun handling features, some of this flexibility is realized.
A conversational flow test was conducted to test and improve the CA prototype. Although failing the accuracy requirements set for intents by 7.4% and for entities by 2%, it provided an indication that with some information on the functionality of the CA, the participants were able to correctly capture the set issue. With some training of the operators, it would be feasible to implement such a CA in a manufacturing environment. Through contextual filtering, the model can filter towards the input of the user, allowing for only context-specific issue to be captured. With a qualitative test it was concluded that this feature decreased the duration of issue capturing. The contextual filtering and issue description control provide feedback to the user whether correct information is captured.

Context & Problem Humans are entering the age of the 4th industrial revolution: improving factories by applying smart sensors, artificial intelligence, and other emerging technologies, such as AGVs: Autonomous Ground Vehicles. these are fully automated driverless vehicles that can transport goods and machinery. The newest generation AGVs moves efficiently and flexibly without guiding rails or fixed paths. All of these extra ingredients are causing the factory to become more complex and less transparent in the face of high demands for safety and efficiency. Humans and their robotic colleagues are in dire need of enhanced methods for information exchange. Augmented Reality (AR) is an excellent tool to provide this exchange because of its inert ability to curate visual information and untangle complexity. The goal is to improve situation awareness and safety in the factory. Analysis The Magna Steyr factory in Graz was visited so to better understand the context. Literature research provided the necessary insights into the state of the art of the smart factory and AGVs as well as the human factors involved. Design & iteration To work towards the design of a solution an explorative approach was first adopted by matching different AR methodologies to different roles within the factory. This created a matrix of possible solutions. The following idea was selected: to place a projector on top of the AGV to provide visual cues to the factory worker by projecting the intention of the robot on the factory floor. An iterative approach was then adopted to develop a solution that could be mounted on top of an AGV. Validation In order to validate the presumed positive effects of this design, a between-groups study was conducted. A questionnaire research was devised in which a test-group and a control-group were shown videos of an AGV approaching the participant. The test-group videos included projected arrows while the control-group videos included no indication of the direction the AGV would take. Multiple realistic scenarios were tested to measure the response of the participants. Apart from the response of the participants, the experienced task load and situation awareness were also measured. Results & conclusion It was concluded that the projection of arrows in front of an AGV improves the perceived safety of workers as well as their assessment of the robot's future actions. Participants that were shown the projected arrows had a far greater chance of executing the desired response toward the robot. Improvement with regards to the situation awareness was measured in some scenarios. Additional research and design opportunities are identified and presented in chapters 12 and 13. This project proposes a framework for future AR projects in the smart factory environment and also provides insights into the merits of using (spatial) augmented reality to facilitate communication between robots and people in the smart factory. It shows that the use of Spatial Augmented Reality can make factories safer and more efficient, paving the way for more industries to adopt AGV systems and take the next step toward the factory 4.0 paradigm.

The way an autonomous delivery vehicle behaves during an encounter with a pedestrian evokes an experience comprised of many short-term emotions, influences which behaviour that person will use in reaction to the robot. In many cases, for both the negative and positive experiences, behaviour emerges that results in time-consuming encounters. This time could be used for deliveries but in order to have a neutral interaction a behavioural change is required. Prior work by Desmet has mapped these ‘micro-emotions’ and described how they can be used to drive product design and shape product experience. As the sensing capacities and intelligence of our artefacts are increasing new opportunities arise. Could these micro-emotions also be used to shape the experience while a product is in use? We conducted a case study on an encounter between pedestrians and a small delivery robot. A clustering, based on people’s initial micro-emotions and experience, seemed to effectively capture how they would subsequently respond to the robot. We were able to design robot behaviour from these ‘emotion-clusters’ that helps to shape people’s response and experience according to achieve a time-efficient/neutral encounter. This concept opens up the exciting new design space of products that actively respond to micro-emotions.

This project is an exploration of potential applications of Conversational AI Agents (CAIA) for industrial maintenance. Specifically, it involves the scientific validation and development of a CAIA for a promising application: the automatic creation of information-rich maintenance reports by conversing with a technician while they perform industrial maintenance. The choice for this application was based on literature research, (in-situ) context analyses and a review of CAIA frameworks and design guidelines. These revealed that maintenance workers rely heavily on their own experience and intuition when solving problems but mechanisms for capturing and accessing this were non-existent. This knowledge is highly valuable and can represent a significant part of a company’s worth. Furthermore, maintenance technicians reported that the existing reporting mechanisms were a nuisance. Lastly, audits revealed that maintenance reports were frequently incomplete or of poor quality. In turn, CAIAs (Conversational AI Agents), have various affordances that make them well-suited to the context of industrial maintenance. They are (1) hands and gaze-free, (2) highly efficient (faster than writing or typing, facilitate multitasking and they provide faster access to specific information), (3) they can adapt to the skill level of the user and (4) impose a minimal cognitive load.
A between-subjects experiment with 24 participants, which involved changing a bicycle inner tube, was used to test three hypotheses regarding the potential value of the application. All three hypotheses compare using a CAIA for reporting whilst performing maintenance, as opposed to writing the reports on paper afterwards. They posited that using the CAIA would result in (1) reports of higher quality (more information relevant to the understanding of the task), (2) time saving, and (3) a lower perceived workload (NASA TLX). T-tests confirmed that all three hypotheses were true. These results indicate that using a CAIA for live-reporting has a clear value proposition for the industrial maintenance domain. Critically, it demonstrated that it could facilitate the capture of valuable “expert knowledge”. Future research could explore integrating multi-modal information capture (e.g. through smart-glasses), identify additional uses for the captured data (e.g. for prescriptive maintenance or providing tips to maintenance technicians) and improve the functionality and usability of the existing application. A prototype was built using the open-source frameworks, Rasa and Mycroft, to demonstrate the technical feasibility of the functional requirements. These requirements include (1) uttering “continuers” in response to the user describing their work, (2) tracking conversational context, (3) asking for clarifications when there is a lack of mutual understanding and (4) asking for status updates when the user is silent for more than a set amount of time. The main challenges for the future development of the prototype are (1) reducing the response time of the CAIA, (2) the accuracy of the intent classifier and entity extractor and (3) improving the handling of fragmented/lengthy user input. Some of the prototype’s features rely on inflexible, hard-coded logic, therefore, future work should collect more conversational data and explore the use of machine learning algorithms.

An autonomous guided vehicle can be used for the delivery of goods. To deliver these goods to your home, the mobile robot will be driving in pedestrian rich environments. In these environments the robot will need to socially navigate itself in a way which is both pleasant for the pedestrians and progressive for the robot. During its drive, the robots behavior should be adapted to the situation it finds itself in. The underlying theory for this is the Social, Technology and Service triangle. This triangle dictates the balance between three aspects. The technology aspect focuses on power consumption and efficiency. The service aspect is all about delivery time and location. The social aspect is all about minimizing social disruption and facilitating intuitive behavior. The behavior of pedestrians can be modelled with the Social Force Model. In this model the objects and pedestrians apply social forces onto each other which determine their movements. The robot can use this SFM for its own social navigation. Multi-Policy Decision Making (MPDM) can be added onto the Social Force Model. This allows for switching between three basic policies: go-solo, follow and stop. Through forward simulations the robot can predict and decide which of the three policies brings the most progress and the least social disruption. However, SFM-MPDM on its own does not cope well with passing and crossing; thus methods were designed to handle these situations. In passing the robot looks ahead and makes room, often keeping right. In crossing the robot slows down and deviates to cross behind the pedestrian. The Social Force Model, the Multi-Policy Decision Making and the passing and crossing all combined form the social navigational model. This model consist of the many different parameters which govern the behavior and is part of the overall computational mechanism. This mechanism takes in the environment through sensors such as LiDAR. It preprocesses these inputs and sends them to the model. The output of this model is what determines the locomotion and behavior of the robot. To find the behavior and its underlying parameters an Evolutionary Algorithm was employed.
The learnt behavioral parameter sets were tested with participants (n=42) and a physical ROSbot. They were asked to rate the robots behavioral performance on comfort, predictability and communication of intent. From these results, it became clear that the robots behavior influences the experience of the pedestrians, but it is unclear which parameter exactly influences which part of the behavior. There are certain expectations to what impact a parameter or group of parameters has, but at which times and to what effect this controls parts of the behavior is difficult to identify. Nevertheless a good foundation has been laid down for future projects through a social navigational model and a way to learn parameters for the many different situations an autonomous delivery robot can encounter. A recommendation is to improve the Evolutionary Algorithm in order to facilitate parameter learning which better matches the desired behavior in the STS-triangle.

Technology developments are making it possible to start automating the last-mile of parcel logistics. This raises the question of how these autonomous technologies will interact with humans. The goal of this project is to design a concept of a delivery robot for DHL, that maximizes acceptance by pedestrians.

Literature research and interviews showed that the appearance of the robot should be highly functional efficient to communicate what the function of the robot is and how it will behave. Cues that improve the understandability of the behavior should be emphasized in the design. Creating aesthetic associations between other logistics products and the robot makes it more familiar.

The level of in which the robot is perceived as a social emotional being should in be in line with peoples’ expectations and needs. How much anthropomorphization is applied on the robot, determines if the robot is seen as a machine, cute or creepy and forms expectations on how to use it.

The goal is to create a product which fulfills its function without disrupting regular pedestrian behavior and without eliciting negative reactions or sabotage from pedestrians. In order to achieve this, the interaction should be very low effort and intuitive. Pedestrians are not the customer and do not want to adapt their behavior dramatically to cope with tens of delivery robots on their sidewalk stroll. Intuitive interactions can be achieved by using the same way of interacting as with known entities, like other pedestrians or vehicles. Tests show that a design in which the behavior of the robot is modeled after standardized pedestrian behavior, could result in favorable interactions. In demanding situations, the robot will use a car-like blinking light, to communicate what its intentions are.

In user experience tests in virtual reality, small body cues from the robot were seen as an important communication tool on top of a predictable maneuvering. In the final design, the four wheels are made highly visible by a higher body and lights under the chassis. Suspension control allows the body of the robot to lean into corners and forward just before braking, to visually accentuate and communicate the impending action.

The sum of all the small cues and the path of the robot are intuitively understood by pedestrians and don’t need much attention while interacting. Pedestrians will feel in control, because the robot behaves predictable and it mingles in with the natural flow of pedestrians.

The behavior of pedestrians and robot are simulated based on the “social-forces model”. The outcome of the simulation is a first definition of desirable robot behavior and what parameters are needed to achieve this. It is found that the robot should have a less dynamic way of moving than pedestrians. It should steer slower and move with less speed changes. It should be very early in communicating its directions by already turning into that direction a few meters before encountering a pedestrian.

Although the social forces model is now used within simulations, it can also be implemented as the basic logic of DHL’s future delivery robot. However, more tests are required with live test subjects, to find definitive conclusions.

We present a novel DC motor driven soft robotic fish and optimization based on experimental, numerical and theoretical investigation into oscillating propulsion for primarily speed and secondarily efficiency. Our system outperforms the previously fastest soft robotic fish Zhong et al. 2017 by a significant margin of 27%, with speeds up to 0.85m/s. A simple wire-driven active body and soft compliant body were used to mimic highly efficient thunniform swimming. The efficient DC motor to drive the system decreases internal losses compared to other soft robotic oscillating propulsion systems which are driven by one or multiple servo motors. The DC motor driven design allows for swimming at higher frequencies. The current design has been tested up to 5.5 Hz but can potentially reach much higher frequencies.

The starting point of this graduation project is noises that nurses experience in the ICU (Intensive Care Unit). There is a defined medical symptom which is called “alarm fatigue” that refers to numb auditory senses, stress, low job satisfaction as a result of being exposed to excessive noise for the extended period, and it often leads to low job performances in the end. One report reveals that 65% of significant ICU incidents occur by not responding to alarms appropriately (Cho, Kim, Lee & Cho, 2016). Since the alarm is not a single source of noises in the ICU, this report first defines which sound group will be seen as a culprit of “noise fatigue” as an expanded concept of alarm fatigue in this project. The way this graduation deals with noise in the ICU is different from the conventional approach of providing a design intervention as a solution. In this project, the focus is rather how sound stimuli and stress level of nurses can be precisely captured from the perspective of nurses and be correlated altogether so that the new system can function as an investigation tool for further design intervention to improve the sound environment in the ICU. In this regard, the sound classification method using machine learning, heart rate tracking as a means of an objective stress level assessment, and emotion report as a subjective stress evaluation tool was studied. Furthermore, beacons, an indoor positioning detection device, was investigated and combined with the project concept, so that Cacophony Mapper can function as a mobile data collection platform with dynamic data summary and analysis. After the exploration of possible technology options for this project, the way of combining opted technologies was contemplated. As a result, two devices for heart rate tracking and sound collection, one mobile application for emotion report and data analysis, and a web platform which function as a hub for the data summary were developed as Cacophony Mapper prototype. In the evaluation phase, the reliability of the sound filter was tested by directly putting the WAV sound data and a dataset collected through a user test, separately. Two datasets were compared to find the significant impact of the environmental factors so that the external impact can be minimized by further optimization. Also, the usability of the overall system and the application was evaluated. It has been done to see the possibility of implementing the Cacophony Mapper system in a medical environment and find which aspect of usability should be improved for further design development.

Indoor cultivation by use of climate-cells provides a higher consistency, quality and more reliable method of producing fruits and vegetables in comparison to greenhouse cultivation. These aspects become increasingly important as we face the challenges brought by higher populations and climate change. However, currently the costs of indoor food production are far too high to compete on a commercial scale. In this report the costs and opportunities of indoor farming are analysed and used as basis for designing more costs effective implementations of climate-cells. Cost effective climate-cells can be achieved by producing more within the same system without compromising accessibility to the crops or making use of expensive automation and transportation solutions. This design project led to the development of such a system. The final concept enables a highly efficient method of producing crops on several layers while minimizing cost and complexity of transport and improving the longevity of expensive parts. Necessary further steps for the completion of this concept into an operational indoor climate-cell are left to the company Certhon whom supported this project.

This document details a lab study into bimanual synchronised haptics, followed by the design of a product that can be used in combination with the SenseGlove to provide their wearer with haptic feedback based on how they operate a steering mechanism in Virtual Reality. The Dutch Ministry of Defense approached SenseGlove B.V., a company specialised in haptic gloves, to develop a Proof of Principle of the potential of haptic gloves in combination with Virtual Reality as a replacement for their high fidelity combat vehicle simulators. This project was split into two parts, a kinematic arm that can block the user’s motions so a person can no longer more their physical hands through digital objects, and a tactile palm strap that provides haptic feedback to the palm of the user to convey the motion of a digital steering mechanism. The kinematic arm was designed internally, and the tactile palm strap was taken as the focus of this graduation project.

An explorative Literature Study was conducted to find the ideal locations for haptic feedback on the human palm, as well as their corresponding types. These feedback locations and types were evaluated using a method called Interaction Prototyping. Based on the results of this study, a Lab Study was set up to evaluate three types of haptic feedback on the palm of the user. For this Study, a prototype haptic interface was made that could provide all three types of haptic feedback.

The outcome of the Lab Study was inconclusive because of a low sample size and many confounding factors. While the results were unsuitable to base a feedback choice on, the confounding factors resulted in valuable Design Challenges. The choice to go for vibrotactile feedback was made based on other factors such as cost, ease of production and company preference. The design challenges formed the basis for the Ideation Phase.

Product Ideas were generated using various creative techniques, and a Morphological Chart was made to condense the ideas into three Product Concepts. One Concept inspired by competitive gloves, one Concept inspired by damping materials, and one Concept inspired by the fingers of the user. Through the aid of a Harris Profile, the choice was made to further embody the concept inspired by the fingers of the user: The Finger Folds.

The Finger Folds form a modular addition to the SenseGlove that can easily be connected and disconnected from the company’s flagship product. It consists of four modules that each provide haptic feedback to the ideal region on the palm of the user independently of one another. Each module is connected to a finger, which automatically aligns the product and keeps the haptic feedback consistent during use, no matter how extreme holds their fingers or folds their hand. Signal consistency is maintained through constant skin contact. The modules can easily be attached to or detached from a Hub that is located between the SenseGlove and the back of the user’s hand. This Hub handles the information communication between the SenseGlove, Virtual Reality and each of the modules.

This master thesis elaborates on the development of a assistive cycling ankle-foot orthosis for Charcot-Marie-Tooth patients. Study the influence of assistive torque on the kinematic analysis of the knee and ankle joints of a CMT patient on an ergometer bike.

This thesis describes what is relevant for interface design for tele-remote driving of heavy road vehicles. A design framework was created to describe what these relevant aspects are. With this design framework a design process is done.

The graduation project is carried out as a collaboration between the Delft University of Technology and Ultimaker. Ultimaker B.V. is a 3D printer company located in Geldermalsen, The Netherlands. Users of 3D printers are presented with a multitude of action possibilities. However, these action possibilities are not yet translated into easy to understand controls that match user intentions. The goal of this project is to design a means for the user to match the intended outcome with the printed result. Eight design opportunities for intent-based 3D printing approaches are defined based on desk research and user experiments. The desk research focuses on the material extrusion process, the 3D printing workflow, user groups, market trends and intent facilitation techniques. The user experiments analyse the role of user intent in the 3D printing workflow. The eight defined design opportunities are: goal-oriented print preparation, reducing the knowledge threshold, component level control, promotion of 3D printing affordances, management of 3D printers, user education, intent communication and feedback. Ideation is applied to these design opportunities to create three concepts, of which the highest scoring concept is further developed. The proposed design is a software plugin for the Cura engine based on product configuration systems used in retail. The software architecture consists of six modules that work together to analyse the model geometry, retrieve requirements and wishes based on the communicated intent, recommend profile selections and user actions, validate the print preparation process and guide the user towards ideal printer configurations and process parameters. By using the 3MF file format, models are separated into several components, which can be configured individually by means of ‘component profiles’. Component profiles are developed by Ultimaker, companies and individual users to present a subset of print settings that facilitate an intended 3D printed result. A software plugin for Cura 3.3.1 is developed as a prototype for testing the designed intent-based 3D printing approach. Due to the scope of this research, testing is limited to component-level manipulations and control of goal-oriented settings. From the user test, it can be concluded that print preparation using component profiles significantly improves user guidance and the educative experience of print preparation. Especially beginners are more confident that their configuration leads to the desired part qualities. The objective plugin reduces the feeling of control, but even expert users show confidence in the final result. Within the plugin users can better voice their intentions through selecting component profiles. These profiles present meaningful feedback to users that both guides and educates them. Of course the realized plugin has its limitations: it does not use intent information that can be included in the supplied model files, nor does it offer all the designed guiding features. Even though these limitations exist, this study represents a step forward in the journey from process-driven to intent-based 3D printing.

The main challenge of operators working with complex information systems is to gain sufficient situation awareness (SA). In this research project, we developed an analysis scheme to support a holistic study of SA. This analysis scheme was complemented with a method to process cognitive task analysis and observational research data to identify deficiencies of systems in supporting SA. This approach was applied in nautical traffic management practice. In total we identified 30 deficiencies, of which 23 were related to how the system interfaces support human information processing. The commonly applied user-centered design method was insufficient to overcome the identified deficiencies. User-centered design therefore was complemented with information engineering (IE) methods for analyzing relationships between information elements and for specifying UI design. Application of different IE methods resulted in the development of three UI concepts; a coherent, an integrated and a context-dependent adaptable UI. The generated UI concepts were tested using a nautical traffic management workplace simulator. Usability testing showed that the proposed IE approach had a positive effect on the effectiveness, efficiency and user satisfaction. Evaluation of the impact of the UI prototypes showed that the application of a graph theory based IE approach had positive effects on operators’ speed of gaining SA, speed of communication with priority stakeholders, and the likeliness that operators executed necessary actions in the required order. Application of semantic networks resulted in a UI which provided better support of answering questions of skippers related to future states of the traffic management environment.

This thesis concerns a research process followed by a design project, conducted at the Industrial Design Engineering department for the Design for Interaction master program. The project concerns the reframing of a work ow for web analytics users, in the form of a new system design for web analytics. The project is founded on four studies of the graduation company’s customers, their context, and use of analytics in the status quo. This is done through research activities such as interviews and observations and generative design methods. Next, the insights derived from this phase are taken as a starting point for ideation in the conceptualization phase. After conceptualization, a concept was formulated towards a design for a new analytics work ow, appropriate for the initially analyzed user and use context. This report ends with some recommendations with regards to the design and research activities.