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Data Analytics for Smart Product Development
- 1. Data Analytics For Smart Product Development Maikel van Eck
- 6. User Goal & Task Analysis SLIDE 5 • Define the product context • Who will use the product? • Where will the product be used? • What will the user do with the product? • What are the elements of the interaction? • Understanding the context of use • User goals reflect what user groups want to achieve • Tasks reflect how user goals are achieved • Use scenarios combine tasks to reach goals
- 11. User Goal & Task Analysis SLIDE 10 • Intervention Scenario • T1: Prepare patient & system • T2: Search area of interest • T3: Perform 3D acquisition • T4: Perform treatment • T5: Assess treatment • T6: Document results
- 13. Design Process Challenges SLIDE 12 • Understanding context of use • Time consuming • Requires access to users • What to do for entirely new products? • Product evaluation • Are we testing the right scenarios? • Will people actually use the product as we imagine?
- 18. Activity Recognition SLIDE 17 F F F B B B? ? ?
- 19. Segmentation & Labeling SLIDE 18 A B C C D B C C D E C E F C E B D A B C C D A AFeatures: median, derivative, relative change, frequency domain, …
- 20. Process Discovery SLIDE 19 Activity Log Process Discovery Algorithm Process Model
- 24. Automated Data Analysis Chain SLIDE 23
- 25. User Feedback & Data Analysis SLIDE 24
- 28. Experiment Monitoring SLIDE 27 Turning bottle upright correlates with temperature increase (30% feedings) Feeding ends, no aftercare in model Deviations: (20% feeds) -Re-heating bottle (30%) -Cold bottle motion (70%)
Editor's Notes
- Welcome everybody. PhD at the TU/e, collaboration project with Philips. Computer scientist, my research is applied & related to industrial engineering. Different background, feel free to talk to me after the presentation about doing research.
- You’ve probably heard it often today: Big data is everywhere! So what? Well, we can do cool stuff with all this data!
- We carry around a super computer, a portable interface to machines halfway across the world Gives us the power to make use of all this data through nice apps
- Not just smartphones, also cool things in the medical domain. Products with sensors and the combination of all sorts of data can be used to develop smart products. Smart products generally combine a product with one or more services. But how do we transform all these awesome ideas and concepts into real smart products?
- Nowadays many products are developed through user-centered design. Start a project with a group of people for who you want to develop a product or improve one.
- User profiles Use environment Intended uses User interface How does this work in practice? Let’s look at an example
- How does this work in practice? Let’s look at an example Existing product that we want to improve
- When in use, its context looks like this. Who will use the procuct? Surgeon / Radiologist Where will the product be used? Here What will the user do with the product? Make pictures during surgery What are the elements of the interaction? Pedals, buttons, screens User goals: see what is going on inside patient, minimal invasive intervention Tasks: prepare patient, search location, make images, assess treatment How do we know what they really do in practice? Observation!
- Observe the context in which you want to use your product: good if you want to understand the current way of working or have an existing product you’re improving See different scenarios
- If you have no access to the real environment or you only have a prototype: simulate the environment.
- Combine these insights into models & documentation
- Go through the cycle, figure out what can be improved or created to support the user goals, develop a product, test it, sell it Sounds easy! Is it? Not really…
- Can we execute this product design process in a smart way? Yes we can! Data science can help us understanding the product context & evaluating our designs, maybe even some other nice things! Let’s look at an example of a project where we designed a smart product
- Philips doesn’t just make medical imaging devices, they also produce baby bottles. Together with PhDs from industrial design, we worked on developing a smart bottle. Sleeve contains sensors, app records & analyses data
- Conceptual model made by designers Preparation, feeding, after care
- Data is not always easy to interpret, multiple sensors. Measuring physical property over time.
- Look for recurring patterns that may be an indication of an activity. Supervised learning if you have a prototype in a lab Unsupervised learning, due to uncertain labeling in real tests
- Segment the signal into pieces Calculate features for a window of measurements Cluster the windows
- Given a dataset of activities, process mining techniques can automatically create models representing the behaviour in the dataset.
- Can automatically create models of user behaviour. Can compare these with ideas designers have.
- We understand the context of use, great! Now we can even use this data in the product itself to do cool stuff.
- We set up an automated analysis chain to figure out on the fly what users are doing. Based on this information, actionable rules were created.
- User can get insights into their own behaviour, designers too!
- This also provides us with a way to do better when evaluating and testing the product
- Designers can get insights into all behaviour Show dashboards Calculate statistics Mine models of behaviour
- Comparing the behaviour of the experiment participants to the model revealed interesting insights. e.g. the temperature sensor was giving incorrect readings because the food loses contact with the sensor as the bottle starts to empty. We can do even more than just monitoring behaviour
- We are currently developing a tool to assist usability engineers in creating accurate test scenarios based on real user behaviour. Plan to use this tool to create usability testing scenarios for the interventional x-ray systems.
- It all comes together in a smart product.