Overall, the AR ecosystem is still small. Nevertheless, it’s fragmented. Google develops ARCore, Apple creates ARKit and Microsoft is working on the Mixed Reality Toolkit. Fortunately, Unity started unifying these APIs with the ARInterface.
The traditional mobile AR app development cycle includes compiling and deploying apps to a real device. That takes a long time and is tedious for quick testing iterations.
A big advantage of ARKit so far has been the ARKit Unity Remote feature. The iPhone runs a simple “tracking” app. It transmits its captured live data to the PC. Your actual AR app is running directly in the Unity Editor on the PC, based on the data it gets from the device. Through this approach, you can run the app by simply pressing the Play-button in Unity, without native compilation.
This is similar to the Holographic Emulation for the Microsoft HoloLens, which has been available for Unity for some time.
The great news is that the new Unity ARInterface finally adds a similar feature to Google ARCore: ARRemoteInterface. It’s available cross-platform for ARKit and ARCore.
In this article, you will learn how to add NFC tag reading to an Android app. It registers for auto-starting when the user taps a specific NDEF NFC tag with the phone. In addition, the app reads the NDEF records from the tag.
In this article, we add a click listener to a RecyclerView on Android. Advanced language features of Kotlin make it far easier than it has been with Java. However, you need to understand a few core concepts of the Kotlin language.
RecyclerView is the best approach to show scrolling lists on Android. It ensures high performance & smooth scrolling, while providing list elements with flexible layouts. Combined with modern language features of Kotlin, the code overhead of the RecyclerView is greatly reduced compared to the traditional Java approach.
In this last part, we bring the vital sign check list to life. Artificial Intelligence interprets assessments spoken in natural language. It extracts the relevant information and manages an up-to-date, browser-based checklist. Real-time communication is handled through Web Sockets with Socket.IO.
The example scenario focuses on a vital signs checklist in a hospital. The same concept applies to countless other use cases.
Training Artificial Intelligence to perform real-life tasks has been painful. The latest AI services now offer more accessible user interfaces. These require little knowledge about machine learning. The Microsoft LUIS service (Language Understanding Intelligent Service) performs an amazing task: interpreting natural language sentences and extracting relevant parts. You only need to provide 5+ sample sentences per scenario.
In this article series, we’re creating a sample app that interprets assessments from vital signs checks in hospitals. It filters out relevant information like the measured temperature or pupillary response. Yet, it’s easy to extend the scenario to any other area.
The vision: automatic checklists, filled out by simply listening to users explaining what they observe. The architecture of the sample app is based on a lightweight architecture: HTML5, Node.js + the LUIS service in the cloud.
Such an app would be incredibly useful in a hospital, where nurses need to perform and log countless vital sign checks with patients every day.
In part 1 of the article, I’ve explained the overall architecture of the service. In this part, we get hands-on and start implementing the Node.js-based backend. It will ultimately handle all the central messaging. It communicates both with the client user interface running in a browser, as well as the Microsoft LUIS language understanding service in the Azure Cloud.
Creating the Node Backend
We don’t have a Christmas tree in our apartment. But in today’s world, this is what Augmented Reality is for, right? Therefore, I decided to create an AR Christmas Tree in 5 minutes. This also gave me an opportunity to check out the new Google ARCore Developer Preview 2.
Christmas Tree 3D Model
First off, you need a 3D model of a Christmas tree. Two of the most accessible sources are Google Poly and Microsoft Remix 3D. Sticking to models created directly by Google and Microsoft, these two are the choices:
During the last few years, cognitive services became immensely powerful. Especially interesting is natural language understanding. Using the latest tools, training the computer to understand real spoken sentences and to extract information is reduced to a matter of minutes. We as humans no longer need to learn how to speak with a computer; it simply understands us.
I’ll show how to use the Language Understanding Cognitive Service (LUIS) from Microsoft. The aim is to build an automated check-list for nurses working at hospitals. Every morning, they record the vital sign of every patient. At the same time, they document the measurements on paper checklists.
With the new app developed in this article, the process is much easier. While checking the vital signs, nurses usually talk to the patients about their assessments. The “Vital Signs Checklist” app filters out the relevant data (e.g., the temperature or the pupillary response) and marks it in a checklist. Nurses no longer have to pick up a pen to manually record the information.
ARCore has a great feature – light estimation. The ARCore SDK estimates the global lighting, which you can use as input for your own shaders to make the virtual objects fit in better with the captured real world. In this article, I’m taking a closer look at how the light estimation works in the current ARCore preview SDK.