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Our cases

AI for E-sports

Shot speed prediction using object tracking for Passion Football

The ICONS app by Passion Football lets football players of all levels develop their skills and challenge others. We developed a solution that uses the smartphone camera to track and accurately estimate a players shot speed.

With the app, users can access a variety of exercises that measure essential football skills. These exercises are designed to be both fun and interactive, while also providing analytical insights to the player and suggesting exercises that target and improve their weak areas.

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AI for Heavy industry

On-device, real time car fuel lid detection  

We developed a CPU-optimized object detection model for on-device detection of fuel lids on cars.

The model was integrated into the central control system of the fueling rig, taking the role of the main detection mechanism, firstly to make sure the car is in the right position, and secondly to guide the fueling arm to the fuel door lid in real-time.When optimizing the model for CPU inference, we explored TensorFlow Lite and OpenVINO. In the end, we used OpenVINO which is a framework for optimizing deep neural nets on Intel CPU’s.

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AI for Proptech

Intelligent indoor climate solution for real estate developer

Predicting what the temperature will be in a meeting room, 25 minutes into the future.

We used data that had been gathered over a year at the company’s HQ. A combination of temperature, CO2, outdoor temperature, climate system, and meeting room booking data.

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AI for E-sports

Real-time object detection for online gaming tournaments

Abios Gaming is a startup that focuses on tracking real-time events in online gaming tournament video streams. They asked us to improve detection performance and make previously impossible detections, possible.

Among the objects to be tracked were various weapons and tools the players use in-game, as well as certain interactions. The system detects and tracks timestamped events in correspondence with the objects the players possess, or use, to provide a structured stream of events that captures essential information about the evolution of the tournament.

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AI for Life sciences

Detecting underlying paroxysmal atrial fibrillation for Zenicor

Together with Zenicor Medical Systems AB, we trained a deep learning model to predict an underlying paroxysmal atrial fibrillation (AF) condition in patients.

Every day, thousands of people suffer from stroke due to an underlying undiagnosed AF condition. Stroke often results in conditions such as paralysis or, in many cases, death. For this reason, precise and early diagnosis of AF is an essential part of stroke prevention.

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AI for Life sciences

Detecting food intake in glucose time-series from diabetes patients

Together with Digital Diabetes Analytics, we created a meal detection system using glucose level data to help people with insulin-treated diabetes.

Today, more than 5,5 million people worldwide live with type 1 diabetes and need insulin-based treatment to survive. When it comes to diabetes, there’s no “one treatment fits all”, therefore doctor’s need support in optimizing treatments for each patient.

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AI for Retail

Forecasting the success of fishing trips, for millions of Fishbrain users

Data from 2.5 millions catches was collected and refined. Each data point was annotated with date, time, location, and fish species. Detailed weather information was added to each catch; air temperature, air pressure, wind speed, cloud cover, and precipitation. Temporal data was transformed into astronomical conditions such as moon phase, solar irradiation, and azimuth angle. A global climate model was created,

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Integrated AI

Detecting faulty transmission pole guy wires in drone images 

The system consists of deep learning and traditional machine learning parts and is able of screening vast amounts of drone images to detect faulty guy wires.

The objective of the project was to create a machine learning pipeline to analyze images from power lines and detect lack of tension in guy wires. To solve this problem, the team broke down the problem in different stages and created a multi-model AI solution, in close collaboration with the client.

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