Toon Goedemé

An overview of today's high-level synthesis tools

High-level synthesis (HLS) is an increasingly popular approach in electronic design automation (EDA) that raises the abstraction level for designing digital circuits. With the increasing complexity of embedded systems, these tools are particularly relevant in embedded systems design. In this paper, we present our evaluation of a broad selection of recent HLS tools in terms of capabilities, usability and quality of results. Even though HLS tools are still lacking some maturity, they are constantly improving and the industry is now starting to adopt them into their design flows.

Towards a more effective method for analyzing mobile eye-tracking data: integrating gaze data with object recognition algorithms

In this paper we present the outlines of a new project that aims at developing and implementing effective new methods for analyzing gaze data collected with mobile eye-tracking devices. More specifically, we argue for the integration of object recognition algorithms from vision engineering, such as invariant region matching techniques, in gaze analysis software. We present a series of arguments why an object-based approach may provide a significant surplus, in terms of analytical precision, flexibility, additional application areas and cost efficiency, to the existing systems that use predefined areas of analysis.n In order to test the actual analytical power of object recognition algorithms for the analysis of gaze data recorded in the wild, we develop a series of test cases in different real world situations, including shopping behavior, navigation, handling and usability of mobile systems. By setting up these case studies in close collaboration with key players in the relevant fields (retailers, signage consultants, market and user-experience research, and developers of eye-tracking hard- and software), we will be able to sketch an accurate picture of the pros and cons of the proposed method in comparison to current analytical practice.

Camera Based Fall Detection Using Multiple Features Validated with Real Life Video

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again unaided. The lack of timely aid can lead to severe complications such as dehydration, pressure ulcers and death. A camera based fall detection system can provide a solution. In this paper we compare four different fall features extracted from the dominant foreground object, as well as various combinations thereof. All tests are executed using real life data, which has been recorded at the home of 4 elderly, containing 24 falls. Experiments indicate that a fall detector based on a combination of aspect ratio, head speed and fall angle is preferred. The preliminary detector, which still has a substantial false alarm rate with a precision of 0.257(±0.073) and a promising recall of 0.896(±0.194), gives insights for further improvement as is discussed.

Automatic analysis of eye-tracking data using object detection algorithms

In this paper we investigate the integration of object detection algorithms with eye-tracking data. The emerging technology of lightweight mobile eye-trackers enables realistic in-the-wild user experience experiments. Unfortunately, mobile eye-trackers generate a large amount of video data, which up to now requires manual analysis. This time-consuming and repetitive task renders processing large datasets economically infeasible. Our main contribution is the use of object detection algorithms to perform this analysis task automatically. We compare several object detection algorithms with regard to both speed and accuracy. To prove their functionality, we have recorded an eye-tracker shopping experiment and processed the data using object detection techniques.