Wearable electric potential sensing: a new modality sensing hair touch and restless leg movement

Electric potential sensors (EPS) are classified as capacitive sensors with the ability to measure small variations in electric potential or electric field remotely and accurately. Here we show how a low cost single chip version of EPS can be integrated into a wearable device such as smart watch to provide relevant information about habitual movements specifically, hair touching and scratching as well as leg movement. This new modality could be used in consumer care product research such as studying the quality of shampoos and to study restless leg syndrome remotely without the need of wearing additional sensors. In both scenarios, a single sensor was worn on the wrist, similar to a smart watch, with the sensing electrode pointing away from the body (i.e. no skin contact)

Verlatstraat te Antwerpen (gem. Antwerpen). Archeologische begeleiding.

Dit rapport werd ingediend bij het agentschap samen met een aantal afzonderlijke digitale bijlagen. Een aantal van deze bijlagen zijn niet inbegrepen in dit pdf document en zijn niet online beschikbaar. Sommige bijlagen (grondplannen, fotos, spoorbeschrijvingen, enz.) kunnen van belang zijn voor een betere lezing en interpretatie van dit rapport. Indien u deze bijlagen wenst te raadplegen kan u daarvoor contact opnemen met: [email protected]

Demer door Diest fase 3 Demerstraat Refugiestraat en Brouwerijstraat gemeente Diest. Archeologische begeleiding en opgraving (deel 1 van 2)

Dit rapport werd ingediend bij het agentschap samen met een aantal afzonderlijke digitale bijlagen. Een aantal van deze bijlagen zijn niet inbegrepen in dit pdf document en zijn niet online beschikbaar. Sommige bijlagen (grondplannen, fotos, spoorbeschrijvingen, enz.) kunnen van belang zijn voor een betere lezing en interpretatie van dit rapport. Indien u deze bijlagen wenst te raadplegen kan u daarvoor contact opnemen met: [email protected]

Ontogenetic Development and Fault Tolerance in the POEtic Tissue

In this article, we introduce the approach to the realization of ontogenetic development and fault tolerance that will be implemented in the POEtic tissue, a novel reconfigurable digital circuit dedicated to the realization of bio-inspired systems. The modelization in electronic hardware of the developmental process of multi-cellular biological organisms is an approach that could become extremely useful in the implementation of highly complex systems, where concepts such as self-organization and fault tolerance are key issues. The concepts presented in this article represent an attempt at finding a useful set of mechanisms to allow the implementation in digital hardware of a bio-inspired developmental process with a reasonable overhead

Opportunistic human activity and context recognition

Although the Internet of Things allows seamless access to billions of sensors readily deployed throughout the world, current context- and activity-recognition approaches restrict ambient intelligence to domains where dedicated sensors are deployed. The big data delivered by the Internet of Things calls for a new opportunistic recognition paradigm. Instead of setting-up information sources for a specific recognition goal, the methods themselves adapt to the data available at any time. We present enabling methods that allow for opportunistic recognition in dynamic sensor configurations. This could be the missing link to fulfill the promise of ambient intelligence anywhere

Demer door Diest fase 4-1 Brouwerijstraat Zoutstraat en De Kaai gemeente Diest. Archeologische begeleiding en opgraving (deel 2 van 2)

Dit rapport werd ingediend bij het agentschap samen met een aantal afzonderlijke digitale bijlagen. Een aantal van deze bijlagen zijn niet inbegrepen in dit pdf document en zijn niet online beschikbaar. Sommige bijlagen (grondplannen, fotos, spoorbeschrijvingen, enz.) kunnen van belang zijn voor een betere lezing en interpretatie van dit rapport. Indien u deze bijlagen wenst te raadplegen kan u daarvoor contact opnemen met: [email protected]

The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition

There is a growing interest on using ambient and wearable sensors for human activity recognition, fostered by several application domains and wider availability of sensing technologies. This has triggered increasing attention on the development of robust machine learning techniques that exploits multimodal sensor setups. However, unlike other applications, there are no established benchmarking problems for this field. As a matter of fact, methods are usually tested on custom datasets acquired in very specific experimental setups. Furthermore, data is seldom shared between different groups. Our goal is to address this issue by introducing a versatile human activity dataset recorded in a sensor-rich environment. This database was the basis of an open challenge on activity recognition. We report here the outcome of this challenge, as well as baseline performance using different classification techniques. We expect this benchmarking database will motivate other researchers to replicate and outperform the presented results, thus contributing to further advances in the state-of-the-art of activity recognition methods

Benchmarking classification techniques using the Opportunity human activity dataset

Human activity recognition is a thriving research field. There are lots of studies in different sub-areas of activity recognition proposing different methods. However, unlike other applications, there is lack of established benchmarking problems for activity recognition. Typically, each research group tests and reports the performance of their algorithms on their own datasets using experimental setups specially conceived for that specific purpose. In this work, we introduce a versatile human activity dataset conceived to fill that void. We illustrate its use by presenting comparative results of different classification techniques, and discuss about several metrics that can be used to assess their performance. Being an initial benchmarking, we expect that the possibility to replicate and outperform the presented results will contribute to further advances in state-of-the-art methods

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work