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In this work, we investigate an active learning scheme in the context of audio-based homemonitoring. Our experimental result shows that using the public dataset, NAR, the active learning scheme applied to a fast approximation of kernel logistic regression reduces the manual annotation by more than 84% while attaining similar performance to the supervised learning approach in terms of classification accuracy. ispartof: Proceedings of Benelearn 2016 ispartof: Benelearn 2016 location:Kortrijk, Belgium date:12 Sep - 13 Sep 2016 status: published

PURPOSE: Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications. METHOD: We performed a thorough literature review and had contact with manufacturers of commercially available devices. RESULTS: This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component. CONCLUSION: Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user's seizure types and personal preferences. publisher: Elsevier articletitle: Non-EEG seizure detection systems and potential SUDEP prevention: State of the art journaltitle: Seizure articlelink: http://dx.doi.org/10.1016/j.seizure.2016.07.012 content_type: article copyright: © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved. ispartof: Seizure vol:41 pages:141-153 ispartof: location:England status: published

This paper describes a system able to detect footstep locations. Through acoustic information retrieved from a wireless sensor network with small and relatively cheap microphone arrays. A dataset was recorded in order to validate the accuracy of the detection. Results on this dataset show that a best median of errors of 31cm per time moment are achievable, but results heavily depend on the positions of the microphones relative to the footsteps. Van Den Broeck B., Vuegen L., Van hamme H., Moonen M., Karsmakers P., Vanrumste B., ''Footstep localization based on in-home microphone-array signals'', Assistive technology: From research to practice (AAATE2013), proceedings 12th European AAATE conference, pp. 90-94, September 19-22, 2013, Vilamoura, Algarve, Portugal. ispartof: pages:90-94 ispartof: Assistive technology: From research to practice (AAATE2013) vol:33 pages:90-94 ispartof: European AAATE Conference location:Vilamoura, Portugal date:19 Sep - 22 Sep 2013 status: published

The development of an in-home fall risk assessment tool is under investigation. Several fall risk screening tests such as the Timed-Get-Up-and-Go-test (TGUG) only provide a snapshot taken at a given time and place, where automated in-home fall risk assessment tools can assess the fall risk of a person on a continuous basis. During this study we monitored four older people in their own home for a period of three months and automatically assessed fall risk parameters. We selected a subset of fixed walking sequences from the resulting real-life video for analysis of the time needed to perform these sequences. The results show a significant diurnal and health-related variance in the time needed to cross the same distance. These results also suggest that trends in the transfer time can be detected with the presented system. © 2013 The authors and IOS Press. All rights reserved. ispartof: pages:59-64 ispartof: Assistive technology: From research to practice: AAATE 2013 vol:33 pages:59-64 ispartof: European AAATE Conference location:Vilamoura, Portugal date:19 Sep - 22 Sep 2013 status: published

This work examines the use of a low-power Wireless AcousticSensor Network (WASN) for the observation of clinicallyrelevant activities of daily living (ADL) (e.g. eating, personalhygiene, toilet usage, etc.) from elderly. The sensors used inthe WASN are both audio and ultrasound receivers. To thebest of our knowledge, the combination of audio and ultrasoundas a basis for ADL monitoring has not been investigated yet.This paper describes a baseline approach for ADL classificationbased on Gaussian mixture models. Preliminary results inthis work indicate that classification accuracies up to 85.0 %± 14.6 for audio and 61.7% ± 11.3 for ultrasound are alreadyachievable on realistic real-life recorded data.Index Terms: acoustic scene analysis, audio, ultrasound,acoustic scene classification, activities of daily living, automaticmonitoring Vuegen L., Van Den Broeck B., Karsmakers P., Van hamme H., Vanrumste B., ''Automatic monitoring of activities of daily living based on real-life acoustic sensor data: a preliminary study'', 4th workshop on speech and language processing for assistive technologies - SLPAT-2013, 6 pp., August 21-22, 2013, Grenoble, France. ispartof: pages:113-118 ispartof: Fourth workshop on speech and language processing for assistive technologies (SLPAT): Proceedings pages:113-118 ispartof: Workshop on speech and language processing for assistive technologies - SLPAT-2013 location:Grenoble, France date:21 Aug - 22 Aug 2013 status: published

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. Debard G., Karsmakers P., Deschodt M., Vlaeyen E., Van den Bergh J., Dejaeger E., Milisen K., Goedemé T., Tuytelaars T., Vanrumste B., ''Camera based fall detection using multiple features validated with real life video'', Workshop proceedings 7th international conference on intelligent environments - IE 2011, vol. 10, pp. 441-450, July 25-28, 2011, Nottingham, UK. ispartof: pages:441-450 ispartof: Workshop Proceedings of the 7th International Conference on Intelligent Environments vol:10 pages:441-450 ispartof: International Conference on Intelligent Environments - IE 2011 location:Nottingham, UK date:25 Jul - 28 Jul 2011 status: published

status: published

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— Many older persons fall and are not able to get up again. The lack of timely aid can lead to severe complications. A camera based fall detection system can provide a solution. This paper gives the current status of our IWT-TETRA project FallCam. The goal of the project is to create a camera based fall detection system. Part of the project is creating an acquisition system for recording simulated as well as real-life falls at the homes of older people. The detection system will be developed and tested using these recordings. Furthermore, we will explain the advantages and disadvantages of different cameras and processing systems. Our choice is a centralized PC platform in combination with two regular IP cameras in each room of the monitored house. Debard G., Van den Bergh J., Bonroy B., Deschodt M., Dejaeger E., Milisen K., Goedemé T., Vanrumste B., ''FallCam: practical considerations in implementing a camera-based fall detection system'', Positioning and context-awareness international conference - POCA 2009, May 28, 2009, Antwerp, Belgium. ispartof: pages:17-23 ispartof: PoCA 09 pages:17-23 ispartof: Positioning and Context-Awareness International Conference location:Antwerp, Belgium date:28 May - 28 May 2009 status: published

Every year, thousands of elderly people are victim of a fall incident. Sometimes with severe consequences such as hip fractures or even death but, in many cases, the main problem is that an injured elderly may be laying on the ground for several hours or even days after a fall incident has occurred. This makes it important to have a fall detection system. Commercial types of fall detection systems are mostly based on wearable sensors, which the elderly may forget to wear. Although we will give a short presentation of these sensor-based devices, this paper focusses on the existing approaches to detect a fall in video. Therefore we have to deal with the different types of background subtraction. After having studied the practical approaches for background subtraction, we went further to the next step in the algorithm, namely fall detection itself. Beside these specific techniques, we also give an overview in difficulties while implementing a fall detection algorithm. In our conclusion we will see that all systems studied in this paper have their own advantages and disadvantages. To become a good video-based fall detection system, a combination of different techniques will be needed. Willems J., Debard G., Bonroy B., Vanrumste B., Goedemé T., ''How to detect human fall in video? An overview'', Positioning and context-awareness international conference - POCA 2009, May 28, 2009, Antwerp, Belgium. ispartof: Positioning and context-awareness international conference - POCA 2009 location:Antwerp, Belgium date:28 May - 28 May 2009 status: accepted