Edith Maier

ALADIN - a Magic Lamp for the Elderly?

Like Aladdin in the medieval oriental folk-tale, the assistive lighting system developed by ALADIN (Ambient Lighting Assistance for an Ageing Population), a research project co-financed by the European Commission, is expected to bring enchantment to people’s lives. But this will not be achieved by magic and genies, but by exploiting our knowledge about the impact of lighting. adaptive lighting can contribute considerably to sound sleep and a regular sleep-wake cycle regulated by people’s ’inner clock’. This tends to deteriorate with ageing, but is essential to preserve and enhance comfort and wellbeing. And this is the main goal of the assistive ALADIN lighting system. So called ’ambient lighting’ with varying color, temperature and brightness has been in use for some time. However, the user has no possibility to interact with the predefined control strategy and the lighting solutions do not take into account individual differences.[3] The intelligent control system of ALADIN, however, is capable of • capturingand analysingtheindividualand situationaldifferencesof thepsychophysiological effects of lighting, and • enabling the users to make adaptations tailored to their specific needs. Whereascurrentlymostcognitiveassessment is donein a clinicalsetting [15], we use sensor-based monitoring combined with adaptive algorithms to assess people’s levelof functioningin a continuousway. Besides, we havetaken the resultsobtained

AAL in the Wild --- Lessons Learned

In the EU-funded ALADIN project the prototype of an ambient assistive lighting system was subjected to a three-month test in private households of older people. Despite intensive usability testing in the development phase, field trials pose special challenges including ethical issues such as obtaining informed consent and the need for guidelines for interviewing old people. Besides, real-life settings give rise to particular distortion effects which have to be taken into account in the analysis of the results. Although the findings indicate any overall slight increase in peoples mental and physical fitness, they also suggest how the prototype can be improved in several respects. Above all it has been shown that packaging the technology with social support measures is essential to achieve higher user acceptance. Besides, the article discusses lessons learned related to the organization of user testing in real-life settings.

Learning a Lightweight Ontology for Semantic Retrieval in Patient-Centered Information Systems

The paper introduces a web-based eHealth platform currently being developed that will assist patients with certain chronic diseases. The ultimate aim is behavioral change. This is supported by online assessment and feedback which visualizes actual behavior in relation to target behavior. Disease-specific information is provided through an information portal that utilizes lightweight ontologies associative networks in combination with text mining. The paper argues that classical word-based information retrieval is often not sufficient for providing patients with relevant information, but that their information needs are better addressed by concept-based retrieval. The focus of the paper is on the semantic retrieval component and the learning of a lightweight ontology from text documents, which is achieved by using a biologically inspired neural network. The paper concludes with preliminary results of the evaluation of the proposed approach in comparison with traditional approaches.

Mobile Stress Recognition and Relaxation Support with SmartCoping: User-Adaptive Interpretation of Physiological Stress Parameters

The paper describes a mobile solution for the early recognition and management of stress based on continuous monitoring of heart rate variability (HRV) and contextual data (activity, location, etc.). A central contribution is the automatic calibration of measured HRV values to perceived stress levels during an initial learning phase where the user provides feedback when prompted by the system. This is crucial as HRV varies greatly among people. A data mining component identifies recurrent stress situations so that people can develop appropriate stress avoidance and coping strategies. A biofeedback component based on breathing exercises helps users relax. The solution is being tested by healthy volunteers before conducting a clinical study with patients after alcohol detoxification.

Climate change and One Health

Abstract The journal The Lancet recently published a countdown on health and climate change. Attention was focused solely on humans. However, animals, including wildlife, livestock and pets, may also be impacted by climate change. Complementary to the high relevance of awareness rising for protecting humans against climate change, here we present a One Health approach, which aims at the simultaneous protection of humans, animals and the environment from climate change impacts (climate change adaptation). We postulate that integrated approaches save human and animal lives and reduce costs when compared to public and animal health sectors working separately. A One Health approach to climate change adaptation may significantly contribute to food security with emphasis on animal source foods, extensive livestock systems, particularly ruminant livestock, environmental sanitation, and steps towards regional and global integrated syndromic surveillance and response systems. The cost of outbreaks of emerging vector‐borne zoonotic pathogens may be much lower if they are detected early in the vector or in livestock rather than later in humans. Therefore, integrated community‐based surveillance of zoonoses is a promising avenue to reduce health effects of climate change.

Recognizing Sleep Stages with Wearable Sensors in Everyday Settings.

The paper presents results from the SmartSleep project which aims at developing a smartphone app that gives users individual advice on how to change their behaviour to improve their sleep. The advice is generated by identifying correlations between behaviour during the day and sleep architecture. To this end, the project addresses two sub-tasks: detecting a user’s daytime behaviour and recognising sleep stages in an everyday setting. The focus of the paper is on the second task. Various sensor devices from the consumer market were used in addition to the usual PSG sensors in a sleep lab. An expert assigned a sleep stage for every 30 seconds. Subsequently, a sleep stage classifier was learned from the resulting sensor data streams segmented into labelled sleep stages of 30 seconds each. Apart from handcrafted features we also experimented with unsupervised feature learning based on the deep learning paradigm. Our best results for correctly classified sleep stages are in the range of 90 to 91% for Wake, REM and N3, while the best recognition rate for N2 is 83%. The classification results for N1 turned out to be much worse, N1 being mostly confused with N2.

A Self-learning Application Framework for Behavioral Change Support

The paper analyzes current weaknesses of behavioral change support systems such as the lack of adequately taking into account the heterogeneity of target users. Based on this analysis the paper presents an application framework that comprises various components to accommodate user preferences and to adapt system interventions to individual users: a goal hierarchy which users can tailor to their needs, dividing nudges into different types that correspond to speech acts, rules for context-specific triggering of nudges. User adaptation is realized with approaches from user modeling and collaborative filtering. The result is a self-learning application that changes in line with a user’s progress, which is expected to enhance user acceptance and increase and sustain people’s motivation for behavioral change. The application framework will be evaluated by comparing a mobile health app using the framework with a simplified version of the app that does not support user tailoring and adaptation.

Digital Change—New Opportunities and Challenges for Tapping Experience and Lessons Learned for Organisational Value Creation

Digital change and Industry 4.0 do not erase the need for human insight or experience. This has been shown by a recent survey conducted among managers in the German-speaking world who still consider experience a highly valuable asset. Digital change, however, has shifted the focus from products to customers and implies new roles for employees such as supervising machines and processes, and assessing data analysis results. At the same time, new digital trends and tools open up new opportunities for automatically capturing, exchanging and preserving lessons learned, and offer support that is both context-aware and situation-specific. Since they should not require any additional effort, digital trends and tools may also help remove a key obstacle to innovation, i.e. the failure to learn from mistakes.

Laying the Foundation for Correlating Daytime Behaviour with Sleep Architecture Using Wearable Sensors.

The paper presents results from the SmartSleep project which aims at developing a smartphone app that gives users individual advice on how to change their behaviour to improve their sleep. The advice is generated by identifying correlations between behaviour during the day and sleep architecture. To this end, the project addressed two sub-tasks: detecting a user’s daytime behaviour and recognising sleep stages in an everyday setting. In the case of daytime activity detection the best results were achieved using an accelerometer at the wrist and another one at the ankle (87%). A subsequent smoothing step increased the accuracy to over 90%. For recognising sleep architecture we experimented with various consumer wearables that we used in addition to the usual PSG sensors in a sleep lab. Several sleep stage classifiers were learned from the resulting sensor data streams segmented into labelled sleep stages of 30 s each. Apart from handcrafted features we experimented with unsupervised feature learning based on the deep learning paradigm. Our best results for correctly classified sleep stages are between 86 and 90% for Wake, REM, N2 and N3, while the best recognition rate for N1 is 37%. Finally, we discuss a preliminary design of the algorithm for determining correlations between daytime behaviour and sleep architecture.

Workshop on intelligent IT tools for knowledge management systems: applicability, usability, and benefits

Successful knowledge management projects incorporate aspects from the following dimensions: processes, contents, corporate culture, information technologies (IT). IT has the role to facilitate storage, retrieval and presentation of information and is therefore often called an enabler. An inadequate use of IT can lead to the failure of a knowledge management project, cause considerable delays and detract from the motivation of the people involved.