Sten Hanke

universAAL – An Open and Consolidated AAL Platform

Due to the demographic development towards an ageing society AAL technologies will play an important role in the future. There has been a lot of work done in the field of AAL, but most of the project outcomes are proprietary and thus impossible to be combined. Accordingly, there is a need for an universal and open platform, which can be used as a starting point for further developments or just as an integration and standardization tool. For future service platform related research projects reference use cases as well as a reference tool set and framework would help to ensure a reusable and expandable platform, which is wide spread and therefore ensures a quality of service. The aim of the universAAL project is to combine the advantages and strengths of still ongoing or already finished research projects to create an universally applicable platform. The focus thereby is on interoperability and standardization to ensure a broad range of applicability and to develop an open platform that will make it technically feasible and economically viable to develop AAL applications. There are two tools for spreading the outcomes and ideas of the project planned: On the one hand the establishment of a store providing plug-and-play AAL applications and services that support multiple execution platforms and can be deployed to various devices and users, and on the other hand the AAL Open Association (AALOA) with the mission to create a platform for identifying key research topics in AAL, and to reach agreement on prioritization of these and to design, develop, evaluate and standardize a common service platform for AAL.

A modular platform for event recognition in smart homes

Ambient Assisted Living technologies try to integrate intelligent assistance-systems in elder peoples homes to maintain a high degree of independence, autonomy and dignity. To speed up the development process and to make the applications more adaptive and flexible to special user needs as well as to ensure compatibility throughout systems a common middleware with standardized interfaces is desirable. The integration of off-the-shelf sensor hardware is an important aspect to assure longterm applicability and interoperability. AIT Austrian Institute of Technology has developed a modular software platform providing services to enable independent living for elder people at home. The platform is based on state-of-the-art software development techniques like OSGi and Spring, which enable modularity and flexibility. The aspect of interoperability at the hardware level is considered by integrating standards from the two areas of medical informatics and home automation. A hardware abstraction module harmonizes data from different sensor networks in terms of a common data format. Based on sensor data, functionalities for the detection of specific events and situations in the AAL domain have been implemented using finite state machines and simple statistical methods.

Smart Environments and Context-Awareness for Lifestyle Management in a Healthy Active Ageing Framework*

Health trends of elderly in Europe motivate the need for technological solutions aimed at preventing the main causes of morbidity and premature mortality. In this framework, the DOREMI project addresses three important causes of morbidity and mortality in the elderly by devising an ICT-based home care services for aging people to contrast cognitive decline, sedentariness and unhealthy dietary habits. In this paper, we present the general architecture of DOREMI, focusing on its aspects of human activity recognition and reasoning.

Human activity recognition using recurrent neural networks

Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.

Ambient Assisted Living Technologies from the Perspectives of Older People and Professionals

Ambient Assisted Living (AAL) and Ambient Intelligence technologies are providing support to older people in living an independent and confident life by developing innovative ICT-based products, services, and systems. Despite significant advancement in AAL technologies and smart systems, they have still not found the way into the nursing home of the older people. The reasons are manifold. On one hand, the development of such systems lack in addressing the requirements of the older people and caregivers of the organization and the other is the unwillingness of the older people to make use of assistive systems. A qualitative study was performed at a nursing home to understand the needs and requirements of the residents and caregivers and their perspectives about the existing AAL technologies.

CogniWin – A Virtual Assistance System for Older Adults at Work

This paper presents an innovative virtual assistant system, which aims to address older adults’ needs in a professional environment by proposing promising and innovative virtual assistance mechanisms. The system, named CogniWin, is expected to alleviate eventual age related memory degradation and gradual decrease of other cognitive capabilities (i.e. speed of processing new information, concentration level) and at the same time assist older adults to increase their learning abilities through personalized learning assistance and well-being guidance. In this paper we describe the overall system concept, the technological approach, the methodology used in the elicitation of user needs, and describe the first pre-trials’ evaluation.

Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment

Convolutional Neural Networks (CNN) are very useful for fully automatic extraction of discriminative features from raw sensor data. This is an important problem in activity recognition, which is of enormous interest in ambient sensor environments due to its universality on various applications. Activity recognition in smart homes uses large amounts of time-series sensor data to infer daily living activities and to extract effective features from those activities, which is a challenging task. In this paper we demonstrate the use of the CNN and a comparison of results, which has been performed with Long Short Term Memory (LSTM), recurrent neural networks and other machine learning algorithms, including Naive Bayes, Hidden Markov Models, Hidden Semi-Markov Models and Conditional Random Fields. The experimental results on publicly available smart home datasets demonstrate that the performance of 1D-CNN is similar to LSTM and better than the other probabilistic models.

Talking Faces in Lab and Field Trials

In recent years, there has been an increasing interest in Ambient Assisted Living technology to support older adults. Research and industry are working jointly on reliable and suitable solutions to help older adults to remain healthy and safe while living independently. Appropriate interaction methods play an important role for the acceptance of such supporting systems. Today, solutions mainly rely on common and well-evaluated interaction techniques such as TV remotes or touch screens to enhance the usability. Projects presented in this work are based on the same interaction techniques, but additionally enrich the interaction experience with a real-time, empathic virtual assistance avatar. In this paper, we present evaluation settings and user involvement results acquired from three different Ambient Assisted Living projects focusing on avatar-based user interaction. Our results show that avatar-based interaction in the Ambient Assisted Living context is very well applicable, especially when combined with speech recognition.

Users’ Perceptions and Attitudes Towards Smart Home Technologies

The concept of smart home is a promising and efficient way of maintaining good health, providing comfort and safety thus helps in enhancing the quality of life. Acceptability of smart homes relies on the users’ perceptions towards its benefits and their concerns related to monitoring and data sharing. Within this study, an online survey with 234 participants has been conducted to understand the attitudes and perceptions of future smart home users, followed by detailed analysis of their responses. In general, the users agree that the smart home technology would improve the quality of life to a greater extent and enhance the safety and security of residents. On the contrary, they raise several concerns such as the increased dependence on technology and the monitoring of private activities, which may be seen as perceived drawbacks. The obtained results show that the older adults (ages from 36 to 70 years) are more open to monitoring and sharing data especially if it useful for their doctors and caregivers while the young adults (ages up to 35 years) are somewhat reluctant to share information.

Development of and adherence to a computer-based gamified environment designed to promote health and wellbeing in older people with mild cognitive impairment.

BACKGROUND The older population of Europe is increasing and there has been a corresponding increase in long term care costs. This project sought to promote active ageing by delivering tasks via a tablet computer to participants aged 65-80 with mild cognitive impairment. OBJECTIVES An age-appropriate gamified environment was developed and adherence to this solution was assessed through an intervention. METHODS The gamified environment was developed through focus groups. Mixed methods were used in the intervention with the time spent engaging with applications recorded supplemented by participant interviews to gauge adherence. There were two groups of participants: one living in a retirement village and the other living separately across a city. RESULTS The retirement village participants engaged in more than three times the number of game sessions compared to the other group possibly because of different social arrangements between the groups. CONCLUSION A gamified environment can help older people engage in computer-based applications. However, social community factors influence adherence in a longer term intervention.