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.

In the era of Internet of Things (IoT) technologies the potential for privacy invasion is becoming a major concern especially in regards to healthcare data and Ambient Assisted Living (AAL) environments. Systems that offer AAL technologies make extensive use of personal data in order to provide services that are context-aware and personalized. This makes privacy preservation a very important issue especially since the users are not always aware of the privacy risks they could face. A lot of progress has been made in the deep learning field, however, there has been lack of research on privacy preservation of sensitive personal data with the use of deep learning. In this paper we focus on a Long Short Term Memory (LSTM) Encoder-Decoder, which is a principal component of deep learning, and propose a new encoding technique that allows the creation of different AAL data views, depending on the access level of the end user and the information they require access to. The efficiency and effectiveness of the proposed method are demonstrated with experiments on a simulated AAL dataset. Qualitatively, we show that the proposed model learns privacy operations such as disclosure, deletion and generalization and can perform encoding and decoding of the data with almost perfect recovery.

Nowadays, information and communication technologies (ICT) have become part of our everyday life, enhancing the quality of life and promoting new forms of social interaction. Despite the numerous benefits of ICT, older adults still present low rates of ICT adoption compared to other population segments. The lack of accessible User Interfaces has been identified as a major barrier. Traditional User Interfaces follow a design for all approach, typically ignoring the needs of older adults. Recent research in Human-Computer Interaction (HCI) proposes adaptive User Interfaces to suit the individual users abilities. Nevertheless, most of the existing approaches perform adaptation based on user profile groups and do not provide personalized adaptation in real-time. This paper introduces a conceptual framework for developing real-time adaptive User Interfaces. The system aims to target most common issues among older adults, ${i.e.}$cognitive decline and vision loss. The developed conceptual framework also presents novel strategic techniques to assess cognitive load and vision related issues in an unobtrusive manner for the user.