Real-Time Sensor Observation Segmentation For Complex Activity Recognition Within Smart Environments

The file attached to this record is the author's final peer reviewed versionActivity Recognition (AR) is at the heart of any types of assistive living systems. One of the key challenges faced in AR is segmentation of the sensor events when inhabitant performs simple or composite activities of daily living (ADLs). In addition, each inhabitant may follow a particular ritual or a tradition in performing different ADLs and their patterns may change overtime. Many recent studies apply methods to segment and recognise generic ADLs performed in a composite manner. However, little has been explored in semantically distinguishing individual sensor events and directly passing it to the relevant ongoing/new atomic activities. This paper proposes to use the ontological model to capture generic knowledge of ADLs and methods which also takes inhabitant-specific preferences into considerations when segmenting sensor events. The system implementation was developed, deployed and evaluated against 84 use case scenarios. The result suggests that all sensor events were adequately segmented with 98% accuracy and the average classification time of 3971ms and 62183ms for single and composite ADL scenarios were recorded, respectively

Reality and Perception: Activity monitoring and data collection within a real-world smart home

The file attached to this record is the author's final peer reviewed version.Smart home technologies have been developing rapidly in the last few years. However, there is still a lack of annotated rich datasets that can be used for different analysis purposes by researchers. The motivation for this study is driven by the need of self-management for chronic disease patients and the often neglected privacy aspects. The study describes the extension of an existing smart home environment at Great Northern Haven (GHN) with ambient and wearable devices. The discussed principles include the design of the experiment, data collection strategies and encountered challenges in regards to the sensors, connection problems and occupation with multiple inhabitants

Challenges in assessing privacy impact: Tales from the front lines

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data protection impact assessments (DPIAs) aim to identify, rank, and mitigate privacy risks. Even though DPIAs are legally mandated in some cases and privacy professionals perform DPIAs on a daily basis, facilitating the systematic measurement of privacy risks is an open problem. Research on privacy risk measurement often does not take into account the practical needs and requirements for DPIAs in real organizations. In this article, we fill this gap by reporting on focus groups we held with a diverse group of privacy professionals. Through thematic analysis, we identify three themes that emerged from the focus groups: (a) how privacy in the contemporary society affects privacy risk assessment; (b) current practices and procedures in privacy risk assessment; and (c) common issues and challenges. Based on these themes, we identify future research directions for privacy risk measurement. Our article can help to ground research on privacy risk measurement in practical challenges faced by privacy professionals

Privacy Modelling and Management for Assisted Living within Smart Homes

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkAmbient Assisted Living (AAL) technologies create intelligent systems to assist the aging population for a healthier and safer life in their living environment. Such systems usually offer context-aware, personalized and adaptive services. However, these kinds of systems make extensive and intensive use of personal data, which makes privacy protection a critical issue. In this paper, we propose a framework for privacy modeling computation and management for AAL within Smart Homes. We analyze the privacy features in the smart home that affect the privacy of the users. Based on these features a metric is developed to compute the sensitivity of the collected information and consequently the potential privacy risk. A simple implementation of the proposed framework is then applied to a real world smart home living environment at Great Northern Haven, in which data were collected and the framework was evaluated. This study offers an effective and practical approach to evaluate the privacy risk of users and proposes a metric that can be used for access control and recommendation of privacy settings to the users of the AAL environments

Users’ Privacy Concerns in IoT based Applications

In recent years user privacy has become an important aspect in the development of the Internet of Things (IoT) services due to their privacy invasive nature. However, there has been comparatively little research so far that aims to understanding users’ notion of privacy in connection with IoT. In this work, we aim to understand how and if contextual factors affect users’ privacy perceptions of IoT environments. To ascertain privacy perceptions, we deployed a public online survey (N=236) and contacted interviews (N=41) to explore factors that could have an influence. Although a lot of the participants identified privacy risks in IoT and rated the collected information items with high privacy ratings, we find that quite a large number of participants would still decide to have the offered IoT service if they find it useful and practical for their daily lives despite the infringement on their privacy. We conclude by highlighting and analyzing the qualitative comments of the participants and suggest possible solutions for the identified issues

A Deep Learning approach to Privacy Preservation in Assisted Living

In the era of IoT technologies the potential for privacy invasion is becoming a major concern especially in regards to healthcare data and Ambient Assisted Living (AAL) environments. The need for sharing of healthcare data between various systems and stakeholders is growing rapidly. 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 an Long Short Term Memory (LSTM) Encoder-Decoder, which is a principal component of deep learning, and propose a new encryption 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

A deep learning approach for privacy preservation in assisted living

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