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

East Midlands Research into Ageing Network (EMRAN) Discussion Paper Series

Academic geriatric medicine in Leicester . There has never been a better time to consider joining us. We have recently appointed a Professor in Geriatric Medicine, alongside Tom Robinson in stroke and Victoria Haunton, who has just joined as a Senior Lecturer in Geriatric Medicine. We have fantastic opportunities to support students in their academic pursuits through a well-established intercalated BSc programme, and routes on through such as ACF posts, and a successful track-record in delivering higher degrees leading to ACL post. We collaborate strongly with Health Sciences, including academic primary care. See below for more detail on our existing academic set-up. Leicester Academy for the Study of Ageing We are also collaborating on a grander scale, through a joint academic venture focusing on ageing, the ‘Leicester Academy for the Study of Ageing’ (LASA), which involves the local health service providers (acute and community), De Montfort University; University of Leicester; Leicester City Council; Leicestershire County Council and Leicester Age UK. Professors Jayne Brown and Simon Conroy jointly Chair LASA and have recently been joined by two further Chairs, Professors Kay de Vries and Bertha Ochieng. Karen Harrison Dening has also recently been appointed an Honorary Chair. LASA aims to improve outcomes for older people and those that care for them that takes a person-centred, whole system perspective. Our research will take a global perspective, but will seek to maximise benefits for the people of Leicester, Leicestershire and Rutland, including building capacity. We are undertaking applied, translational, interdisciplinary research, focused on older people, which will deliver research outcomes that address domains from: physical/medical; functional ability, cognitive/psychological; social or environmental factors. LASA also seeks to support commissioners and providers alike for advice on how to improve care for older people, whether by research, education or service delivery. Examples of recent research projects include: ‘Local History Café’ project specifically undertaking an evaluation on loneliness and social isolation; ‘Better Visits’ project focused on improving visiting for family members of people with dementia resident in care homes; and a study on health issues for older LGBT people in Leicester. Clinical Geriatric Medicine in Leicester We have developed a service which recognises the complexity of managing frail older people at the interface (acute care, emergency care and links with community services). There are presently 17 consultant geriatricians supported by existing multidisciplinary teams, including the largest complement of Advance Nurse Practitioners in the country. Together we deliver Comprehensive Geriatric Assessment to frail older people with urgent care needs in acute and community settings. The acute and emergency frailty units – Leicester Royal Infirmary This development aims at delivering Comprehensive Geriatric Assessment to frail older people in the acute setting. Patients are screened for frailty in the Emergency Department and then undergo a multidisciplinary assessment including a consultant geriatrician, before being triaged to the most appropriate setting. This might include admission to in-patient care in the acute or community setting, intermediate care (residential or home based), or occasionally other specialist care (e.g. cardiorespiratory). Our new emergency department is the county’s first frail friendly build and includes fantastic facilities aimed at promoting early recovering and reducing the risk of hospital associated harms. There is also a daily liaison service jointly run with the psychogeriatricians (FOPAL); we have been examining geriatric outreach to oncology and surgery as part of an NIHR funded study. We are home to the Acute Frailty Network, and those interested in service developments at the national scale would be welcome to get involved. Orthogeriatrics There are now dedicated hip fracture wards and joint care with anaesthetists, orthopaedic surgeons and geriatricians. There are also consultants in metabolic bone disease that run clinics. Community work Community work will consist of reviewing patients in clinic who have been triaged to return to the community setting following an acute assessment described above. Additionally, primary care colleagues refer to outpatients for sub-acute reviews. You will work closely with local GPs with support from consultants to deliver post-acute, subacute, intermediate and rehabilitation care services. Stroke Medicine 24/7 thrombolysis and TIA services. The latter is considered one of the best in the UK and along with the high standard of vascular surgery locally means one of the best performances regarding carotid intervention

Security and privacy issues of physical objects in the IoT: Challenges and opportunities

In the Internet of Things (IoT), security and privacy issues of physical objects are crucial to the related applications. In order to clarify the complicated security and privacy issues, the life cycle of a physical object is divided into three stages of pre-working, in-working, and post-working. On this basis, a physical object-based security architecture for the IoT is put forward. According to the security architecture, security and privacy requirements and related protecting technologies for physical objects in different working stages are analyzed in detail. Considering the development of IoT technologies, potential security and privacy challenges that IoT objects may face in the pervasive computing environment are summarized. At the same time, possible directions for dealing with these challenges are also pointed out

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