Przemyslaw Woznowski

A multi-modal sensor infrastructure for healthcare in a residential environment

Ambient Assisted Living (AAL) systems based on sensor technologies are seen as key enablers to an ageing society. However, most approaches in this space do not provide a truly generic ambient space - one that is not only capable of assisting people with diverse medical conditions, but can also recognise the habits of healthy habitants, as well as those with developing medical conditions. The recognition of Activities of Daily Living (ADL) is key to the understanding and provisioning of appropriate and efficient care. However, ADL recognition is particularly difficult to achieve in multi-resident spaces; especially with single-mode (albeit carefully crafted) solutions, which only have limited capabilities. To address these limitations we propose a multi-modal system architecture for AAL remote healthcare monitoring in the home, gathering information from multiple, diverse (sensor) data sources. In this paper we report on developments made to-date in various technical areas with respect to critical issues such as cost, power consumption, scalability, interoperability and privacy.

Classification and suitability of sensing technologies for activity recognition

Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply different AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a different angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for home-based AR algorithms, it highlights the problem of technology-driven cycle of development in this area.

A Human Activity Recognition Framework for Healthcare Applications: Ontology, Labelling Strategies, and Best Practice

Human Activity Recognition (AR) is an area of great importance for health and well-being applications including Ambient Intelligent (AmI) spaces, Ambient Assisted Living (AAL) environments, and wearable healthcare systems. Such intelligent systems reason over large amounts of sensor-derived data in order to recognise users’ actions. The design of AR algorithms relies on ground-truth data of sufficient quality and quantity to enable rigorous training and validation. Ground-truth is often acquired using video recordings which can produce detailed results given the appropriate labels. However, video annotation is not a trivial task and is, by definition, subjective. In addition, the sensitive nature of the recordings has to be foremost in minds of the researchers to protect the identity and privacy of participants. In this paper, a hierarchical ontology for the annotation of human activity recognition in the home is proposed. Strategies that support different levels of granularity are presented enabling consistent, and repeatable annotations for training and validating activity recognition algorithms. Best practice regarding the handling of this type of sensitive data is discussed.

Rule-Based Real-Time ADL Recognition in a Smart Home Environment

This paper presents a rule-based approach for both offline and real-time recognition of Activities of Daily Living (ADL), leveraging events produced by a non-intrusive multi-modal sensor infrastructure deployed in a residential environment. Novel aspects of the approach include: the ability to recognise arbitrary scenarios of complex activities using bottom-up multi-level reasoning, starting from sensor events at the lowest level; an effective heuristics-based method for distinguishing between actual and ghost images in video data; and a highly accurate indoor localisation approach that fuses different sources of location information. The proposed approach is implemented as a rule-based system using Jess and is evaluated using data collected in a smart home environment. Experimental results show high levels of accuracy and performance, proving the effectiveness of the approach in real world setups.

SPLASH: Smart-Phone Logging App for Sustaining Hydration Enabled by NFC

Maintaining good hydration is crucial for adequate physical and mental performance for all human beings. In this paper we present SPLASH, an Android app that enables users to set daily goals and to keep track of their liquid intake through a combination of smart-phone NFC technology and NFC-tagged cups. We conducted several experiments to verify the robustness of the technology, which indicated that the selected NFC tags had acceptable robustness, operational distance and good penetration ability to meet the intended requirements for monitoring hydration. To further assess the feasibility of our concept, we evaluated SPLASH with ten users who gave feedback on its usability. We discuss the current prototypes advantages and limitations, as well as possible improvements and potential capabilities. At the end of this paper, we propose additional healthcare application scenarios for our concept.

Evaluating the use of voice-enabled technologies for ground-truthing activity data

Reliably discerning human activity from sensor data is a nontrivial task in ubiquitous computing, which is central to enabling smart environments. Ground-truth acquisition techniques for such environments can be broadly divided into observational and self-reporting approaches. In this paper we explore one self-reporting approach, using speech-enabled logging to generate ground-truth data. We report the results of a user study in which participants (N=12) used both a smart-watch and a smart-phone app to record their activities of daily living using primarily voice, then answered questionnaires comprising the System Usability Scale (SUS) as well as open ended questions about their experiences. Our findings indicate that even though user satisfaction with the voice-enabled activity logging apps was relatively high, this approach presented significant challenges regarding compliance, effectiveness, and privacy. We discuss the implications of these findings with a view to offering new insights and recommendations for designing systems for ground-truth acquisition ‘in the wild’.

Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation

Ubiquitous eHealth systems based on sensor technologies are seen as key enablers in the effort to reduce the financial impact of an ageing society. At the heart of such systems sit activity recognition algorithms, which need sensor data to reason over, and a ground truth of adequate quality used for training and validation purposes. The large set up costs of such research projects and their complexity limit rapid developments in this area. Therefore, information sharing and reuse, especially in the context of collected datasets, is key in overcoming these barriers. One approach which facilitates this process by reducing ambiguity is the use of ontologies. This article presents a hierarchical ontology for activities of daily living (ADL), together with two use cases of ground truth acquisition in which this ontology has been successfully utilised. Requirements placed on the ontology by ongoing work are discussed.

Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective

Delivering effortless interactions and appropriate interventions through pervasive systems requires making sense of multiple streams of sensor data. This is particularly challenging when these concern people’s natural behaviours in the real world. This paper takes a multidisciplinary perspective of annotation and draws on an exploratory study of 12 people, who were encouraged to use a multi-modal annotation app while living in a prototype smart home. Analysis of the app usage data and of semi-structured interviews with the participants revealed strengths and limitations regarding self-annotation in a naturalistic context. Handing control of the annotation process to research participants enabled them to reason about their own data, while generating accounts that were appropriate and acceptable to them. Self-annotation provided participants an opportunity to reflect on themselves and their routines, but it was also a means to express themselves freely and sometimes even a backchannel to communicate playfully with the researchers. However, self-annotation may not be an effective way to capture accurate start and finish times for activities, or location associated with activity information. This paper offers new insights and recommendations for the design of self-annotation tools for deployment in the real world.

Measuring functional activities of patients in a stroke unit: Comparison of a sensor based Real Time Location System with the Observational Behaviour Mapping Technique [Poster Abstract]

This abstract looks at establishing the effectiveness of combined walking and cognitive training in long-term stroke through a series of N-of-1 studiesIntroduction: To overcome the limitations of the current activity monitoring methods and to effectively investigate early stage functional activities post stroke, we are developing a new computerised Real Time Location System (RTLS).Having previously established excellent RTLS reliability (Intraclass Correlation Coefficients≥0.90), this study aims to determine its validity by comparing it to the Observational Behaviour Mapping Technique (OBMT). Methods: All rooms routinely accessed by patients are fitted with infra-red room locators which send their location codes to specialised Radio-Frequency Identification (RFID) tags. The RFID tags that have in-built motion sensors transmit their location and movement signals to a computer. All participating patients and staff members wear the tags and additional tags are attached to equipment like walking-aids and wheelchairs. Simultaneously, on various days, OBMT is being used to record patients’ location, interaction and activity every ten minutes. Descriptive statistics and Pearson’s Correlation Coefficients (PCCs) are being used for statistical analysis. Results: So far, we have analysed the results for the location category of three patients and have observed only small differences between the two systems for mean time spent in own room (diff=7min; OBMT=550, RTLS=557) and in therapy room (diff=4min; OBMT=90, RTLS=86). Further analysis will involve comparing the methods for time spent in categories like interacting with staff members, doing therapeutic and non therapeutic activities and using equipment. Conclusion: Based on results, we hope to determine that the RTLS is a valid system for continuous, unobtrusive patient activity measurement and can provide much needed quantifiable information about functional recovery post stroke.order Oral Presentations Acute Care 1 Case Reports and Interesting Cases Secondary Prevention Rehabilitation 1 Audit 1 Education Good Practice in User Involvement Health Economics and Impact of Stroke Service Development and Delivery 1 Other Communication Swallowing Nutrition Vision Social and Community Care Vascular Cognitive Impairment Acute Care 2 Rehabilitation 2 Cognitive, Emotional and Psychological Genetics Basic Neuroscience Imaging Audit 2 Service Development and Delivery 2 TIA Primary Prevention Risk Factors of Stroke Exercise After Stroke Assistive Technology IJS_960_Front index.indd iv 11/1/2012 6:33:11 PM