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Mesh Networking over time has gained increased traction within the Internet of Things. It enables mass collection of data while also establishing new signal transmission pathways to avoid unnecessary depletion factors such as Material Penetration or Environmental factors like Weather generated interference. However, Mesh Networks have a lower power consumption efficiency due to the transceiver nodes power division. This has a direct impact on the Received Signal Strength Indicator (RSSI) measurement. This power difference can, therefore, result in inaccuracies for applications that employ an RSSI Based location algorithm for user tracking to occur as these are designed for Peer-to-Peer (P2P) communication. Additionally, through chaining the source RSSI to any given endpoint it also becomes possible to provide an estimated endpoint destination distance measurement which could be applied for user location services within Emergency Response situations. This study, therefore, investigates the offset generated by Mesh networking. An average loss of 5.75 dBm for reception and an average loss of 6.577dBm for transmission was identified within the bridging node due to the power split. Additionally, when chaining the RSSI across 30.48 m, this offset was found to present an average decrease of power signal strength efficiency of 202% within the system setup when compared to that of the P2P setup.

Objectives: To describe and critique a systematic multidisciplinary approach to user engagement, and selection and evaluation of sensor technologies for development of a sensor-based Digital Toolkit for assessment of movement in children with cerebral palsy (CP).Methods: A sequential process was employed comprising three steps: Step 1: define user requirements, by identifying domains of interest; Step 2: map domains of interest to potential sensor technologies; and Step 3: evaluate and select appropriate sensors to be incorporated into the Digital Toolkit. The process employed a combination of principles from frameworks based in either healthcare or technology design.Results: A broad range of domains were ranked as important by clinicians, patients and families, and industry users. These directly informed the device selection and evaluation process that resulted in three sensor-based technologies being agreed for inclusion in the Digital Toolkit, for use in a future research study.Conclusion: This report demonstrates a systematic approach to user engagement and device selection and evaluation during the development of a sensor-based solution to a healthcare problem. It also provides a narrative on the benefits of employing a multidisciplinary approach throughout the process. This work uses previous frameworks for evaluating sensor technologies and expands on the methods used for user engagement.

BACKGROUND There is a global challenge related to the increase in the number of people with dementia (PwD) and the diminishing capacity of governments, health systems, and caregivers to provide the best care for them. Cost-effective technology solutions that enable and ensure a good quality of life for PwD via monitoring patients and interventions have been investigated comprehensively in the literature. OBJECTIVE The objective of this study was to investigate the challenges with the design, deployment and functioning of a Smart Home In a Box (SHIB) approach to monitoring PwD wellbeing within a care home. This could then support future implementations and present further opportunities for the SHIB approach. An important consideration was that most care homes do not have the appropriate infrastructure for installing and using ambient sensors. METHODS The SHIB was evaluated via installation in the rooms of 3 PwD with varying degrees of dementia. Sensors from the SHIB were installed in the rooms of the PwD to test the capabilities of these sensors for detecting Activities of Daily Living (ADLs). The sensors used were: (i) thermal sensors, (ii) contact sensors, (iii) Passive Infrared (PIR) sensors, and (iv) audio level sensors. Data was collected, stored and handled using a ‘SensorCentral’ data platform. RESULTS This study highlighted challenges and opportunities that should be considered when installing and using a SHIB approach in a dementia care home. Lessons learned from this investigation are presented in addition to recommendations that could support the wellbeing monitoring of PwD. This study also presents results from initial data analysis and demonstrates how activities, falls and abnormal behaviors could be detected and acted upon. CONCLUSIONS This study was conducted by Ulster University’s Pervasive Computing Research Group (PCRG) as part of the Northern Ireland Connected Health Innovation Centre (NI-CHIC) project. The design, deployment and functioning of the SHIB approach within Kirk House Care Home in Belfast provided the community with useful lessons, that will continue to be applied to improve future implementations of the SHIB approach. Several challenges arose during the installation, functioning and data collection at Kirk House Care Home, for example, regarding the adaption of technology to a building that was not originally designed for the integration of ambient sensors. The main findings of this study are: (i) most care home buildings were not originally designed to appropriately install ambient sensors, and (ii) installation of SHIB sensors should be adapted depending on the specific case of the care home where they will be installed. It was acknowledged that in addition to care homes, the homes of dementia patients were also not designed for an appropriate integration with ambient sensors. Hence, another possible use of a SHIB approach.

Research interests spanning numerous domains increasingly rely upon computational systems which can store and process a large volume of variable data that is stored at high velocity –representing a big data problem. This is particularly notable within the domain of ubiquitous and pervasive computing. This domain increasingly relies on storage and retrieval of sensor data to enable outcomes such as predictive analytics and activity recognition. Several current big data platforms exist; however, they have a range of deficiencies including lack of generic interoperability with agnostic sensors and an absence of features supporting academic research. Due to these deficiencies a custom, research oriented, high performance, and big data platform was devised and implemented. This platform is called SensorCentral and is presented within this paper. SensorCentral provides a framework which enables interoperability with a large range of agnostic sensor devices whilst simultaneously providing features which support research. Research supporting features include; facility to define experiments, ability to annotate experimental instances via purpose-built mobile applications, integrated machine learning functionality, facility to export data sets, rule-based classification and an extensible platform. The flagship implementation of this platform has been in operation for over 28 months within a University research group and has been successfully integrated with a range of sensors from a variety of manufacturers. This implementation currently stores over 850 million records and has been central to several research and industrial projects. Future work will integrate this platform into the open data initiative enabling collaboration with the international community of researchers.

Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision.

Patients with dementia often suffer from stress episodes that escalate to anxiety. This paper presents a feasibility study of using environmental smart microphones to detect anxiety in patients with dementia. It is based on the identified auditory manifestations of anxiety. To have a better understanding of the anxiety manifestations in patients with dementia, 70 diagnosed patients were observed in-situ and 4 caregivers were interviewed. The design of an environmental smart microphone called AnxiCare has been developed and it is introduced. Feasibility interviews regarding the use of AnxiCare were conducted with caregivers at a care residence in Spain. Results from the observations, interviews and a preliminary validation are presented.

Data annotation is the process of segmenting and labelling any type of data (images, audio or text). It is an important task for producing reliable datasets that can be used to train machine learning algorithms for the purpose of Activity Recognition. This paper presents the work in progress towards a semi-automated approach for collecting and annotating audio data from simple sounds that are typically produced at home when people perform daily activities, for example the sound of running water when a tap is open. We propose the use of an app called ISSA (Intelligent System for Sound Annotation) running on smart microphones to facilitate the semi-automated annotation of audible activities. When a sound is produced, the app tries to classify the activity and notifies the user, who can correct the classification and/or provide additional information such as the location of the sound. To illustrate the feasibility of the approach, an initial version of ISSA was implemented to train an audio classifier in a one-bedroom apartment.

As the TV experience evolves to provide customers with a richer, more interactive experience across multiple devices, it is increasingly important to make the best use of subjective and objective techniques to inform the development of TV user interfaces. This paper describes the design of a new experiment to evaluate the TV customer experience using eye tracking technology, focused on the BT Player, a visually-rich Video-on-Demand application. Eye tracking provides an objective assessment which does not interfere with the natural interaction of the user with the system. The evaluation will capture a unique data set through the observation of test subjects exposed to a prioritised set of test conditions presented within a controlled environment. The paper presents the design of the experiments, including requirements capture, hardware and software setup, experimental protocol, data collection and analysis. The paper also outlines the challenges posed by the dynamic nature of the content and user interaction with the TV interface.

With the increasing abundance of technologies and smart devices, equipped with a multitude of sensors for sensing the environment around them, information creation and consumption has now become effortless. This, in particular, is the case for photographs with vast amounts being created and shared every day. For example, at the time of this writing, Instagram users upload 70 million photographs a day. Nevertheless, it still remains a challenge to discover the “right” information for the appropriate purpose. This paper describes an approach to create semantic geospatial metadata for photographs, which can facilitate photograph search and discovery. To achieve this we have developed and implemented a semantic geospatial data model by which a photograph can be enrich with geospatial metadata extracted from several geospatial data sources based on the raw low-level geo-metadata from a smartphone photograph. We present the details of our method and implementation for searching and querying the semantic geospatial metadata repository to enable a user or third party system to find the information they are looking for.

Safety and security rank highly in the priorities of older people on both an individual and policy level. Older people are commonly targeted as victims of doorstep crime, as they can be perceived as being vulnerable. As a result, this can have a major effect on the victim's health and wellbeing. There have been numerous prevention strategies implemented in an attempt to combat and reduce the number of doorstep crimes. There is, however, little information available detailing the effectiveness of these strategies and how they impact on the fear of crime, particularly with repeat victims. There is therefore clear merit in the creation and piloting of a technology based solution to combat doorstep crime. This paper presents a developed solution to provide increased security for older people within their home.

Adoption of the pervasive and ubiquitous computing paradigm is increasing. One application of this paradigm is Ambient Assistive Living where ICT-based assistive services are deployed in an environment. In this study, a Smart Home in a Box (SHIB) has been devised as an AAL solution. This SHIB relies upon Internet of Things (IoT) devices, such as thermal vision sensors, within an environment to function. Each SHIB deployment consists of IoT devices taken from a common pool of devices. The exact device manifest of each SHIB deployment will change as dictated by end-user requirements. Additionally, this pool will grow and expand when devices are removed or added from previous SHIB deployments. To efficiently manage this pool of devices, and SHIB deployments, a remote management solution is required. Current remote management solutions have a range of deficiencies that render them unsuitable for use with the current SHIB platform. To address these deficiencies, a secure mechanism to manage the IoT devices has been devised. This paper presents this mechanism and evaluates its applicability to SHIB deployments. The evaluation focused on assessing the security of the mechanism and the viability of its Low-Power Wireless Wide-Area Network-based communications. This evaluation showed that the proposed solution was reliable and suitable for the intended deployment scenario. An extended evaluation and system improvements have been identified and proposed within future work.

With the growing change to a more aged population and greater strain on our health care services, due to higher costs and demand, there is a growing need to develop solutions to help solve this health care crisis. Generally older people prefer to keep their independence as they age, which gives them a better quality of life. Independent living can be achieved both within care homes and at home, and with the assistance of technology this can be achieved more easily. In this study a Smart Home in a Box (SHIB) solution has been developed to support Ambient Assistive Living (AAL). This paper presents a solution for an interface to interact and be notified by the SHIB platform. A usability evaluation of the developed solution is also presented in the paper. This evaluation asked 8 people consisting of management staff and a Family and Relatives group from a care home, to determine how older people would feel about a solution like this. Overall the participants of the usability evaluation felt the developed solution would benefit older people living at home.

Purpose – Improvements and portability of technologies and smart devices have enabled a rapid growth in the amount of user-generated media such as photographs and videos. Whilst various media generation and management systems exist, it still remains a challenge to discover the right information, for the right purpose. This paper aims to propose an approach to reverse geocoding by cross-referencing multiple geospatial data sources to enable the enrichment of media and therefore enable better organisation and searching of the media to create an overall picture about places. Design/methodology/approach – The paper presents a system architecture that incorporates the proposed approach to aggregate several geospatial databases to enrich geo-tagged media with human readable information, which will further enable the goal of creating an overall picture about places. The approach enables the semantic information relating to point of interest. Findings – Implementation of the proposed approach shows that a single geospatial data source does not contain enough information to accurately describe the high-level geospatial information for geocoded multimedia. However, fusing several geospatial data sources together enables richer, more accurate high-level geospatial information to be tagged to the geocoded multimedia. Originality/value – The contribution in this paper shows that high-level geospatial information can be retrieved from many data sources and fused together to enrich geocoded multimedia which can facilitate better searching and retrieval of the multimedia.

Children with physical impairments, ranging from impaired mobility to very limited mobility, often require mobility aids to compensate for these difficulties. These impairments can adversely affect the child to varying degrees and have an impact on their health and wellbeing. It is estimated that 30 %–40 % of medical interventions have no reported evidence base and another 20 % of interventions delivered are ineffective. Clinicians are under increasing pressure to provide evidence of the effectiveness of prescribed treatments and products. Therefor there is a need to provide clinicians with empirical data that evidences practice and provides a quantified assessment of treatment efficacy through data gathering in both real-time and longitudinally, combined with data analytics to further develop treatment strategies. This paper presents a system to assist and enable clinicians to analyze and asses the effectiveness and usage of prescribed treatments for physically impaired children. The system achieves this through the use of a gamified data collection app and a web portal to analyze and present summarized measures of gait.

In today's world of internet connected devices and smart phones, it has become effortless to create and consume vast amounts of information. This is particularly the case with photographs, with vast amounts being created and shared online every day. Never-the-less, it still remains a challenge to discover the "right" information for the appropriate purpose. This paper describes and discusses the testing and evaluation of the MediaPlace system with the well-known dataset YFCC-100M, which contains 48 million geospatial geotagged photographs, from Flickr produced by Yahoo. MediaPlace is a system which we have developed to automatically enrich geotagged photographs with semantic geospatial information derived from several online geospatial datasets.

This paper describes an approach to model data extracted from several geospatial data sources based on the geo-metadata from a smartphone photograph. The model enables the geospatial data sources to be cross referenced and inferences made about the relationships between the data and the photograph. The service provided by our system takes a photograph and generates semantic geospatial enrichments that can be annotated to the photograph.

Improvements and portability of technologies and smart devices has enabled rapid growth in the amount of user generated media such as photographs and videos. Whilst various media generation and management systems exist it still remains a challenge to discover the "right" metadata information for the right purpose. This paper describes an approach to extract relevant geospatial information through reverse geocoding in addition to cross-referencing several public geospatial data sources. The extracted geospatial information can be used to enable enrichment of media with rich semantic geo-metadata and therefore enable improved organisation and searching of the media. Central to the system is the cross-referencing and data fusion of several public geospatial datasets to determine the most relevant Points of Interest and extract their relevant features. These relevant Points of Interest and features are subsequently used to annotate media with human readable information, leading to enriched media repositories. The system has been implemented as a client/server architecture, with a web interface for the client front end and Java for the backend server side processing. Details of the implementation are discussed. Testing in a scenario has been undertaken and a discussion of the testing technique and results is presented. The initial results show the system to be effective at fusing several public geospatial datasets and extracting relevant geo-metadata.