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The aim of the research work presented in this paper is to investigate a mechanism that can recognise high level activities (for example, going for a walk, travelling on the bus, doing evening activity, etc.) and behaviour of low entropy people (people with regular daily life routines, e.g. elderly people with dementia, patients with regular routines) in order to help them improve their health related daily life activities by using wireless proximity data (e.g. Bluetooth, Wi‐Fi).

The paper adopted a tiered approach to recognise activities and behaviour. Higher level activities are divided into sub‐activities and tasks. Separating the tasks from the raw wireless proximity data is achieved by designing task separator (TASE) algorithm. TASE takes wireless proximity data as an input and separates it into different tasks. These detected tasks and the high level daily activity plans that are made in a planning language Asbru, are then fed into the activity recogniser that compares the detected tasks with the plans and recognises the high level activities that the user is performing.

The paper provides an insight to how only wireless proximity data can be utilised to recognise high level activities and behaviour of individuals. A number of scenarios and experiments are designed to prove the validity of the proposed methodology.

This paper focussed on relatively low entropy individuals with regular routines and behavioural patterns which can be improved by increasing the level of entropies in behavioural routines.

The paper includes implications for the utilisation in health care environments for elderly people and physically impaired individuals.

This paper provides a detailed and original study of algorithms and techniques that can be used to recognise high level activities and behaviour of individuals by using only wireless proximity data.

Recognizing daily life activities and human behaviour from contextual information is a challenging task. The purpose of the research work in this paper is to develop a system that can detect indoor and outdoor daily life activities of low entropy mobile people such as elderly people and patients with regular routines using non-intrusive sensor and contextual information.

A framework is proposed that utilises a hierarchical approach in which high-level activities are divided into sub-activities and tasks and recognises the high-level outdoor and indoor activities of daily life. Tasks are recognised at lower level from sensor data and then used by the “activity recogniser” at higher level to recognise the high-level activities. For outdoor activities recognition, wireless proximity data are used, whereas for indoor activities, object usage data obtained through radio frequency identification sensors are used.

For outdoor tasks, results have shown 100 per cent recognition for experiment 1 and a decrease in recognition from 100 to 82.7 per cent, respectively, for experiment 2-9 due to increase in the entropy of individual tasks. Outdoor activity recognition ranges from 84.1 to 100 per cent. For indoor tasks, generating alternative tasks sequences approach effectively recognised the single tasks that were conducted with objects without any order. Average indoor activity recognition rate remains above 90 per cent. The reason why this approach is able to detect the activities without their distinct features is the planning capability of the Asbru that is used in the modelling of high-level activities.

The novelty of this research work is a framework that utilises different types of sensor data and recognises both indoor and outdoor daily life activities of individuals.

This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge.

This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients.

A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches.

The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features.

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.

The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology‐based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse‐grained and fine‐grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.

Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad hoc static models, data scarcity and scalability. Analysis of the state‐of‐the‐art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context‐aware personalised activity‐based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind, which provides an integrated framework‐based on the unified conceptual backbone, i.e. activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability, which facilitates interoperability, reusability and intelligent processing at a higher level of automation.

The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology‐based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.

Speech recognition machines currently on the market are all built upon the same research foundation. The most important milestones on the road to present‐day systems are reviewed in this article based largely on an interview with Dr Roger Moore of the Royal Signals and Radar Establishment.

The purpose of this paper is to understand how changes in technological devices, implemented to increase productivity and enhance performance are translated by medical professions in their clinical work. As organizations become more technology dependent by digitalization, deeper understanding of change processes will enhance change outcomes.

A case study based on interviews, observations, on site and document analyses is undertaken to study the use of electronic speech recognizer (SR). An actor network theory (ANT) approach is used to address practice.

Doctors diversely adjust to the new technology. The use of the SR technology was negotiated and translated by the doctors. The technology was continuously re-designed and interacting with the human actors. In the translation process, powerful actors (doctors) influence outcome of changes, and thus, they affect the effectiveness of the change initiatives.

The theoretical approach enables a detailed and rich understanding of the sociology of technology. Future research should go deeper into case studies in other contexts.

Technology is not deterministic entities, and politicians and managers should pay attention to how technology interact with key actors in implementation of system (technology) changes. The design and use phases implicate on the effect of such changes.

In order to successfully manage change processes, powerful actors should be motivated to actively participate in the design and the implementation phases in order to design and redesign the functions and roles of technologies.

The theoretical approach (ANT) addresses technology according to the concept of sociomateriality. This approach enables understanding technology, people and organizations as entangled (integrated). The theoretical concepts developed knowledge to gain deeper and wider understanding of the role of technology in managing of performance and productivity initiatives.

A real potential exists for library use of voice technologies: as aids to the disabled or illiterate library user, as front‐ends for general library help systems, in online systems for commands or control words, and in many of the hands‐busy‐eyes‐busy activities that are common in libraries. Initially, these applications would be small, limited processes that would not require the more fluent human‐machine communication that we might hope for in the future. Voice technologies will depend on and benefit from new computer systems, advances in artificial intelligence and expert systems to facilitate their use and enable them to better circumvent present input and output problems. These voice systems will gradually assume more importance, improving access to information and complementing existing systems, but they will not likely revolutionize or dominate human‐machine communications or library services in the near future.

Mobile handheld devices, such as cellular phones and personal digital assistants, are inherently small and lack an intuitive and natural user interface. Speech recognition and synthesis technology can be used in mobile handheld devices to improve the user experience. The purpose of this paper is to describe a prototype system that supports multiple speech‐enabled applications in a mobile handheld device.

The main component of the system, the Program Manager, coordinates and controls the speech‐enabled applications. Human speech requests to, and responses from, these applications are processed in the mobile handheld device, to achieve the goal of human‐like interactions between the human and the device. In addition to speech, the system also supports graphics and text, i.e., multimodal input and output, for greater usability, flexibility, adaptivity, accuracy, and robustness. The paper presents a qualitative and quantitative evaluation of the prototype system. The Program Manager is currently designed to handle the specific speech‐enabled applications that we developed.

The paper determines that many human interactions involve not single applications but multiple applications working together in possibly unanticipated ways.

Future work includes generalization of the Program Manager so that it supports arbitrary applications and the addition of new applications dynamically. Future work also includes deployment of the Program Manager and the applications on cellular phones running the Android Platform or the Openmoko Framework.

This paper presents a first step towards a future human interface for mobile handheld devices and for speech‐enabled applications operating on those devices.

This study aims to examine how chief executive officer's (CEO) personality traits influence the corporate sustainability performance (CSP) of firms. The paper also examines the moderating effect of board power on this relationship.

Using a linguistic tool (IBM's Watson Personality Insight Service), the authors measured the personality traits of 229 CEOs from 176 firms from 2009 to 2018. Firm-level CSP are obtained from the Sustainalytics database. The hypotheses are tested using multiple regression analysis. The robustness of the results of the study is confirmed by addressing endogeneity concerns and by validating the measurement of CEO personality traits using Personality Recognizer, an alternative linguistic tool.

The results show that CEO personality traits of extraversion and neuroticism are significant predictors of CSP. The paper also identifies board power as a contingent factor that influences the suggested relationships.

Using upper echelon theory and cybernetic big five theory, this paper identifies CEO personality traits as important antecedents of corporate sustainability performance and adds to the micro-foundations of corporate sustainability literature. To the authors’ understanding, this is the first study that examines the influence of CEO personality on CSP using a comprehensive trait framework. The paper also demonstrates the usefulness of text-analytic tools to measure CEO personality traits, thereby contributing to the progress of upper echelon theory.