Issues in the Development of Conversation Dialog for Humanoid Nursing Partner Robots in Long-Term Care

The purpose of this chapter is to explore the issues of development of conversational dialog of robots for nursing, especially for long-term care, and to forecast humanoid nursing partner robots (HNRs) introduced into clinical practice. In order to satisfy the required performance of HNRs, it is important that anthropomorphic robots act with high-quality conversational dialogic functions. As for its hardware, by allowing independent range of action and degree of freedom, the burden of quality exerted in human-robot communication is reduced, thereby unburdening nurses and professional caregivers. Furthermore, it is critical to develop a friendlier type of robot by equipping it with non-verbal emotive expressions that older people can perceive. If these functions are conjoined, anthropomorphic intelligent robots will serve as possible instructors, particularly for rehabilitation and recreation activities of older people. In this way, more than ever before, the HNRs will play an active role in healthcare and in the welfare fields

Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local point clouds are matched to a global tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 200 m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12 cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5 m/s. The accuracy and speed limit are realistic during forest operations.</p

Human-like arm motion generation: a review

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.FCT Project UID/MAT/00013/2013FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Calibrating mini-mental state examination scores to predict misdiagnosed dementia patients

Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot

In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values.This research was financed by the plant of Mercedes-Benz Vitoria through the PIF program to develop an intelligent production. Moreover, The Regional Development Agency of the Basque Country (SPRI) is gratefully acknowledged for their economic support through the research project “Motor de Accionamiento para Robot Guiado Automáticamente”, KK-2019/00099, Programa ELKARTEK

A Novel Multi-View Table Tennis Umpiring Framework

This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status. While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies. In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients

A decentralised secure and privacy-preserving e-government system

Electronic Government (e-Government) digitises and innovates public services to businesses, citizens, agencies, employees and other shareholders by utilising Information and Communication Technologies. E-government systems inevitably involves finance, personal, security and other sensitive information, and therefore become the target of cyber attacks through various means, such as malware, spyware, virus, denial of service attacks (DoS), and distributed DoS (DDoS). Despite the protection measures, such as authentication, authorisation, encryption, and firewalls, existing e-Government systems such as websites and electronic identity management systems (eIDs) often face potential privacy issues, security vulnerabilities and suffer from single point of failure due to centralised services. This is getting more challenging along with the dramatically increasing users and usage of e-Government systems due to the proliferation of technologies such as smart cities, internet of things (IoTs), cloud computing and interconnected networks. Thus, there is a need of developing a decentralised secure e-Government system equipped with anomaly detection to enforce system reliability, security and privacy. This PhD work develops a decentralised secure and privacy-preserving e-Government system by innovatively using blockchain technology. Blockchain technology enables the implementation of highly secure and privacy preserving decentralised applications where information is not under the control of any centralised third party. The developed secure and decentralised e-Government system is based on the consortium type of blockchain technology, which is a semi-public and decentralised blockchain system consisting of a group of pre-selected entities or organisations in charge of consensus and decisions making for the benefit of the whole network of peers. Ethereum blockchain solution was used in this project to simulate and validate the proposed system since it is open source and supports off-chain data storage such as images, PDFs, DOCs, contracts, and other files that are too large to be stored in the blockchain or that are required to be deleted or changed in the future, which are essential part of e-Government systems. This PhD work also develops an intrusion detection system (IDS) based on the Dendritic cell algorithm (DCA) for detecting unwanted internal and external traffics to support the proposed blockchain-based e-Government system, because the blockchain database is append-only and immutable. The IDS effectively prevent unwanted transactions such as virus, malware or spyware from being added to the blockchain-based e-Government network. Briefly, the DCA is a class of artificial immune systems (AIS) which was introduce for anomaly detection in computer networks and has beneficial properties such as self-organisation, scalability, decentralised control and adaptability. Three significant improvements have been implemented for DCA-based IDS. Firstly, a new parameters optimisation approach for the DCA is implemented by using the Genetic algorithm (GA). Secondly, fuzzy inference systems approach is developed to solve nonlinear relationship that exist between features during the pre processing stage of the DCA so as to further enhance its anomaly detection performance in e-Government systems. In addition, a multiclass DCA capable of detection multiple attacks is developed in this project, given that the original DCA is a binary classifier and many practical classification problems including computer network intrusion detection datasets are often associated with multiple classes. The effectiveness of the proposed approaches in enforcing security and privacy in e- Government systems are demonstrated through three real-world applications: privacy and integrity protection of information in e Government systems, internal threats detection, and external threats detection. Privacy and integrity protection of information in the proposed e- Government systems is provided by using encryption and validation mechanism offered by the blockchain technology. Experiments demonstrated the performance of the proposed system, and thus its suitability in enhancing security and privacy of information in e-Government systems. The applicability and performance of the DCA-based IDS in e Government systems were examined by using publicly accessible insider and external threat datasets with real world attacks. The results show that, the proposed system can mitigate insider and external threats in e-Government systems whilst simultaneously preserving information security and privacy. The proposed system also could potentially increase the trust and accountability of public sectors due to the transparency and efficiency which are offered by the blockchain applications

An Innovation-driven IT Governance Framework for Benefits Realisation and its Application to Public Sector

Information and communication technology (ICT or IT) provides benefits to an organisation. However, a large number of IT projects fail. The research literature shows extant governance frameworks are not adequately protecting against project failures and are not as effective on many of these IT projects as they should be. This thesis developed a new IT governance framework using an agile benefit management approach, aimed to achieve benefits realisation for any scale of IT projects, particularly for projects in the public sector such as Defence. The framework objectively targets digital transformation and technological agility, however, is shown in the thesis to assist in other enterprise challenges with IT acquisition and through-life management, such as cyber-resilience. The framework is based on many theories, principles, and practices, such as: Transaction Cost Economics Theory, Prospect Theory (Decision-Making Under Uncertainty), Reference Class Forecasting, Stratification and the Incremental Enlargement Principle and Fuzzy Logic. The framework is shown to be effective primarily through better informed decision-making. Benefits realisation is critical for information technology project success. The framework provides a systematic methodology on how to define economic benefit, technical benefit, and strategic benefit. It provides benefit realisation measures through fuzzy inference system, and it provides decision support based on the benefit performance measures and dis-benefit risk management. When compared to industry-based frameworks for IT acquisition and sustainment this new framework is unique because it includes all internal and external stakeholders such as users, providers, industry, and academia to continuously collaborate for innovating, iterating, and evaluating technology for realisation of benefits to achieve the organisational goals and objectives. The proof of concept has been conducted through a detailed case study in the Defence public sector, and several critiques of IT reform in other public sector applications where difficulties are occurring. Organisations will be able to use this framework for a more rapid and assured uptake of emerging technologies of the Fourth Industrial Revolution into their technology stack. Examples of some of these emerging technologies are AI, machine learning, geo-spatial, block chain, cognitive and brain computing, cloud computing, data echo system, and cybersecurity