A Smart Modular Wireless System for Condition Monitoring Data Acquisition

Smart sensors, big data, the cloud and distributed data processing are some of the most interning changes in the way we collect, manage and treat data in recent years. These changes have not significantly influenced the common practices in condition monitoring for shipping. In part this is due to the reduced trust in data security, data ownership issues, lack of technological integration and obscurity of direct benefit. This paper presents a method of incorporating smart sensor techniques and distributed processing in data acquisition for condition monitoring to assist decision support for maintenance actions addressing these inhibitors

Vibration edge computing in maritime IoT

IoT and the Cloud are among the most disruptive changes in the way we use data today. These changes have not significantly influenced practices in condition monitoring for shipping. This is partly due to the cost of continuous data transmission. Several vessels are already equipped with a network of sensors. However, continuous monitoring is often not utilised and onshore visibility is obscured. Edge computing is a promising solution but there is a challenge sustaining the required accuracy for predictive maintenance. We investigate the use of IoT systems and Edge computing, evaluating the impact of the proposed solution on the decision making process. Data from a sensor and the NASA-IMS open repository were used to show the effectiveness of the proposed system and to evaluate it in a realistic maritime application. The results demonstrate our real-time dynamic intelligent reduction of transmitted data volume by without sacrificing specificity or sensitivity in decision making. The output of the Decision Support System fully corresponds to the monitored system's actual operating condition and the output when the raw data are used instead. The results demonstrate that the proposed more efficient approach is just as effective for the decision making process

mini-ELSA: using Machine Learning to improve space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0

In previous work a novel Edge Lightweight Searchable Attribute-based encryption (ELSA) method was proposed to support Industry 4.0 and specifically Industrial Internet of Things applications. In this paper, we aim to improve ELSA by minimising the lookup table size and summarising the data records by integrating Machine Learning (ML) methods suitable for execution at the edge. This integration will eliminate records of unnecessary data by evaluating added value to further processing. Thus, resulting in the minimization of both the lookup table size, the cloud storage and the network traffic taking full advantage of the edge architecture benefits. We demonstrate our mini-ELSA expanded method on a well-known power plant dataset. Our results demonstrate a reduction of storage requirements by 21% while improving execution time by 1.27x

Evaluation of home-based rehabilitation sensing systems with respect to standardised clinical tests

With increased demand for tele-rehabilitation, many autonomous home-based rehabilitation systems have appeared recently. Many of these systems, however, suffer from lack of patient acceptance and engagement or fail to provide satisfactory accuracy; both are needed for appropriate diagnostics. This paper first provides a detailed discussion of current sensor-based home-based rehabilitation systems with respect to four recently established criteria for wide acceptance and long engagement. A methodological procedure is then proposed for the evaluation of accuracy of portable sensing home-based rehabilitation systems, in line with medically-approved tests and recommendations. For experiments, we deploy an in-house low-cost sensing system meeting the four criteria of acceptance to demonstrate the effectiveness of the proposed evaluation methodology. We observe that the deployed sensor system has limitations in sensing fast movement. Indicators of enhanced motivation and engagement are recorded through the questionnaire responses with more than 83% of the respondents supporting the system’s motivation and engagement enhancement. The evaluation results demonstrate that the deployed system is fit for purpose with statistically significant ( ϱc>0.99 , R2>0.94 , ICC>0.96 ) and unbiased correlation to the golden standard

Factors that contribute to the use of stroke self-rehabilitation technologies : a review

Background: Stroke is increasingly one of the main causes of impairment and disability. Contextual and empirical evidence demonstrate that, mainly due to service delivery constraints, but also due to a move toward personalized health care in the comfort of patients’ homes, more stroke survivors undergo rehabilitation at home with minimal or no supervision. Due to this trend toward telerehabilitation, systems for stroke patient self-rehabilitation have become increasingly popular, with many solutions recently proposed based on technological advances in sensing, machine learning, and visualization. However, by targeting generic patient profiles, these systems often do not provide adequate rehabilitation service, as they are not tailored to specific patients’ needs. Objective: Our objective was to review state-of-the-art home rehabilitation systems and discuss their effectiveness from a patient-centric perspective. We aimed to analyze engagement enhancement of self-rehabilitation systems, as well as motivation, to identify the challenges in technology uptake. Methods: We performed a systematic literature search with 307,550 results. Then, through a narrative review, we selected 96 sources of existing home rehabilitation systems and we conducted a critical analysis. Based on the critical analysis, we formulated new criteria to be used when designing future solutions, addressing the need for increased patient involvement and individualism. We categorized the criteria based on (1) motivation, (2) acceptance, and (3) technological aspects affecting the incorporation of the technology in practice. We categorized all reviewed systems based on whether they successfully met each of the proposed criteria. Results: The criteria we identified were nonintrusive, nonwearable, motivation and engagement enhancing, individualized, supporting daily activities, cost-effective, simple, and transferable. We also examined the motivation method, suitability for elderly patients, and intended use as supplementary criteria. Through the detailed literature review and comparative analysis, we found no system reported in the literature that addressed all the set criteria. Most systems successfully addressed a subset of the criteria, but none successfully addressed all set goals of the ideal self-rehabilitation system for home use. Conclusions: We identified a gap in the state-of-the-art in telerehabilitation and propose a set of criteria for a novel patient-centric system to enhance patient engagement and motivation and deliver better self-rehabilitation commitment

Prediction of wear rates of UHMWPE bearing in hip joint prosthesis with support vector model and grey wolf optimization

One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness (Ra) values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments

Data science enabled rehabilitation

Previously held under moratorium from 10th June 2021 until 12th June 2023.Stroke is a main cause of impairment/disability. More stroke survivors undergo unsupervised home rehabilitation. Autonomous self-rehabilitation systems using sensing and machine learning are not tailored to patients’ needs. Based on a systematic narrative literature review, home-based rehabilitation systems were taxonomized and new design criteria were formulated for increased patient engagement enhancement and individualism. No system that addresses all the criteria was found in literature. An in-house low-cost home-based rehabilitation Ambient Intelligence (AmI) system was deployed meeting the criteria, and an accuracy evaluation method proposed, in line with medically approved tests. The Timed Up and Go (TUG) and Five Time Sit To Stand (FTSTS) tests evaluate daily living activity performance in the presence/development of comorbidities. The AmI-driven system complies with Accountability, Responsibility, and Transparency (ART) requirements for wider acceptability. A method is presented for generating synthetic datasets complementing experimental observations mitigating bias present due to practical limitations. Also, an incremental hybrid machine learning algorithm is proposed. It combines ensemble learning and hybrid stacking using extreme gradient boosted decision trees and k-nearest neighbours to meet individualisation, and ART requirements while maintaining low computation footprint. The proposed approach was based on the criteria: nonintrusive, nonwearable, motivation and engagement enhancing, individualized, supporting daily activities, cost-effective, simple, and transferable. The motivation method, suitability for elderly, and intended use were examined as supplementary criteria. Indicators of enhanced motivation and engagement, through questionnaire responses, demonstrate that >83% of participants support the proposed system’s motivation and engagement enhancement. The system is fit for purpose with statistically significant (ϱc>0.99, R2 >0.94, ICC>0.96) and unbiased correlation to the gold standard. The model reaches up to 100% accuracy for FTSTS and TUG in predicting associated patient medical condition, and 100% or 83.13%, respectively, in predicting area of difficulty in the segments of the test. Results show an improvement of 5% and 15% for FTSTS and TUG, over previous intrusive approaches. Keywords: Home-based rehabilitation systems, Stroke rehabilitation, Telerehabilitation, Patient participation, Motivation, Comparative effectiveness research, Automated timed up and go test, Automated five time sit to stand test, Self-evaluation, Evaluation of sensor systems, Non-intrusive sensing, Sensing for health, Accountable Artificial Intelligence, Responsible Artificial Intelligence, Transparent Artificial Intelligence, Hybrid ensemble learning, Patient-centric individualised rehabilitationStroke is a main cause of impairment/disability. More stroke survivors undergo unsupervised home rehabilitation. Autonomous self-rehabilitation systems using sensing and machine learning are not tailored to patients’ needs. Based on a systematic narrative literature review, home-based rehabilitation systems were taxonomized and new design criteria were formulated for increased patient engagement enhancement and individualism. No system that addresses all the criteria was found in literature. An in-house low-cost home-based rehabilitation Ambient Intelligence (AmI) system was deployed meeting the criteria, and an accuracy evaluation method proposed, in line with medically approved tests. The Timed Up and Go (TUG) and Five Time Sit To Stand (FTSTS) tests evaluate daily living activity performance in the presence/development of comorbidities. The AmI-driven system complies with Accountability, Responsibility, and Transparency (ART) requirements for wider acceptability. A method is presented for generating synthetic datasets complementing experimental observations mitigating bias present due to practical limitations. Also, an incremental hybrid machine learning algorithm is proposed. It combines ensemble learning and hybrid stacking using extreme gradient boosted decision trees and k-nearest neighbours to meet individualisation, and ART requirements while maintaining low computation footprint. The proposed approach was based on the criteria: nonintrusive, nonwearable, motivation and engagement enhancing, individualized, supporting daily activities, cost-effective, simple, and transferable. The motivation method, suitability for elderly, and intended use were examined as supplementary criteria. Indicators of enhanced motivation and engagement, through questionnaire responses, demonstrate that >83% of participants support the proposed system’s motivation and engagement enhancement. The system is fit for purpose with statistically significant (ϱc>0.99, R2 >0.94, ICC>0.96) and unbiased correlation to the gold standard. The model reaches up to 100% accuracy for FTSTS and TUG in predicting associated patient medical condition, and 100% or 83.13%, respectively, in predicting area of difficulty in the segments of the test. Results show an improvement of 5% and 15% for FTSTS and TUG, over previous intrusive approaches. Keywords: Home-based rehabilitation systems, Stroke rehabilitation, Telerehabilitation, Patient participation, Motivation, Comparative effectiveness research, Automated timed up and go test, Automated five time sit to stand test, Self-evaluation, Evaluation of sensor systems, Non-intrusive sensing, Sensing for health, Accountable Artificial Intelligence, Responsible Artificial Intelligence, Transparent Artificial Intelligence, Hybrid ensemble learning, Patient-centric individualised rehabilitatio

Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation

Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation and, specifically, artificial ambient intelligence with individualisation to support engagement and motivation. Artificial intelligence must also comply with accountability, responsibility, and transparency (ART) requirements for wider acceptability. This paper presents such a patient-centric individualised home-based rehabilitation support system. To this end, the Timed Up and Go (TUG) and Five Time Sit To Stand (FTSTS) tests evaluate daily living activity performance in the presence or development of comorbidities. We present a method for generating synthetic datasets complementing experimental observations and mitigating bias. We present an incremental hybrid machine learning algorithm combining ensemble learning and hybrid stacking using extreme gradient boosted decision trees and k-nearest neighbours to meet individualisation, interpretability, and ART design requirements while maintaining low computation footprint. The model reaches up to 100% accuracy for both FTSTS and TUG in predicting associated patient medical condition, and 100% or 83.13%, respectively, in predicting area of difficulty in the segments of the test. Our results show an improvement of 5% and 15% for FTSTS and TUG tests, respectively, over previous approaches that use intrusive means of monitoring such as cameras

Individualised responsible Artificial Intelligence for home-based rehabilitation

Socioeconomic reasons post COVID-19 demand unsupervised home-based rehabilitation and specifically, Artificial Ambient Intelligence with individualisation to support engagement and motivation. Artificial Intelligence must also comply with Accountability, Responsibility, and Transparency (ART) requirements for wider acceptability. This paper presents such a patient-centric individualised home-based rehabilitation support system. To this end the Timed Up and Go (TUG) and Five Time Sit To Stand (FTSTS) tests evaluate daily living activity performance in the presence or development of comorbidities. We present a method for generating synthetic datasets complementing experimental observations and mitigating bias. We present an incremental hybrid machine learning algorithm combining ensemble learning and hybrid stacking using extreme gradient boosted decision trees and k-nearest neighbours to meet individualisation, interpretability, and ART design requirements while maintaining low computation footprint. The model reaches up to 100% accuracy for both FTSTS and TUG in predicting associated patient medical condition, and 100% or 83.13%, respectively, in predicting area of difficulty in the segments of the test. Our results show an improvement of 5% and 15% for FTSTS and TUG tests, respectively, over previous approaches that use intrusive means of monitoring such as cameras

Secure Data Transfer and Provenance for Distributed Healthcare

The rise of the Internet of Things (IoT) has enabled a shift to a smart, remote, and more distributed healthcare ecosystem supported by learning-based secure Internet of Medical Things (IoMT). Infrastructure availability is a barrier. The distributed and layered architecture of IoMT another. Trust must be addressed across the full stack and involves challenges in security, privacy, and edge intelligence. This chapter’s objective is to examine the state-of-the-art in security, privacy preservation and provenance of data generated by the IoMT and identify challenges and opportunities. The chapter highlights the existing security and challenges and how they can be addressed from the incorporation of blockchain technologies. Also, it discusses the challenges generated by infrastructure availability and suggests edge computing and federated learning as opportunities to address IoMT service provision where infrastructure is lacking. To demonstrate the feasibility of the proposed solutions a state-of-the-art exemplar system is examined. The system is designed with trustworthy Artificial Intelligence (AI) principles in mind and the results demonstrate not only the benefit for remote diagnostics but also the improvements in security, privacy preservation and provenance when transferring and processing data. The chapter proposes future directions of research to enhance transfer and provenance in distributed healthcare data