LSTM Step Prediction and Ontology-Based Recommendation Generation in Activity eCoaching

Chatterjee, Ayan; Pahari, Nibedita; Riegler, Michael; Prinz, Andreas

doi:10.1109/wimob55322.2022.9941356

Cited by 4 publications

(4 citation statements)

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“…We designed and developed the ontology mapping in the Protégé (v. 5.x) open-source software and visualized the ontology using the OWLViz tool in Protégé [16,17]. In the object-oriented representation, owl:Thing acts as a global parent class and the arrows define a hierarchical relationship (IS-A) between the concepts [18,19].…”

Section: Knowledge Representationmentioning

confidence: 99%

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A Systematic Review on ICT-based Remote and Automatic COVID-19 Patient Monitoring and Care

Chatterjee

Prinz

Riegler

et al. 2023

Preprint

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Background: e-Health has played a crucial role during the COVID-19 pandemic in primary health care. e-Health is the cost-effective and secure use of Information and Communication Technologies (ICT) to support health and health-related fields. As a result of ICTs, the healthcare sector is undergoing a new revolution and societal governance is becoming more sustainable. As a result of the COVID-19 pandemic, information, and communication technology (ICT) has improved the quality of healthcare and the exchange of information, trained healthcare professionals and patients, and facilitated the relationship between patients and their healthcare providers. Various stakeholders worldwide use ICTs, including individuals, non-profit organizations, health practitioners, and governments. This study systematically reviews the literature on ICT-based automatic and remote monitoring methods and different ICT techniques used in the care of COVID-19-infected patients. Objective: The purpose of this systematic review is to identify the e-Health methods, associated information and communication technologies, implementation strategies, information collection techniques, advantages, and disadvantages of remote and automatic patient monitoring and care in COVID-19. Methods: The search included primary studies that were published between January 2020 and June 2022 in scientific and electronic databases, such as EBSCOhost, Scopus, ACM, Nature, SpringerLink, IEEE Xplore, MEDLINE, Google Scholar, JMIR, Web of Science, and PubMed. In this review, the findings from the included publications are presented and elaborated according to the research questions identified. Evidence-based systematic reviews and meta-analyses were conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Additionally, we improved the review process using the Rayyan tool and the Scale for the Assessment of Narrative Review Articles (SANRA). Among the eligibility criteria were methodological rigor, conceptual clarity, and useful implementation of ICTs in e-Health for remote and automatic monitoring of COVID-19 patients. Results: Our initial search identified 664 potential studies; 102 were assessed for eligibility in the pre-final stage and 65 articles were used in the final review with inclusion and exclusion criteria. The review identified the following eHealth methods-Telemedicine, Mobile Health (mHealth), and Telehealth. The associated ICTs are Wearable Body Sensors, Artificial Intelligence (AI) algorithms, Internet-of-Things, or Internet-of-Medical-Things (IoT or IoMT), Biometric Monitoring Technologies (BioMeTs), and Bluetooth-enabled (BLE) home health monitoring devices. Spatial or positional data, personal and individual health, and wellness data, including vital signs, symptoms, biomedical images and signals, and lifestyle data are examples of information that is managed by ICTs. Different AI and IoT methods have opened new possibilities for automatic and remote patient monitoring with associated advantages and weaknesses. Our findings were represented in a structured manner using an ontology model. Conclusions: Various e-Health methods, associated remote monitoring technologies, approaches, information types, adoption of ICT tools for automatic remote patient monitoring (RPM), advantages and limitations of RMTs in the COVID-19 case are discussed in this systematic literature review. The use of e-Health during the COVID-19 pandemic illustrates the constraints and possibilities of using ICT. ICTs are not merely an external tool to achieve definite remote and automatic health monitoring goals; instead, they are embedded in contexts. Therefore, the importance of the mutual design process between ICT and society during the global health crisis has been observed from a social informatics perspective. A global health crisis can be observed as an information crisis (e.g., insufficient information, unreliable information, and inaccessible information); however, this review shows the influence of ICTs on COVID-19 patients' health monitoring and related information collection techniques.

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Section: Knowledge Representationmentioning

confidence: 99%

“…Figure 1 illustrates the percentage (%) of qualitative, quantitative, and mixed studies. [2][3][4], [7][8][9][10][11][12][13][14][15][16][17][18][19], and [82] studies relevant to study narration and formulation are not a part of the 65 papers selected through this systematic literature review.…”

Section: Quality Scoring For Final Selectionmentioning

confidence: 99%

A Systematic Review on ICT-based Remote and Automatic COVID-19 Patient Monitoring and Care

Chatterjee

Prinz

Riegler

et al. 2023

Preprint

Self Cite

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“…This study builds upon personalized eCoach recommendation models from our previous works [27][28][29] and extends the concept to a community-based approach. The goal is to enhance physical activity by combining wearable activity sensors and digital activity trackers, providing motivation and expectancy to participants.…”

Section: Motivationmentioning

confidence: 99%

“…These are sample recommendations to encourage each group to stay active and work toward achieving their weekly goals (Table 10). However, if the system identifies any inactive user in the group, then it will try to generate separate personalized recommendations to motivate, and the approach has been directed in our previous studies focusing on personalized physical activity recommendation generation in eCoaching [27][28][29] . Such a scenario can happen when a group exhibits a "Mixed" performance level.…”

Section: Recommendation Generationmentioning

confidence: 99%

Conceptualization of Community (Advice)-based Health eCoach Recommendation Generation for Physical Activity Level Monitoring using an Explainable and Ethical Approach

Chatterjee,

Riegler,

Halvorsen

2023

Preprint

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Utilizing a digital platform for self-monitoring offers advantages for remote activity tracking and generating recommendations. An automatic coaching system, known as eCoach, can be a valuable tool. It consistently gathers individual health and wellness data for the purpose of creating lifestyle recommendations, either tailored to an individual or based on community input. While personal recommendations in eCoaching are well-received, there is a lack of research on community-based recommendations. This study introduces an effective community-based eCoach recommendation system for assessing daily activity levels and offering physical activity suggestions. The approach involves collecting data from remote participants using wearable activity sensors and categorizing the data into sedentary (0) and active (1) classes for binary classification. The dataset used for physical activity levels exhibits an imbalance in class distribution and unequal misclassification costs, making imbalanced classification challenging. To address this issue and improve the performance of traditional machine-learning models, the study employs various oversampling techniques, including the Synthetic Minority Oversampling Technique (SMOTE), to create balanced datasets by generating synthetic samples from the minority class. Third, we explain the model classifications with Shapley Additive Explanations (SHAP). Fourth, we use hyperparameter tuning methods to obtain the best classification performance. Fifth, we consider a preference, activity patterns, and demographic attributes, such as age, gender, height, and weight, for advice and community-based recommendations to meet activity goals. For experimentation, we collected activity data from 16 participants (Male: 12; Female: 4) using a wearable MOX2-5 activity sensor (CE-certified) and combined the dataset with the public Fibit-based PMData activity dataset of 15 participants (Male: 12; Female: 3) to avoid bias, and for verification and advice-based recommendation planning, we considered last week’s participant data. The system’s performance has been evaluated using metrics such as accuracy, precision, recall, F1-score, brier-score, and Mathew’s coefficient (MCC) and achieves 99.8% accuracy (F1 = 99.8%, Precision = 99.8%, Recall = 99.8%, and MCC = 99.8%) with DecisionTree Classifier on the imbalanced dataset and 99.8% accuracy (F1 = 99.8%, Precision = 99.8%, Recall = 99.8%, and MCC = 99.8%) with DecisionTree Classifier on the balanced dataset. Ethical AI involves more than just coding; it entails ethical considerations and decision-making throughout development. In this study, we evaluated our models for bias and fairness, concentrating on the recommendation generation within an eCoach system. Future research will focus on efficacy evaluation in controlled trials, extending beyond this theoretical investigation.

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An automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontology

Chatterjee

Prinz

Riegler

et al. 2023

Sci Rep

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Electronic coaching (eCoach) facilitates goal-focused development for individuals to optimize certain human behavior. However, the automatic generation of personalized recommendations in eCoaching remains a challenging task. This research paper introduces a novel approach that combines deep learning and semantic ontologies to generate hybrid and personalized recommendations by considering “Physical Activity” as a case study. To achieve this, we employ three methods: time-series forecasting, time-series physical activity level classification, and statistical metrics for data processing. Additionally, we utilize a naïve-based probabilistic interval prediction technique with the residual standard deviation used to make point predictions meaningful in the recommendation presentation. The processed results are integrated into activity datasets using an ontology called OntoeCoach, which facilitates semantic representation and reasoning. To generate personalized recommendations in an understandable format, we implement the SPARQL Protocol and RDF Query Language (SPARQL). We evaluate the performance of standard time-series forecasting algorithms [such as 1D Convolutional Neural Network Model (CNN1D), autoregression, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU)] and classifiers [including Multilayer Perceptron (MLP), Rocket, MiniRocket, and MiniRocketVoting] using state-of-the-art metrics. We conduct evaluations on both public datasets (e.g., PMData) and private datasets (e.g., MOX2-5 activity). Our CNN1D model achieves the highest prediction accuracy of 97$$\%$$ % , while the MLP model outperforms other classifiers with an accuracy of 74$$\%$$ % . Furthermore, we evaluate the performance of our proposed OntoeCoach ontology model by assessing reasoning and query execution time metrics. The results demonstrate that our approach effectively plans and generates recommendations on both datasets. The rule set of OntoeCoach can also be generalized to enhance interpretability.

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LSTM Step Prediction and Ontology-Based Recommendation Generation in Activity eCoaching

Cited by 4 publications

References 25 publications

A Systematic Review on ICT-based Remote and Automatic COVID-19 Patient Monitoring and Care

A Systematic Review on ICT-based Remote and Automatic COVID-19 Patient Monitoring and Care

Conceptualization of Community (Advice)-based Health eCoach Recommendation Generation for Physical Activity Level Monitoring using an Explainable and Ethical Approach

An automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontology

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