Ayan Chatterjee

• Chatterjee, Ayan; Gerdes, Martin; Prinz, Andreas; Riegler, Michael & Martinez, Santiago (2024). Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study. Scientific Reports. ISSN 2045-2322. 14(1). doi: 10.1038/s41598-024-55183-6.
• Chatterjee, Ayan; Pahari, Nibedita; Prinz, Andreas & Riegler, Michael Alexander (2023). AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach. BMC Medical Informatics and Decision Making. ISSN 1472-6947. 23(1). doi: 10.1186/s12911-023-02364-4. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Prinz, Andreas; Riegler, Michael & Das, Jishnu (2023). A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care. BMC Health Services Research. ISSN 1472-6963. 23(1). doi: 10.1186/s12913-023-10047-z. Fulltekst i vitenarkiv
• Mortensen, Jon Andreas; Mollov, Martin Efremov; Chatterjee, Ayan; Ghose, Debasish & Li, Frank Yong (2023). Multi-Class Stress Detection Through Heart Rate Variability: A Deep Neural Network Based Study. IEEE Access. ISSN 2169-3536. 11, s. 57470–57480. doi: 10.1109/ACCESS.2023.3274478. Fulltekst i vitenarkiv Vis sammendrag

  Stress is a natural human reaction to demands or pressure, usually when perceived as harmful or/and toxic. When stress becomes constantly overwhelmed and prolonged, it increases the risk of mental health and physiological uneasiness. Furthermore, chronic stress raises the likelihood of mental health plagues such as anxiety, depression, and sleep disorder. Although measuring stress using physiological parameters such as heart rate variability (HRV) is a common approach, how to achieve ultra-high accuracy based on HRV measurements remains as a challenging task. HRV is not equivalent to heart rate. While heart rate is the average value of heartbeats per minute, HRV represents the variation of the time interval between successive heartbeats. The HRV measurements are related to the variance of RR intervals which stand for the time between successive R peaks. In this study, we investigate the role of HRV features as stress detection bio-markers and develop a machine learning-based model for multi-class stress detection. More specifically, a convolution neural network (CNN) based model is developed to detect multi-class stress, namely, no stress, interruption stress, and time pressure stress, based on both time- and frequency-domain features of HRV. Validated through a publicly available dataset, SWELL−KW, the achieved accuracy score of our model has reached 99.9% (Precision = 1, Recall = 1, F1−score = 1, and MCC = 0.99), thus outperforming the existing methods in the literature. In addition, this study demonstrates the effectiveness of essential HRV features for stress detection using a feature extraction technique, i.e., analysis of variance.

• Chatterjee, Ayan; Prinz, Andreas; Riegler, Michael Alexander & Meena, Yogesh Kumar (2023). An automatic and personalized recommendation modelling in activity eCoaching with deep learning and ontology. Scientific Reports. ISSN 2045-2322. 13(1). doi: 10.1038/s41598-023-37233-7. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Prinz, Andreas; Pahari, Nibedita; Das, Jishnu & Riegler, Michael (2022). Sleep Monitoring with Wearable Sensor Data in an eCoach Recommendation System: A Conceptual Study with Machine Learning Approach, Frontiers of ICT in Healthcare. Springer Nature. ISSN 978-981-19-5191-6. s. 551–564. doi: https:/doi.org/10.1007/978-981-19-5191-6_44. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Pahari, Nibedita; Gerdes, Martin & Bajpai, Ram (2022). Analyze the Effect of Healthy Behavior on Weight Change and Its Conceptual Use in Digital Behavioral Intervention, 2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE conference proceedings. ISSN 978-1-6654-8016-1. s. 222–228. doi: 10.1109/HealthCom54947.2022.9982785. Vis sammendrag

  The gradual increase of negative behavior in humans because of physical inactivity, unhealthy habit, and improper nutrition expedites the growth of lifestyle diseases. Proper lifestyle management may help to reach personal weight goals or maintain a normal weight range with optimization of health behaviors (physical activity, diet, and habits). This study conceptualizes a method to integrate the proposed mathematical model in a digital intervention strategy targeting obesity as a study case. We verify our proposed model with simulated data and compare it with related models based on the defined constraints. We express the mathematical model as a function of activity, habit, and nutrition with the first order law of thermodynamics, basal metabolic rate (BMR), total daily energy expenditure (TDEE), and body-mass-index (BMI) to establish a link between health behavior and weight change. We have used revised Harris-Benedict formulas (HB) for BMR and TDEE calculations. The proposed model showed a strong relationship between health behavior and weight change. The adoption of BMR and TDEE measures following the revised HB formula has outperformed the classical Wishnofsky’s rule (3500 kcal. ≈ 1 lb. or 7700 kcal ≈ 1 Kg.), and the models proposed by Toumasis et al., Azzeh et al., and Mickens et al. with an average standard deviation (σ) of ±2.26, ±2.67, ±2.432, and ±2.29 respectively. This study helped us to understand the impact of healthy behavior on weight change with a mathematical model and its importance in maintaining a healthy lifestyle.

• Chatterjee, Ayan; Pahari, Nibedita; Prinz, Andreas & Riegler, Michael (2022). Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation. Scientific Reports. ISSN 2045-2322. 12. doi: 10.1038/s41598-022-24118-4. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Pahari, Nibedita; Riegler, Michael & Prinz, Andreas (2022). LSTM Step Prediction and Ontology-Based Recommendation Generation in Activity eCoaching, 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE conference proceedings. ISSN 978-1-6654-6975-3. s. 13–18. doi: 10.1109/WiMob55322.2022.9941356. Vis sammendrag

  An eCoach system may allow people to manage a healthy lifestyle with health state monitoring (e.g., physical activity) and personalized recommendation generation. Daily step count is an important feature to provide a direct and indirect reflection on individual activity levels. Therefore, a personalized, predictive model may be beneficial to forecast future “steps” to motivate participants based on the temporal “step” pattern. Here, we have conceptualized the idea with a Bidirectional Long-ShortTerm-Memory (LSTM) model for weekly activity forecasting and a rule-base for personalized recommendation generation with Ontology reasoning and querying in activity eCoaching. First, we have used the publicly available “PMData” dataset of 16 adults (M: 13; F: 3) to train and test the models and explore the possibility of accurate univariate time-series forecasting of “step counts”. Second, we have created an Ontology and a rule-base to generate personalized activity recommendations to motivate participants to accomplish their activity goals (e.g., complete “X” steps daily and stay active for the entire week).

• Chatterjee, Ayan; Pahari, Nibedita & Prinz, Andreas (2022). HL7 FHIR with SNOMED-CT to Achieve Semantic and Structural Interoperability in Personal Health Data: A Proof-of-Concept Study. Sensors. ISSN 1424-8220. 22(10). doi: 10.3390/s22103756. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Prinz, Andreas; Gerdes, Martin; Martinez, Santiago; Pahari, Nibedita & Meena, Yogesh Kumar (2022). ProHealth eCoach: user-centered design and development of an eCoach app to promote healthy lifestyle with personalized activity recommendations. BMC Health Services Research. ISSN 1472-6963. 22. doi: 10.1186/s12913-022-08441-0. Fulltekst i vitenarkiv Vis sammendrag

  Background: Regular physical activity (PA), healthy habits, and an appropriate diet are recommended guidelines to maintain a healthy lifestyle. A healthy lifestyle can help to avoid chronic diseases and long-term illnesses. A monitoring and automatic personalized lifestyle recommendation system (i.e., automatic electronic coach or eCoach) with considering clinical and ethical guidelines, individual health status, condition, and preferences may successfully help participants to follow recommendations to maintain a healthy lifestyle. As a prerequisite for the prototype design of such a helpful eCoach system, it is essential to involve the end-users and subject-matter experts throughout the iterative design process. Methods: We used an iterative user-centered design (UCD) approach to understend context of use and to collect qualitative data to develop a roadmap for self-management with eCoaching. We involved researchers, non-technical and technical, health professionals, subject-matter experts, and potential end-users in design process. We designed and developed the eCoach prototype in two stages, adopting diferent phases of the iterative design process. In design workshop 1, we focused on identifying end-users, understanding the user’s context, specifying user requirements, designing and developing an initial low-fdelity eCoach prototype. In design workshop 2, we focused on maturing the low-fdelity solution design and development for the visualization of continuous and discrete data, artifcial intelligence (AI)-based interval forecasting, personalized recommendations, and activity goals. Results: The iterative design process helped to develop a working prototype of eCoach system that meets end-user’s requirements and expectations towards an efective recommendation visualization, considering diversity in culture, quality of life, and human values. The design provides an early version of the solution, consisting of wearable technology, a mobile app following the “Google Material Design” guidelines, and web content for self-monitoring, goal setting, and lifestyle recommendations in an engaging manner between the eCoach app and end-users. Conclusions: The adopted iterative design process brings in a design focus on the user and their needs at each phase. Throughout the design process, users have been involved at the heart of the design to create a working.

• Chatterjee, Ayan; Gerdes, Martin; Khatiwada, Pankaj & Prinz, Andreas (2022). SFTSDH: Applying Spring Security Framework with TSD-Based OAuth2 to Protect Microservice Architecture APIs. IEEE Access. ISSN 2169-3536. 10, s. 41914–41934. doi: 10.1109/ACCESS.2022.3165548. Fulltekst i vitenarkiv Vis sammendrag

  The Internet of Medical Things (IoMT) combines medical devices and applications that use network technologies to connect healthcare information systems (HIS). IoMT is reforming the medical industry by adopting information and communication technologies (ICTs). Identity verification, secure collection, and exchange of medical data are essential in health applications. In this study, we implemented a hybrid security solution to secure the collection and management of personal health data using Spring Framework (SF), Services for Sensitive Data (TSD) as a service platform, and Hyper-Text-Transfer-Protocol (HTTP (H)) security methods. The adopted solution (SFTSDH = SF + TSD + H) instigated the following security features: identity brokering, OAuth2, multifactor authentication, and access control to protect the Microservices Architecture Application Programming Interfaces (APIs), following the General Data Protection Regulation (GDPR). Moreover, we extended the adopted security solution to develop a digital infrastructure to facilitate the research and innovation work in the electronic health (eHealth) section, focusing on solution validation with theoretical evaluation and experimental testing. We used a web engineering security methodology to achieve and explain the adopted security solution. As a case study, we designed and implemented electronic coaching (eCoaching) prototype system and deployed the same in the developed infrastructure to securely record and share personal health data. Furthermore, we compared the test results with related studies qualitatively for the efficient evaluation of the implemented security solution. The SFTSDH implementation and configuration in the prototype system have effectively secured the eCoach APIs from an attack in all the considered scenarios. The eCoach prototype with the SFTSDH solution effectively sustained a load of (≈) 1000 concurrent users in the developed digital health infrastructure. In addition, we performed a qualitative comparison among the following security solutions: SF security, third-party security, and SFTSDH, where SFTSDH showed a promising outcome.

• Chatterjee, Ayan & Prinz, Andreas (2022). Personalized Recommendations for Physical Activity e-Coaching (OntoRecoModel): Ontological Modeling. JMIR Medical Informatics. ISSN 2291-9694. 10(6). doi: 10.2196/33847. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Prinz, Andreas & Riegler, Michael (2022). Prediction Modeling in Activity eCoaching for Tailored Recommendation Generation: A Conceptualization, 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE conference proceedings. ISSN 9781665482998. doi: 10.1109/MeMeA54994.2022.9856556. Vis sammendrag

  The collective effects of a sedentary lifestyle are correlated with numerous adverse health consequences, including increased risk of obesity, diabetes type II, high blood pressure, depression, and cardiovascular risks. Research in eHealth can provide methodologies to enhance personal healthcare with Information and Communication Technologies (ICTs). An eCoach system may allow people to manage a healthy lifestyle with health state monitoring (e.g., physical activity) and tailored recommendation generation. Daily step count is an important feature to provide a direct and indirect reflection on individual activity levels. Therefore, a personalized, predictive model may be beneficial to forecast future "steps" based on the temporal analysis of the individual “step” pattern. In this study, we have conceptualized the same idea with statistical forecast models (e.g., Kalman Filter (KF), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA)) and a ruleset for activity type determination in personalized activity eCoaching. First, we used the publicly available “PMData” dataset of 16 adults (M: 13; F:3) to train and test the models and explore the possibility of accurate univariate time-series forecasting of “step counts”. Second, we established an idea to generate personalized activity recommendations based on the forecasting outcome, ruleset, and personal preference settings. The ARIMA model has outdated other predictors against root mean squared error (RMSE). Therefore, we conceptualized using the ARIMA in our eCoach system to generate personalized recommendations to motivate participants to accomplish their activity goals (e.g., complete “X” steps daily and stay active for the entire week) as proof of concept.

• Chatterjee, Ayan & Prinz, Andreas (2022). Applying Spring Security Framework with KeyCloak-Based OAuth2 to Protect Microservice Architecture APIs: A Case Study. Sensors. ISSN 1424-8220. 22(5). doi: 10.3390/s22051703. Fulltekst i vitenarkiv Vis sammendrag

  : In this study, we implemented an integrated security solution with Spring Security and Keycloak open-access platform (SSK) to secure data collection and exchange over microservice architecture application programming interfaces (APIs). The adopted solution implemented the following security features: open authorization, multi-factor authentication, identity brokering, and user management to safeguard microservice APIs. Then, we extended the security solution with a virtual private network (VPN), Blowfish and crypt (Bcrypt) hash, encryption method, API key, network firewall, and secure socket layer (SSL) to build up a digital infrastructure. To accomplish and describe the adopted SSK solution, we utilized a web engineering security method. As a case study, we designed and developed an electronic health coaching (eCoach) prototype system and hosted the system in the expanded digital secure infrastructure to collect and exchange personal health data over microservice APIs. We further described our adopted security solution’s procedural, technical, and practical considerations. We validated our SSK solution implementation by theoretical evaluation and experimental testing. We have compared the test outcomes with related studies qualitatively to determine the efficacy of the hybrid security solution in digital infrastructure. The SSK implementation and configuration in the eCoach prototype system has effectively secured its microservice APIs from an attack in all the considered scenarios with 100% accuracy. The developed digital infrastructure with SSK solution efficiently sustained a load of (≈)300 concurrent users. In addition, we have performed a qualitative comparison among the following security solutions: Spring-based security, Keycloak-based security, and their combination (our utilized hybrid security solution), where SSK showed a promising outcome.

• Chatterjee, Ayan; Gerdes, Martin; Prinz, Andreas & Martinez, Santiago (2021). A Comparative Study to Analyze the Performance of Advanced Pattern Recognition Algorithms for Multi-Class Classification. I Tavares, J. M. R. S.; Chakrabarti, S.; Bhattacharya, A. & Ghatak, S. (Red.), Emerging Technologies in Data Mining and Information Security. Springer. ISSN 978-981-15-9773-2. s. 111–124. doi: 10.1007/978-981-15-9774-9_11.
• Chatterjee, Ayan; Gerdes, Martin; Prinz, Andreas & Martinez, Santiago (2021). Comparing Performance of Ensemble-Based Machine Learning Algorithms to Identify Potential Obesity Risk Factors from Public Health Datasets. I Hassanien, Aboul Ella; Bhattacharyya, Siddhartha; Chakrabati, Satyajit; Bhattacharya, Abhishek & Dutta, Soumi (Red.), Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing. Springer. ISSN 978-981-15-9926-2. s. 253–269. doi: 10.1007/978-981-15-9927-9_26.
• Chatterjee, Ayan (2021). Calculate Pulse from Touch Error Free PPG Signal with 2nd Order Butterworth Filter . I ., . (Red.), 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-1262-9. doi: 10.1109/ICECCME52200.2021.9591088.
• Chatterjee, Ayan; Shahaab, Ali; Gerdes, Martin; Martinez, Santiago & Khatiwada, Pankaj (2021). Leveraging Technology for Healthcare and Retaining Access to Personal Health Data to Enhance Health and Well-being. I Bhattacharyya, Siddhartha (Red.), Recent Trends in Computational Intelligence Enabled Research. Elsevier. ISSN 978-0-12-822844-9. s. 367–376. doi: 10.1016/B978-0-12-822844-9.00044-X.
• Chatterjee, Ayan; Gerdes, Martin; Martinez, Santiago & Prinz, Andreas (2021). Human Coaching Methodologies for Automatic Electronic Coaching (eCoaching) as Behavioral Interventions with Information and Communication Technology: Systematic Review. Journal of Medical Internet Research. ISSN 1438-8871. 23(3). doi: 10.2196/23533. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Prinz, Andreas; Gerdes, Martin & Martinez, Santiago (2021). Digital interventions on healthy lifestyle management: Systematic review. Journal of Medical Internet Research. ISSN 1438-8871. 23(11). doi: 10.2196/26931. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Prinz, Andreas; Gerdes, Martin & Martinez, Santiago (2021). An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study . Journal of Medical Internet Research. ISSN 1438-8871. 23(4), s. 1–28. doi: 10.2196/24656.
• Khatiwada, Pankaj; Bhusal, Hari; Chatterjee, Ayan & Gerdes, Martin (2020). A Proposed Access Control-Based Privacy Preservation Model to Share Healthcare Data in Cloud. IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications. ISSN 2160-4886. s. 40–47. doi: 10.1109/WiMob50308.2020.9253414.
• Chatterjee, Ayan; Gerdes, Martin & Martinez, Santiago (2020). Development of a Smart e-Coach Recommendation System for Obesity. Studies in Health Technology and Informatics. ISSN 0926-9630. 270, s. 1259–1260. doi: 10.3233/SHTI200391.
• Chatterjee, Ayan; Gerdes, Martin & Martinez, Santiago (2020). Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death. Sensors. ISSN 1424-8220. 20(11). doi: 10.3390/s20113089. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Gerdes, Martin & Martinez, Santiago (2020). Identification of Risk Factors Associated with Obesity and Overweight - A Machine Learning Overview. Sensors. ISSN 1424-8220. 20(9). doi: 10.3390/s20092734. Fulltekst i vitenarkiv
• Chatterjee, Ayan; Gerdes, Martin; Prinz, Andreas; Martinez, Santiago & Medin, Anine Christine (2020). Reference Design Model for a Smart e-Coach Recommendation System for Lifestyle Support Based on ICT Technologies. I Sendra, Sandra; Murata, Yoshitoshi; Civit-Masot, Javier & Rajh, Arian (Red.), eTELEMED 2020, The Twelfth International Conference on eHealth, Telemedicine, and Social Medicine. International Academy, Research and Industry Association (IARIA). ISSN 978-1-61208-763-4. s. 52–58. doi: 10.13140/RG.2.2.33574.47689. Vis sammendrag

  As acknowledged by the World Health Organization (WHO), the demographic development shows that by 2030, 8 out of the 10 foremost causes of death will be connected to risk conditions of lifestyle diseases, regardless of gender. Chronic illness associated with modifiable lifestyle factors will be accountable for the highest death worldwide. Health behavior change should be given priority to avoid serious reparations. An Electronic Coach (e-Coach) system can empower people to achieve a healthy lifestyle with early risk predictions and appropriate tailored lifestyle recommendations. Research in eHealth has the potential to provide methods in order to improve personal healthcare with Information and Communication Technologies (ICT). An Electronic Health (e-Health) virtual coaching recommendation system can monitor people and convey the appropriate recommendations in context with enough time to prevent, ameliorate the living with non-communicable or lifestyle diseases. This paper addresses the potential of selected emerging information and communication technologies to make e-Health systems smarter, more collaborative and more efficient. As a result of the analysis, a reference design is discussed here to develop and validate the performance of a smart e-Coach system utilizing ICT, Internet of Things (IoT) and Artificial Intelligence (AI) to provide individual lifestyle recommendations aiming at a healthier lifestyle to prevent obesity and overweight. The healthcare sector is still looking for collaborative, user-friendly, optimized, cost-effective, reliable systems for health e-Coaching with contextual, tailored lifestyle recommendations and our current research is focused on its implementation and validation, considering obesity and overweight as a case study.

• Chatterjee, Ayan; Gerdes, Martin & Martinez, Santiago (2019). eHealth Initiatives for The Promotion of Healthy Lifestyle and Allied Implementation Difficulties, 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE conference proceedings. ISSN 978-1-7281-3316-4. s. 7–14. doi: 10.1109/WiMOB.2019.8923324.
• Chatterjee, Ayan & Prinz, Andreas (2018). Image Analysis on Fingertip Video to Obtain PPG. Biomedical and Pharmacology Journal (BPJ). ISSN 0974-6242. 11(4), s. 1811–1827. doi: 10.13005/bpj/1554.

Se alle arbeider i Cristin

• Chatterjee, Ayan; Pahari, Nibedita; Prinz, Andreas & Riegler, Michael (2023). Author Correction: Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation (Scientific Reports, (2022), 12, 1, (19825), 10.1038/s41598-022-24118-4). Scientific Reports. ISSN 2045-2322. 13(1). doi: 10.1038/s41598-023-30029-9.
• Chatterjee, Ayan & Joshi, Pooja (2021). Economic feasibility of Artificial Intelligence in e-health.
• Khatiwada, Pankaj; Chatterjee, Ayan & Subedi, Matrika (2021). Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network.
• Chatterjee, Ayan; Prinz, Andreas; Gerdes, Martin & Martinez, Santiago (2020). A Statistical Study to Analyze the Impact of External Weather Change on Chronic Pulmonary Infection in South Norway with Machine Learning Algorithms. In Intelligent Technologies and Applications.
• Chatterjee, Ayan (2023). Automatic Generation of Personalized Recommendations in eCoaching. Universitetet i Agder. Fulltekst i vitenarkiv

Se alle arbeider i Cristin