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Shahid, Z., Saguna, S. & Åhlund, C. (2024). Federated Learning for Unsupervised Anomaly Detection in ADLs of Elderly in Single-resident Smart Homes. In: The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain.: . Paper presented at The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain.. New York, NY, USA,
Åpne denne publikasjonen i ny fane eller vindu >>Federated Learning for Unsupervised Anomaly Detection in ADLs of Elderly in Single-resident Smart Homes
2024 (engelsk)Inngår i: The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain., New York, NY, USA,, 2024, s. -3Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

One of the main concerns regarding the facilitation of the elderly well-being monitoring system is to preserve the participants’ privacy, enable the older adults to live longer independently, and support caregivers. Human Activity Recognition (HAR) in smart homes allows us to foresee the residents’ needs by identifying changes in behaviour that might link to possible health conditions. We propose a Federated learning (FL) model within the health monitoring application to generalize for diverse participant populations and to achieve comparable performance without disclosing the raw data to the traditional centralized approach, which raises privacy issues. In this study, we evaluate an unsupervised variational autoencoder (VAE) in centralized, individualized, compared to federated learning settings to learn the features of normal patterns of daily activities and build an anomaly detector based on reconstructed error resulting as outcomes by the trained model. Further, we validated our proposed approach on real-world datasets collected over three years from six single-resident elderly households. The individual and centralized-based learning models were used as a baseline to compare with FL. Our results show that the personalized FedAvg models achieve RMSE of about 1%, while the Global FL models achieve RMSE of approximately. 4%. The centralized model achieves RMSE of about 0.5%, and the RMSE of individual models based on local training ranges between 1% to 6%. The FL models are relatively comparable to the centralized baseline model.

sted, utgiver, år, opplag, sider
New York, NY, USA,: , 2024
Serie
The 39th ACM/SIGAPP Symposium On Applied Computing Avila, Spain, ISSN 979-8-4007-0243-3/24/04 ; 4
Emneord
Applied computing → Health informatics; • Computing methodologies → Neural networks; Anomaly detection
HSV kategori
Identifikatorer
urn:nbn:se:ltu:diva-103826 (URN)10.1145/3605098.3636163 (DOI)979-8-4007-0243-3 (ISBN)
Konferanse
The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain.
Tilgjengelig fra: 2024-01-18 Laget: 2024-01-18 Sist oppdatert: 2024-02-08
Shahid, Z. K., Saguna, S. & Åhlund, C. (2024). Multi-Armed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes. IEEE Internet of Things Journal, 11(3), 4414-4429
Åpne denne publikasjonen i ny fane eller vindu >>Multi-Armed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes
2024 (engelsk)Inngår i: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 11, nr 3, s. 4414-4429Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Sleep is an essential activity that affects an individual’s health and ability to perform Activities of Daily Living (ADL). Inadequate sleep reduces cognitive capacity and leads to health-related issues such as cardiovascular diseases. Sleep disorders are more prevalent in older adults. Therefore, it is essential to recognize sleep patterns and support older adults and their caregivers. In our study, we collect data in real-world unconstrained and non-intrusive environments. This paper presents a novel sleep activity recognition method using motion sensors for recognizing nighttime and daytime sleep, which can further enable the development of insightful healthcare applications. The research objectives are to evaluate the application of using Multi-Armed Bandit methods to (i) learn normal sleep patterns, (ii) evaluate sleep quality, and (iii) detect anomalies in sleep activity for 11 elderly participants living in single-resident smart homes. We evaluate the performance of Thompson Sampling, Random Selection, and Upper Confidence Bound MAB methods. Thompson Sampling outperformed the other two methods. Our findings show most elderly participants slept between 6 and 8 hours with 85% sleep efficiency and up to 3 awakenings per night.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2024
Emneord
Anomaly detection, Elderly healthcare, Internet of Things, Medical services, Motion detection, Multi-Armed bandits, Older adults, Reinforcement learning, Sensors, Sleep, Sleep patterns, Smart homes
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-99666 (URN)10.1109/JIOT.2023.3300015 (DOI)2-s2.0-85166780685 (Scopus ID)
Prosjekter
FraViVo—Framtidens Välfärdsteknik med Internet of Things
Forskningsfinansiär
Vinnova, 2020-04096
Merknad

Validerad;2024;Nivå 2;2024-03-18 (hanlid);

Full text license: CC BY-NC-ND

Tilgjengelig fra: 2023-08-15 Laget: 2023-08-15 Sist oppdatert: 2024-03-18bibliografisk kontrollert
Shahid, Z. K., Saguna, S. & Åhlund, C. (2024). Recognizing Seasonal Sleep Patterns of Elderly in Smart Homes Using Clustering. In: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC): . Paper presented at 21st IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 6-9, 2024 (pp. 490-498). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Recognizing Seasonal Sleep Patterns of Elderly in Smart Homes Using Clustering
2024 (engelsk)Inngår i: 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC), IEEE, 2024, s. 490-498Konferansepaper, Publicerat paper (Fagfellevurdert)
sted, utgiver, år, opplag, sider
IEEE, 2024
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-103827 (URN)10.1109/CCNC51664.2024.10454817 (DOI)2-s2.0-85189207183 (Scopus ID)
Konferanse
21st IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, January 6-9, 2024
Merknad

ISBN for host publication: 979-8-3503-0457-2;

Tilgjengelig fra: 2024-01-18 Laget: 2024-01-18 Sist oppdatert: 2024-04-08bibliografisk kontrollert
Kim, J. C., Saguna, S. & Åhlund, C. (2024). The Effects of Augmented Reality Companion on User Engagement in Energy Management Mobile App. Applied Sciences, 14(7), Article ID 2621.
Åpne denne publikasjonen i ny fane eller vindu >>The Effects of Augmented Reality Companion on User Engagement in Energy Management Mobile App
2024 (engelsk)Inngår i: Applied Sciences, E-ISSN 2076-3417, Vol. 14, nr 7, artikkel-id 2621Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

As the impact of global warming on climate change becomes noticeable, the importance of energy efficiency for reducing greenhouse gas emissions grows immense. To this end, a platform, solution, and mobile apps are developed as part of the European Union’s Horizon 2020 research and innovation program to support energy optimization in residences. However, to ensure long-term energy optimization, it is crucial to keep users engaged with the apps. Since augmented reality (AR) and a virtual animal companion positively influenced user engagement, we designed an AR companion that represented the user’s residence states; thereby making the user aware of indoor information. We conducted user evaluations to determine the effect of the AR companion on user engagement and perceived usability in the context of energy management. We identified that the user interface (UI) with AR (ARUI) barely affected user engagement and perceived usability compared to the traditional UI without AR (TUI); however, we found that the ARUI positively affected one of the user engagement aspects. Our results show AR companion integration’s potential benefits and effects on energy management mobile apps. Furthermore, our findings provide insights into UI design elements for developers considering multiple interaction modalities with AR.

Emneord
user interface, user evaluation, user engagement, perceived usability, augmented reality, Internet of Things, energy management
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-104850 (URN)10.3390/app14072671 (DOI)
Forskningsfinansiär
EU, Horizon 2020, 893079
Merknad

Validerad;2024;Nivå 2;2024-03-22 (signyg);

Full text license: CC BY

Tilgjengelig fra: 2024-03-22 Laget: 2024-03-22 Sist oppdatert: 2024-03-22bibliografisk kontrollert
Mololoth, V. K., Åhlund, C. & Saguna, S. (2023). A Private Blockchain Based P2P Energy Trading Platform for Energy Users. In: Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023: . Paper presented at 9th IEEE International Smart Cities Conference, ISC2 2023, Bucharest, Romania, September 24-27, 2023. Institute of Electrical and Electronics Engineers Inc.
Åpne denne publikasjonen i ny fane eller vindu >>A Private Blockchain Based P2P Energy Trading Platform for Energy Users
2023 (engelsk)Inngår i: Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023, Institute of Electrical and Electronics Engineers Inc. , 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc., 2023
Serie
Proceedings of the IEEE International Smart Cities Conference, ISSN 2687-8852, E-ISSN 2687-8860
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-103305 (URN)10.1109/ISC257844.2023.10293651 (DOI)2-s2.0-85178342222 (Scopus ID)979-8-3503-9775-8 (ISBN)979-8-3503-9776-5 (ISBN)
Konferanse
9th IEEE International Smart Cities Conference, ISC2 2023, Bucharest, Romania, September 24-27, 2023
Tilgjengelig fra: 2023-12-13 Laget: 2023-12-13 Sist oppdatert: 2023-12-13bibliografisk kontrollert
Kim, J. C., Saguna, S. & Åhlund, C. (2023). Acceptability of a Health Care App With 3 User Interfaces for Older Adults and Their Caregivers: Design and Evaluation Study. JMIR Human Factors, 10, Article ID e42145.
Åpne denne publikasjonen i ny fane eller vindu >>Acceptability of a Health Care App With 3 User Interfaces for Older Adults and Their Caregivers: Design and Evaluation Study
2023 (engelsk)Inngår i: JMIR Human Factors, E-ISSN 2292-9495, Vol. 10, artikkel-id e42145Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: The older population needs solutions for independent living and reducing the burden on caregivers while maintaining the quality and dignity of life.

Objective: The aim of this study was to design, develop, and evaluate an older adult health care app that supports trained caregivers (ie, formal caregivers) and relatives (ie, informal caregivers). We aimed to identify the factors that affect user acceptance of interfaces depending on the user’s role.

Methods: We designed and developed an app with 3 user interfaces that enable remote sensing of an older adult’s daily activities and behaviors. We conducted user evaluations (N=25) with older adults and their formal and informal caregivers to obtain an overall impression of the health care monitoring app in terms of user experience and usability. In our design study, the participants had firsthand experience with our app, followed by a questionnaire and individual interview to express their opinions on the app. Through the interview, we also identified their views on each user interface and interaction modality to identify the relationship between the user’s role and their acceptance of a particular interface. The questionnaire answers were statistically analyzed, and we coded the interview answers based on keywords related to a participant’s experience, for example, ease of use and usefulness.

Results: We obtained overall positive results in the user evaluation of our app regarding key aspects such as efficiency, perspicuity, dependability, stimulation, and novelty, with an average between 1.74 (SD 1.02) and 2.18 (SD 0.93) on a scale of −3.0 to 3.0. The overall impression of our app was favorable, and we identified that “simple” and “intuitive” were the main factors affecting older adults’ and caregivers’ preference for the user interface and interaction modality. We also identified a positive user acceptance of the use of augmented reality by 91% (10/11) of the older adults to share information with their formal and informal caregivers.

Conclusions: To address the need for a study to evaluate the user experience and user acceptance by older adults as well as both formal and informal caregivers regarding the user interfaces with multimodal interaction in the context of health monitoring, we designed, developed, and conducted user evaluations with the target user groups. Our results through this design study show important implications for designing future health monitoring apps with multiple interaction modalities and intuitive user interfaces in the older adult health care domain.

sted, utgiver, år, opplag, sider
JMIR Publications, 2023
Emneord
Internet of Things, health monitoring, older adults, augmented reality, user experience, independent living, design study, mobile phone
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-95822 (URN)10.2196/42145 (DOI)001017203700025 ()36884275 (PubMedID)2-s2.0-85149873927 (Scopus ID)
Merknad

Validerad;2023;Nivå 2;2023-08-10 (joosat);

Funder: Swedish Governmental Agency for Innovation Systems (grant 2017-02807)

Licens fulltext: CC BY License

Tilgjengelig fra: 2023-03-09 Laget: 2023-03-09 Sist oppdatert: 2023-09-05bibliografisk kontrollert
de Koning, E., van der Haas, Y., Saguna, S., Stoop, E., Bosch, J., Beeres, S., . . . Boogers, M. (2023). AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study. JMIR Cardio, 7(1), Article ID e51375.
Åpne denne publikasjonen i ny fane eller vindu >>AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study
Vise andre…
2023 (engelsk)Inngår i: JMIR Cardio, E-ISSN 2561-1011, Vol. 7, nr 1, artikkel-id e51375Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: Overcrowding of hospitals and emergency departments (EDs) is a growing problem. However, not all ED consultations are necessary. For example, 80% of patients in the ED with chest pain do not have an acute coronary syndrome (ACS). Artificial intelligence (AI) is useful in analyzing (medical) data, and might aid health care workers in prehospital clinical decision-making before patients are presented to the hospital.

Objective: The aim of this study was to develop an AI model which would be able to predict ACS before patients visit the ED. The model retrospectively analyzed prehospital data acquired by emergency medical services' nurse paramedics.

Methods: Patients presenting to the emergency medical services with symptoms suggestive of ACS between September 2018 and September 2020 were included. An AI model using a supervised text classification algorithm was developed to analyze data. Data were analyzed for all 7458 patients (mean 68, SD 15 years, 54% men). Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for control and intervention groups. At first, a machine learning (ML) algorithm (or model) was chosen; afterward, the features needed were selected and then the model was tested and improved using iterative evaluation and in a further step through hyperparameter tuning. Finally, a method was selected to explain the final AI model.

Results: The AI model had a specificity of 11% and a sensitivity of 99.5% whereas usual care had a specificity of 1% and a sensitivity of 99.5%. The PPV of the AI model was 15% and the NPV was 99%. The PPV of usual care was 13% and the NPV was 94%.

Conclusions: The AI model was able to predict ACS based on retrospective data from the prehospital setting. It led to an increase in specificity (from 1% to 11%) and NPV (from 94% to 99%) when compared to usual care, with a similar sensitivity. Due to the retrospective nature of this study and the singular focus on ACS it should be seen as a proof-of-concept. Other (possibly life-threatening) diagnoses were not analyzed. Future prospective validation is necessary before implementation.

sted, utgiver, år, opplag, sider
JMIR Publications, 2023
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-102492 (URN)10.2196/51375 (DOI)
Merknad

Validerad;2023;Nivå 1;2023-11-17 (joosat);

CC BY 4.0 License

Tilgjengelig fra: 2023-11-17 Laget: 2023-11-17 Sist oppdatert: 2023-11-17bibliografisk kontrollert
Mololoth, V. K., Åhlund, C. & Saguna, S. (2023). An energy trading framework using smart contracts. In: 2023 IEEE Green Technologies Conference, GreenTech: . Paper presented at 15th Annual IEEE Green Technologies Conference, GreenTech 2023, Denver, United States, April 19-21, 2023 (pp. 214-218). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>An energy trading framework using smart contracts
2023 (engelsk)Inngår i: 2023 IEEE Green Technologies Conference, GreenTech, IEEE , 2023, s. 214-218Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The adoption of blockchain in various industries is gaining more popularity, especially in the energy industry. With the increase of distributed energy resources (DER), energy users can generate, store, and trade their resources with others. Utility companies or energy users are influenced by blockchain-based peer-To-peer (P2P) energy trading markets. Blockchain adds transparency and immutability to the involved transactions. Smart contracts in blockchain automatically execute when the conditions are met without any third-party intervention. Motivated by these benefits, in this paper an energy trading framework is developed using Ethereum smart contracts. Energy users can trade their excess energy or buy energy using the smart contract functions. Smart contract written in solidity is compiled and deployed using remix with injected metamask provider. Ganache is used to create accounts and these accounts are imported to metamask for signing transactions. We also discuss alternative methods for smart contract deployment. Computational cost analysis is performed by evaluating the gas consumption analysis for the smart contract functions.

sted, utgiver, år, opplag, sider
IEEE, 2023
Serie
IEEE Green Technologies Conference, ISSN 2166-546X, E-ISSN 2166-5478
Emneord
blockchain, distributed energy sources, Energy trading, smart contracts
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-100640 (URN)10.1109/GreenTech56823.2023.10173814 (DOI)001043037200041 ()2-s2.0-85166216071 (Scopus ID)978-1-6654-9287-4 (ISBN)978-1-6654-9288-1 (ISBN)
Konferanse
15th Annual IEEE Green Technologies Conference, GreenTech 2023, Denver, United States, April 19-21, 2023
Tilgjengelig fra: 2023-08-17 Laget: 2023-08-17 Sist oppdatert: 2024-03-07bibliografisk kontrollert
Shahid, Z. K., Saguna, S. & Åhlund, C. (2023). Autoencoders for Anomaly Detection in Electricity and District Heating Consumption: A Case Study in School Buildings in Sweden. In: Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, (EEEIC / I&CPS Europe 2023): . Paper presented at 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, June 6-9, 2023. Institute of Electrical and Electronics Engineers Inc.
Åpne denne publikasjonen i ny fane eller vindu >>Autoencoders for Anomaly Detection in Electricity and District Heating Consumption: A Case Study in School Buildings in Sweden
2023 (engelsk)Inngår i: Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, (EEEIC / I&CPS Europe 2023), Institute of Electrical and Electronics Engineers Inc. , 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Real-time anomaly detection in real-time energy consumption helps identify the technical and infrastructure issues and events that result in significant energy waste and provides the end users feedback to address the issues, which reduces drift operation costs and saves energy in the building. This study proposes deep learning reconstruction models to detect anomalies in daily energy consumption data for nine school buildings. We evaluated the performance of three proposed models, stacked RNN-LSTM autoencoder, CNN-LSTM autoencoder, and LSTM Variational Autoencoder (VAE), to learn the features of normal consumption in an unsupervised manner and detect anomalies based on reconstruction error. We used Exponential Moving Average (EMA) and static threshold to detect local and global anomalies. The experimental results demonstrate that the local CNN-LSTM autoencoder performs better than the local Stacked Autoencoder(AE), with RMSE values ranging between 8-13% for electricity and 11-19% for district heating compared to 12-17% and 15-34% resulting from AE model, respectively. Local LSTM-Variational Autoencoder (VAE) outperformed both methods, with RMSE 4-6% for electricity and 5-7% for district heating. LSTM-VAE trained model on grouped training datasets of schools with similar energy consumption and building profiles has improved the local model by lowering RMSE values to 2-3% for electricity and 3-4% in district heating.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc., 2023
Emneord
Anomaly Detection, Autoencoder, CNN-LSTM, electricity and district heating, Daily Consumption, RNN-LSTM, school buildings
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-101201 (URN)10.1109/EEEIC/ICPSEurope57605.2023.10194605 (DOI)2-s2.0-85168668252 (Scopus ID)979-8-3503-4744-9 (ISBN)979-8-3503-4743-2 (ISBN)
Konferanse
2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, June 6-9, 2023
Tilgjengelig fra: 2023-09-05 Laget: 2023-09-05 Sist oppdatert: 2023-10-14bibliografisk kontrollert
Mololoth, V. K., Saguna, S. & Åhlund, C. (2023). Blockchain and Machine Learning for Future Smart Grids: A Review. Energies, 16(1), Article ID 528.
Åpne denne publikasjonen i ny fane eller vindu >>Blockchain and Machine Learning for Future Smart Grids: A Review
2023 (engelsk)Inngår i: Energies, E-ISSN 1996-1073, Vol. 16, nr 1, artikkel-id 528Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in the development of smart grid and investigate the benefits achieved by using both techniques for the future smart grid scenario. Further, we discuss research challenges and future research directions of applying blockchain and machine learning techniques for smart grids both individually as well as combining them together. The identified areas that require significant research are demand management in power grids, improving the security of grids with better consensus mechanisms, electric vehicle charging systems, scheduling of the entire grid system, designing secure microgrids, and the interconnection of different blockchain networks.

sted, utgiver, år, opplag, sider
MDPI, 2023
Emneord
blockchain, demand response management, electric vehicles, energy trading, machine learning, security, smart grids
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
urn:nbn:se:ltu:diva-95274 (URN)10.3390/en16010528 (DOI)000909000800001 ()2-s2.0-85145775833 (Scopus ID)
Merknad

Validerad;2023;Nivå 2;2023-01-16 (hanlid);

Funder: Stiftelsen Rönnbäret

Tilgjengelig fra: 2023-01-16 Laget: 2023-01-16 Sist oppdatert: 2023-08-28bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-8561-7963