Autors: Tonchev, K., Koleva, P. H., Manolova, A. H., Tsenov, G. T., Poulkov, V. K.
Title: Non-intrusive sleep analyzer for real time detection of sleep anomalies
Keywords: Context awareness; HMM; NREM; REM; Sleep anomaly detection;

Abstract: Solutions for caring for the elderly both efficacious and cost-effective are given by Ambient Assisted Living (AAL) systems that combine the research fields of intelligent systems and communication technologies. These systems are promising for the improvement of the quality of life of elderly and disabled people. One important characteristic of health and well-being is sleep. While sleep quantity is directly measurable, its quality has traditionally been assessed with subjective methods such as questionnaires. In this paper, we propose a non-intrusive sleep analyzer for real time detection of sleep anomalies, part of an effective AAL system. The proposed solution is based on combination of non-invasive sensors and an algorithm for sleep analysis with two stages - low and high level reasoning. It also offers the opportunity to include third party devices. Using the analyzer we can monitor basic sleep behavior and to detect sleep anomalies, which can serve as an important indicator ..

References

    Issue

    in Proceedings of International Conference on Telecommunications and Signal Processing (TSP), 27-29 June 2016, pp. 400-404, 2016, Austria, DOI 10.1109/TSP.2016.7760906

    Copyright IEEE

    Цитирания (Citation/s):
    1. Fahim, M., Sillitti, A., "Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review", IEEE Access, vol. 7, pp. 81664-81681, 2019, DOI: 10.1109/ACCESS.2019.2921912. - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Yahaya, S.W., Langensiepen, C., Lotfi, A., "Anomaly detection in activities of daily living using one-class support vector machine", Advances in Intelligent Systems and Computing, vol. 840, pp. 362-371, 2019, DOI: 10.1007/978-3-319-97982-3_30. - 2019 - в издания, индексирани в Scopus или Web of Science
    3. Howedi, A., Lotfi, A., Pourabdollah, A., "A multi-scale fuzzy entropy measure for anomaly detection in activities of daily living", ACM International Conference Proceeding Series, pp. 383-390, 2020, DOI: 10.1145/3389189.3397987. - 2020 - в издания, индексирани в Scopus или Web of Science
    4. Al-amri, R., Murugesan, R. K., Man, M., Abdulateef, A. F., Al-Sharafi, M. A., & Alkahtani, A. A. (2021). A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data. Applied Sciences, 11(12), 5320. - 2021 - в издания, индексирани в Scopus или Web of Science
    5. Gupta, D., Gupta, M., Bhatt, S., & Tosun, A. S. (2021, August). Detecting anomalous user behavior in remote patient monitoring. In 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI) (pp. 33-40). IEEE. - 2021 - в издания, индексирани в Scopus или Web of Science
    6. Yahaya, S. W., Langensiepen, C., & Lotfi, A. (2018, September). Anomaly detection in activities of daily living using one-class support vector machine. In UK Workshop on Computational Intelligence (pp. 362-371). Springer, Cham. - 2018 - в издания, индексирани в Scopus или Web of Science
    7. Al-Amri, R., Murugesan, R.K., Alshari, E.M., Alhadawi, H.S., "Toward a Full Exploitation of IoT in Smart Cities: A Review of IoT Anomaly Detection Techniques", Lecture Notes in Networks and Systems, vol. 322, pp. 193-214, 2022, DOI: 10.1007/978-3-030-85990-9_17. - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science