Leo Seļāvo

Projekti

3 rezultāti

• Uz fiziku balstītu mašīnmācīšanās metožu pielietošana glikozes līmeņa tukšā dūšā prognozēšanai un ietekmēšanai (PRAESIIDIUM) 01.01.2023. – 31.12.2025. Noslēgts
• Viedo materiālu, fotonikas, tehnoloģiju un inženierijas ekosistēma (MOTE) 01.01.2023. – 31.12.2024. Noslēgts
• Multidisciplināra pieeja COVID19 un citu nākotnes epidēmiju monitorēšanai, kontrolei un ierobežošanai Latvijā (COV-MITIGATE) 01.07.2020. – 31.12.2020. Noslēgts

Publikācijas

• 2024

  A Set of Tools and Data Management Framework for the IoT–Edge–Cloud Continuum(atveras jaunā logā)

  Judvaitis J, Blumbergs E, Arzovs A, Mackus AI, Balass R, Selavo L.

  Applied System Innovation (ISSN 2571-5577), 2024, Sēj. 7 (6), MDPI

  Kopsavilkums

  Developing and managing complex IoT–Edge–Cloud Continuum (IECC) systems are challenging due to the system complexity and diversity. Internet of Things (IoT), Edge, and Cloud components combined with artificial intelligence (AI) in data processing systems must ensure strong security and privacy for data sources. The approach of the IECC Data Management Framework (DMF) introduces a novel combination of multiple easy-to-configure plugin environments using data visualization features. These contributions collectively address the critical challenges inherent in heterogeneous environments such as scalability, data privacy, and configuration management by standardizing data flow configurations and increasing stakeholder trust in sensitive applications, particularly in critical infrastructure monitoring.

  Indeksēts Scopus / WoS
• 2024

  PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring—A Comprehensive Review(atveras jaunā logā)

  Didzis Lapsa, Rims Janeliukstis, Margus Metshein, Leo Selavo

  Applied Sciences, Sēj. 14 (17), lpp. 7451, MDPI

  Kopsavilkums

  The monitoring of hemodynamic parameters, such as heart rate and blood pressure, provides valuable indications of overall cardiovascular health. It is preferable that such monitoring is non-invasive and in real time via an affordable, compact and small-scale device for maximum convenience. Numerous literature sources have exploited derivations of these parameters from photoplethysmogram (PPG) and electrical bioimpedance (EBI) signal measurements through the use of calculation algorithms of varying complexity. Compared to electrocardiogram (ECG), these measurement techniques have a merit of well-established practices of designing a wearable device that could conveniently be put on a wrist. The current paper provides a comprehensive review on the use of PPG and EBI measurement techniques in the context of hemodynamic parameter monitoring using a wearable device. A special emphasis is placed on the most basic hemodynamic parameter—heart rate—describing different algorithms of heart rate detection and monitoring. The last section provides an overview of commercially available and in-home wearable device technologies based on PPG and EBI measurements, their design challenges, and future prospects.

  Indeksēts Scopus / WoS · Scopus procentile: 75
• 2024

  Distributed Learning in the IoT–Edge–Cloud Continuum(atveras jaunā logā)

  Arzovs, A.; Judvaitis, J.; Nesenbergs, K.; Selavo, L

  Machine Learning and Knowledge Extraction, Sēj. 6 (1), lpp. 283-315., MDPI

  Kopsavilkums

  The goal of the IoT–Edge–Cloud Continuum approach is to distribute computation and data loads across multiple types of devices taking advantage of the different strengths of each, such as proximity to the data source, data access, or computing power, while mitigating potential weaknesses. Most current machine learning operations are currently concentrated on remote high-performance computing devices, such as the cloud, which leads to challenges related to latency, privacy, and other inefficiencies. Distributed learning approaches can address these issues by enabling the distribution of machine learning operations throughout the IoT–Edge–Cloud Continuum by incorporating Edge and even IoT layers into machine learning operations more directly. Approaches like transfer learning could help to transfer the knowledge from more performant IoT–Edge–Cloud Continuum layers to more resource-constrained devices, e.g., IoT. The implementation of these methods in machine learning operations, including the related data handling security and privacy approaches, is challenging and actively being researched. In this article the distributed learning and transfer learning domains are researched, focusing on security, robustness, and privacy aspects, and their potential usage in the IoT–Edge–Cloud Continuum, including research on tools to use for implementing these methods. To achieve this, we have reviewed 145 sources and described the relevant methods as well as their relevant attack vectors and provided suggestions on mitigation.

  Indeksēts Scopus / WoS · Scopus procentile: 91
• 2023

  Testbed Facilities for IoT and Wireless Sensor Networks: A Systematic Review(atveras jaunā logā)

  Jānis Judvaitis, Valters Āboliņš, Amr Elkenawy, Rihards Balašs, Leo Seļāvo, Kaspars Ozols

  Journal of Sensor and Actuator Networks, Sēj. 12 (2), lpp. 48, MDPI

  Kopsavilkums

  Abstract As the popularity and complexity of WSN devices and IoT systems are increasing, the testing facilities should keep up. Yet, there is no comprehensive overview of the landscape of the testbed facilities conducted in a systematic manner. In this article, we provide a systematic review of the availability and usage of testbed facilities published in scientific literature between 2011 and 2021, including 359 articles about testbeds and identifying 32 testbed facilities. The results of the review revealed what testbed facilities are available and identified several challenges and limitations in the use of the testbed facilities, including a lack of supportive materials and limited focus on debugging capabilities. The main contribution of this article is the description of how different metrics impact the uasge of testbed facilities, the review also highlights the importance of continued research and development in this field to ensure that testbed facilities continue to meet the changing needs of the ever-evolving IoT and WSN domains.

  Indeksēts Scopus / WoS · Scopus procentile: 94 · SNIP: 1.389

  DOI(atveras jaunā logā)
• 2016

  Wearable Sensor System for Human Biomechanics Monitoring

  Atis Hermanis, Ricards Cacurs, Krisjanis Nesenbergs, Modris Greitans, Emil Syundyukov, Leo Selavo

  Kopsavilkums

  <p>Atis Hermanis, Ricards Cacurs, Krisjanis Nesenbergs, Modris Greitans, Emil Syundyukov, Leo Selavo "Wearable Sensor System for Human Biomechanics Monitoring". (2016, February). In EWSN (pp. 247-248).</p> <p>All authors: Atis Hermanis, Ricards Cacurs, Krisjanis Nesenbergs, Modris Greitans, Emil Syundyukov, Leo Selavo.</p> <p><strong>Abstract:</strong></p> <p>We propose a system that combines wired and wireless sensors for human bio-mechanics monitoring during physiatric rehabilitation. The wearable system consists of sensors that are able to acquire vital signs data from and array of sensor nodes attached to patient's body. Data is transmitted in real-time via wireless communication channel to a mobile application for analysis, visualization and communication. Prototypes were developed and tested in real life conditions by involving patients completing rehabilitation exercises with physiotherapists. The user experience of the demonstration will include prototypes for different applications: knee joint and 3D printed palm prosthetic dynamics monitoring, posture and head position detection in real-time.</p>

Patenti

• 2018 LR patents Nr. 15308

  Mobila ierīce bezvadu sensoru tīklu un to mezglu efektīvai izstrādei mērķa vide

  Izgudrotāji:Leo SEĻĀVO, Krišjānis NESENBERGS, Jānis JUDVAITIS, Didzis LAPSA, Rihards BALAŠS, Arnis SALMIŅŠ, Modris GREITĀNS

  Apraksts un saite

  LR patents Nr. 15308 “Mobila ierīce bezvadu sensoru tīklu un to mezglu efektīvai izstrādei mērķa vide”, Izgudrotāji Leo SEĻĀVO, Krišjānis NESENBERGS, Jānis JUDVAITIS, Didzis LAPSA, Rihards BALAŠS, Arnis SALMIŅŠ, Modris GREITĀNS, publicēts 20.03.2018.

  Skatīt patentu Espacenet datubāzē(atveras jaunā logā)