Publications

    Book chapter

  1. Delaram Amiri, Arman Anzanpour, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg, Nikil Dutt, Marco Levorato, "Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control", Fog Computing: Theory and Practice, Wiley, 2019

  2. Journal paper

  3. Emad Kasaeyan Naeini, Mingzhe Jiang, Elise Syrjälä, Michael-David Calderon, Riitta Mieronkoski, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana Nelson, Amir M Rahmani, "A Prospective Study Evaluating a Pain Assessment Tool in Postoperative Environment: A Protocol for Algorithm Testing and Enhancement", JMIR Research Protocols Jorunal, 2020
  4. Hannakaisa Niela-Vilen, Amir M. Rahmani, Pasi Liljeberg, and Anna Axelin, "Being ‘A Google Mom’ or Securely Monitored at Home – Perceptions of Remote Monitoring in Maternity Care", Wiley's Journal of Advanced Nursing, 2019
  5. Maximilian Götzinger, Arman Anzanpour, Iman Azimi, Nima TaheriNejad, Axel Jantsch, Amir M. Rahmani, Pasi Liljeberg, "Confidence-Enhanced Early Warning Score Based on Fuzzy Logic", ACM/Springer Mobile Networks and Applications (ACM/Springer-MONET), 2019
  6. Delaram Amiri, Arman Anzanpour, Iman Azimi, Marco Levorato, Pasi Liljeberg, Nikil Dutt, and Amir M. Rahmani, "Context-Aware Control of Wearable Sensors via Edge Computing", ACM Transactions on Computing for Healthcare (ACM-HEALTH), 2019
  7. Johanna Saarikko; Hannakaisa Niela-Vilen; Eeva Ekholm; Lotta Hamari; Iman Azimi; Pasi Liljeberg; Amir M Rahmani; Eliisa Löyttyniemi; Anna Axelin;, "Continuous 7-month Internet of Things -based monitoring of Health Parameters of Pregnant and Postpartum Women: A Feasibility Study", JMIR, 2020
  8. Geng Yang, Zhibo Pang, Jamal Deen, Mianxiong Dong, Yuan-Ting Zhang, Nigel H. Lovell, and Amir M. Rahmani, "Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies", IEEE Journal of Biomedical and Health Informatics, 2020
  9. Manoj Vishwanath, Salar Jafarlou, Ikhwan Shin, Miranda M. Lim, Nikil Dutt, Amir M. Rahmani, Hung Cao, "Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice", MDPI Sensors 2020, 2020
  10. Han, H. J., Labbaf, S., Borelli, J. L., Dutt, N. & Rahmani, A. M., "Objective stress monitoring based on wearable sensors in everyday settings", Taylor & Francis Journal of Medical Engineering and Technology, 2020
  11. Iman Azimi, Olugbenga Oti, Sina Labbaf, Hannakaisa Niela-Vilen, Anna Axelin, Nikil Dutt, Pasi Liljeberg, and Amir M. Rahmani, "Personalized Maternal Sleep Quality Assessment: An Objective IoT-based Longitudinal Study", IEEE Access, 2019
    BibTex
    @ARTICLE{8758420,
    author={I. {Azimi} and O. {Oti} and S. {Labbaf} and H. {Niela-Vilén} and A. {Axelin} and N. {Dutt} and P. {Liljeberg} and A. M. {Rahmani}},
    journal={IEEE Access},
    title={Personalized Maternal Sleep Quality Assessment: An Objective IoT-based Longitudinal Study},
    year={2019},
    volume={7},
    number={},
    pages={93433-93447},
    abstract={Sleep is a composite of physiological and behavioral processes that undergo substantial changes during and after pregnancy. These changes might lead to sleep disorders and adverse pregnancy outcomes. Several studies have investigated this issue; however, they were restricted to subjective measurements or short-term actigraphy methods. This is insufficient for a longitudinal maternal sleep quality evaluation. A longitudinal study: 1) requires a long-term data collection approach to acquire data from everyday routines of mothers and 2) demands a sleep quality assessment method exploiting a large volume of multivariate data to assess sleep adaptations and overall sleep quality. In this paper, we present an Internet-of-Things-based long-term monitoring system to perform an objective sleep quality assessment. We conduct longitudinal monitoring, where 20 pregnant mothers are remotely monitored for six months of pregnancy and one month postpartum. To evaluate sleep quality adaptations, we: 1) extract several sleep attributes and study their variations during the monitoring and 2) propose a semi-supervised machine learning approach to create a personalized sleep model for each subject. The model provides an abnormality score, which allows an explicit representation of the sleep quality in a clinical routine, reflecting possible sleep quality degradation with respect to her own data. Sleep data of 13 participants (out of 20) are included in our analysis, as their data are adequate for the study, including 172.15±33.29 days of sleep data per person. Our fine-grained objective measurements indicate that the sleep duration and sleep efficiency are deteriorated in pregnancy and notably in postpartum. In comparison to the mid of the second trimester, the sleep model indicates the increase of sleep abnormality at the end of pregnancy (2.87 times) and postpartum (5.62 times). We also show that the model enables individualized and effective care for sleep disturbances during pregnancy, as compared to a baseline method.},
    keywords={Task analysis;Sensors;Internet of Things;Resource management;Remuneration;Device-to-device communication;Wireless sensor networks;Anomaly detection;Internet of Things;longitudinal study;maternity care;sleep monitoring;sleep quality assessment},
    doi={10.1109/ACCESS.2019.2927781},
    ISSN={2169-3536},
    month={},}

  12. Conference paper

  13. Holly Borg, Hannah Vasquez, Michelle Heredia, Melissa Navarrete, Nikil Dutt, Amir M. Rahmani, and Yuqing Guo, "A Self-Management Model: Using Wearable Devices for Continuous Monitoring during the Perinatal Period", National Perinatal Association Annual Conference Perinatal Care and the 4th Trimester” (NPA’20), 2020
  14. Emad Kasaeyan Naeini, Sina Shahhosseini, Ajan Subramanian, Tingjue Yin, Amir M. Rahmani, Nikil Dutt, "An Edge-Assisted and Smart System for Real-Time Pain Monitoring", IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, 2019
  15. Manoj Vishwanath, Salar Jafarlou, Ikhwan Shin, Nikil Dutt, Amir M. Rahmani, Miranda Lim, Hung Cao,, "Classification of Mild Traumatic Brain Injury in a Mouse Model Using Machine Learning Approaches", 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020
  16. Florian Maurer, Bryan Donyanavard, Amir M. Rahmani, Nikil Dutt, and Andreas Herkersdorf, "Constraint-Awareness in Self-Optimizing MPSoCs", Self-Awareness in Cyber-Physical Systems Workshop (SelPhys’20), 2020
  17. Dongjoo Seo, Sina Shahhosseini, Milad Asgari Mehrabadi, Bryan Donyanavard, Sung-Soo Lim, Nikil Dutt, and Amir Rahmani, "Dynamic iFogSim: A Framework for Full-Stack Simulation of Dynamic Resource Management in IoT Systems", IEEE International Conference on Omni-layer Intelligent Systems (COINS), 2020
  18. Momahmmad-Reza Nakhkash, Anil Kanduri, Amir M. Rahmani, and Pasi Liljeberg, "End-to-End Approximation for Characterizing Energy Efficiency of IoT Applications", IEEE Nordic Circuits and Systems Conference (NORCAS’19), 2019
  19. Lucretia Williams, Gillian R. Hayes, Yuqing Guo, Amir M. Rahmani, Nikil Dutt, "HCI and mHealth Wearable Tech: A Multidisciplinary Research Challenge", ACM CHI Conference on Human Factors in Computing Systems, Case Study (CHI'20-Case Study), 2020
  20. Juho Laitala, Mingzhe Jiang, Elise Syrjälä, Emad Kasaeyan Naeini, Antti Airola, Amir M. Rahmani, Nikil D. Dutt, Pasi Liljeberg, "Robust ECG R-peak Detection Using LSTM", The 35th ACM/SIGAPP Symposium On Applied Computing (SAC'20), 2019
  21. Jung-Ah Lee, Anthony Park, and Amir M. Rahmani, "Sleep Duration and Quality in Dementia Caregivers: Wearable Iot Technology", Western Institute of Nursing's 52nd Annual Communicating Nursing Research Conference (WINURSING’20), 2020
  22. Amir M. Rahmani, Sina Labbaf , Yuqing Guo, Joseph Onwuka, Ali Tazarv, Marco Levorato, and Nikil Dutt, "UNITE Technological Design: Promotion of Maternal Health Equity in Underserved Communities in Orange County, California", Institute for Diversity and Health Equity 2020 National Leadership and Education Conference (NLEC’20), 2020