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. 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
  4. 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
  5. 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
  6. 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={},}

  7. Conference paper

  8. 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
  9. 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