A Sleep Monitoring System Using Ultrasonic Sensors

Article


Shammi, U. A. and Ahad, M. 2022. A Sleep Monitoring System Using Ultrasonic Sensors. International Journal of Biomedical Soft Computing and Human Sciences. 27 (1), pp. 13-20. https://doi.org/10.24466/ijbschs.27.1_13
AuthorsShammi, U. A. and Ahad, M.
Abstract

Sleep is an important activity for every human being. Proper and balanced periods of sleep are essential for an individual’s health state. Sleep posture is related to the quality of sleep. Therefore, during sleep, it is crucial to study the sleep postures and limbs’ movements. For some diseases, body positions and movements are intra-related (i.e., restless legs syndrome and patellofemoral pain syndrome). We propose a sleep monitoring system based on ultrasonic sensors. Our system does not need any on-body sensors to wear, nor does it need any kind of additional action from the users outside the daily routine. Ultrasonic sensors can be used for finding out the distance from the sensor to any object/subject that is in front of it. By wiring ultrasonic sensors with an Arduino Mega board, we can calculate this distance. Later, we put that sensor value on to ‘Processing’ software for further data analysis. The main contributions of this paper are to find if the bed is empty or not, if the person is sitting or lying on the bed, and to find out at which posture the subject is sleeping. We present the result of our analysis as a medical report, which will help a doctor for finding the sleeping posture of a person and what might be the reason for body pain, fatigue, or other sleep disorders. Our proposed method can be used in normal households with a minimal arrangement.

JournalInternational Journal of Biomedical Soft Computing and Human Sciences
Journal citation27 (1), pp. 13-20
ISSN2185-2421
Year2022
PublisherBiomedical Fuzzy Systems Association (BMFSA)
Accepted author manuscript
License
File Access Level
Repository staff only
Digital Object Identifier (DOI)https://doi.org/10.24466/ijbschs.27.1_13
Web address (URL)http://www.ijbschs.org/ijbschs-contents/contents.html
Publication dates
OnlineJul 2022
Publication process dates
Deposited26 Jul 2023
Copyright holder© 2022, Biomedical Fuzzy Systems Association
Permalink -

https://repository.uel.ac.uk/item/8qqvy

  • 54
    total views
  • 1
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

Learn Programming with C: An Easy Step-by-Step Self-Practice Book for Learning C
Imran, S. M. S. and Ahad, M. A. R. 2024. Learn Programming with C: An Easy Step-by-Step Self-Practice Book for Learning C. CRC Press: Taylor & Francis Group.
Deep learning with image-based autism spectrum disorder analysis: A systematic review
Uddin, M. Z., Shahriar, M. A., Mahamood, M. N., Alnajjar, F., Pramanik, M. I. and Ahad, M. A. R. 2024. Deep learning with image-based autism spectrum disorder analysis: A systematic review. Engineering Applications of Artificial Intelligence. 127 (Art. 107185). https://doi.org/doi.org/10.1016/j.engappai.2023.107185
Annotator-dependent uncertainty-aware estimation of gait relative attributes
Shehata, A., Makihara, Y., Muramatsu, D., Ahad, M. and Yasushi, Y. 2023. Annotator-dependent uncertainty-aware estimation of gait relative attributes. Pattern Recognition. 136 (Art. 109197). https://doi.org/10.1016/j.patcog.2022.109197
HID 2022: The 3rd International Competition on Human Identification at a Distance
Yu, S., Huang, Y., Wang, L., Makihara, Y., Wang, S., Ahad, M. and Nixon, M. 2022. HID 2022: The 3rd International Competition on Human Identification at a Distance. IJCB 2022: IEEE International Joint Conference on Biometrics. Abu Dhabi, UAE 10 - 13 Dec 2023 IEEE. https://doi.org/10.1109/IJCB54206.2022.10007993
Automated detection approaches to autism spectrum disorder based on human activity analysis: A review
Rahman, S., Ahmed, S. F., Shahid, O., Arrafi, M. A. and Ahad, M. A. R. 2022. Automated detection approaches to autism spectrum disorder based on human activity analysis: A review. Cognitive Computation. 14, pp. 1773-1800. https://doi.org/10.1007/s12559-021-09895-w
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?
Nazmus Sakib, A. H. M., Basak, P., Doha Uddin, S., Mustavi Tasin, S. and Ahad, M. 2022. Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity? 3rd International Conference on Activity and Behavior Computing (ABC 2021). Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_10
Identification of Food Packaging Activity Using MoCap Sensor Data
Anwar, A., Islam Tapotee, M., Saha, P. and Ahad, M. 2022. Identification of Food Packaging Activity Using MoCap Sensor Data. 3rd International Conference on Activity and Behavior Computing (ABC 2021). Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_11
Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features
Pritom, Y. A., Rahman, M. S., Rahman, H. R., Kowshik, M. A. and Ahad, M. 2022. Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features. 3rd International Conference on Activity and Behavior Computing (ABC 2021). Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_12
Bento Packaging Activity Recognition Based on Statistical Features
Rakib Sayem, F., Sheikh, M. M. and Ahad, M. 2022. Bento Packaging Activity Recognition Based on Statistical Features. 3rd International Conference on Activity and Behavior Computing (ABC 2021). Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_13
MUMAP: Modified Ultralightweight Mutual Authentication protocol for RFID enabled IoT networks
Raju, M. H., Ahmed, M. U. and Ahad, M. A. R. 2021. MUMAP: Modified Ultralightweight Mutual Authentication protocol for RFID enabled IoT networks. Journal of the Institute of Industrial Applications Engineers. 9 (2), pp. 33-39. https://doi.org/10.12792/JIIAE.9.33
Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques
Islam, M. R., Moni, M. A., Islam, M. M., Rashed-Al-Mahfuz, M., Islam, M. S., Hasan, M. K., Hossain, M. S., Ahmad, M., Uddin, S., Azad, A., Alyami, S. A., Ahad, M. A. R. and Lió, P. 2021. Emotion Recognition from EEG Signal Focusing on Deep Learning and Shallow Learning Techniques. IEEE Access. 9, pp. 94601-94624. https://doi.org/10.1109/ACCESS.2021.3091487
Static Postural Transition-based Technique and Efficient Feature Extraction for Sensor-based Activity Recognition
Ahmed, M., Das Antar, A. and Ahad, M. 2021. Static Postural Transition-based Technique and Efficient Feature Extraction for Sensor-based Activity Recognition. Pattern Recognition Letters. 147, pp. 25-33. https://doi.org/10.1016/j.patrec.2021.04.001
Activity Recognition from Accelerometer Data Based on Supervised Learning for Wireless Sensor Network
Israt, F. A., Hossain, T., Inoue, S. and Ahad, M. A. R. 2021. Activity Recognition from Accelerometer Data Based on Supervised Learning for Wireless Sensor Network. International Journal of Biomedical Soft Computing and Human Sciences. 26 (2), pp. 73-86. https://doi.org/10.24466/ijbschs.26.2_73
Action recognition using Kinematics Posture Feature on 3D skeleton joint locations
Ahad, M. A. R., Ahmed, M., Antar, A. D., Makihara, Y. and Yagi. Y. 2021. Action recognition using Kinematics Posture Feature on 3D skeleton joint locations. Pattern Recognition Letters. 145, pp. 216-224. https://doi.org/10.1016/j.patrec.2021.02.013
Exploring Human Activities Using eSense Earable Device
Islam, M. S., Hossain, T., Ahad, M. and Inoue, S. 2021. Exploring Human Activities Using eSense Earable Device. in: Ahad, M., Inoue, S., Roggen, D. and Fujinami, K. (ed.) Activity and Behavior Computing Springer Singapore. pp. 169–185
Contactless Human Monitoring: Challenges and Future Direction
Mahbub, U., Rahman, T. and Ahad, M. 2021. Contactless Human Monitoring: Challenges and Future Direction. in: Ahad, M., Mahbub, U. and Ahad, M. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 335-364
Contactless Human Emotion Analysis Across Different Modalities
Nahid, N., Rahman, A. and Ahad, M. 2021. Contactless Human Emotion Analysis Across Different Modalities. in: Ahad, M., Mahbub, U. and Rahman, T. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 237-269
Contactless Fall Detection for the Elderly
Nahian, M. J. A., Raju, M. H., Tasnim, Z., Mahmud, M., Ahad, M. and Kaiser, M. S. 2021. Contactless Fall Detection for the Elderly. in: Ahad, M., Mahbub, U. and Rahman, T. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 203-235
Signal Processing for Contactless Monitoring
Billah, M. S., Ahad, M. and Mahbub, U. 2021. Signal Processing for Contactless Monitoring. in: Ahad, M., Mahbub, U. and Rahman, T. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 113-144
Skeleton-Based Activity Recognition: Preprocessing and Approaches
Sarker, S., Rahman, S., Hossain, T., Faiza Ahmed, S., Jamal, L. and Ahad, M. 2021. Skeleton-Based Activity Recognition: Preprocessing and Approaches. in: Ahad, M., Mahbub, U. and Rahman, T. (ed.) Contactless Human Activity Analysis Springer, Cham. pp. 48-81
IoT Sensor-Based Activity Recognition: Human Activity Recognition
Ahad, M., Antar, A. D. and Ahmed, M. 2021. IoT Sensor-Based Activity Recognition: Human Activity Recognition. Springer, Cham.
A Method for Sensor-Based Activity Recognition in Missing Data Scenario
Hossain, T., Ahad, M. A. R. and Inoue, S. 2020. A Method for Sensor-Based Activity Recognition in Missing Data Scenario. Sensors. 20 (14), pp. 1-23. https://doi.org/10.3390/s20143811
An AI-based Visual Aid with Integrated Reading Assistant for the Completely Blind
Khan, M. A., Paul, P., Rashid, M., Hossain, M. and Ahad, M. 2020. An AI-based Visual Aid with Integrated Reading Assistant for the Completely Blind. IEEE Transactions on Human-Machine Systems. 50 (6), pp. 507-517. https://doi.org/10.1109/THMS.2020.3027534