An IoT Based System to Detect a Person/Wheelchair Fall

  • Authors

    • Dr. H. Shaheen
    • E. Himabindu
    • Dr. T. Sreenivasulu
    • Dr. Rajasekar Rangasamy
    https://doi.org/10.14419/ijet.v7i3.20.26739
  • measuring system, gyro meter, microcontroller

  • Abstract

    Keeping an in depth tab of recent folks or folks on chair with bound health conditions for his or her health and safety is a very important task. With maturity, weak bones and weakness because of alternative health connected problems could lead to will increase risk of falling. A supervisor might not continually be on the market with them and if correct assistance is not provided at the correct time it should cause larger health considerations which will need extra resources for treatment. For this purpose we've projected a wise IOT Fall Detection System exploitation acceptable sensors that square measure integrated facilitate|to assist} report these incidents to assist avail help at the correct time to forestall additional injury to health. The same system uses sensors like associate degree measuring system to live the speed of the person, a rotating mechanism to live the person’s orientation so as to live their stability, a load sensing element once the system is employed by an individual employing a chair to live their weight, a Wi-Fi module and a microcontroller that sends the general readings to alert the involvedthose that shall give with the right suggests that to assist the person in want. The microcontroller receives all the info from the sensors and perpetually transmits and monitors the acceleration and also the orientation of the person. Any fast abrupt modification within the system which will result from a fall is taken into account as a ‘fall’ and is reported . a serious concern would be that not all fast movement may end up from a fall and be thought of as a matter of concern. To avoid this warning a napbutton is provided to snooze the system. This button will be ironed before a definite time say 15-30 seconds to prevent the system from causation the alert, thus avoiding any confusion and panic. this method will be mounted to the person’s chair or will be created compact to be created into a wearable device which will be worn on the hand.

     

  • References

    1. [1] Luca Catarinucci, Danilo De Donno, Luca Mainetti, Luca Palano, Luigi Patrono, Maria Laura Stefanizzi, and Luciano Tarricone “An IoT-Aware Architecture for Smart Healthcare Systems†vol. 2, 2015.

      [2] Preeth, S.K.S.L., Dhanalakshmi, R., Kumar, R.,Shakeel PM.An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system.Journal of Ambient Intelligence and Humanized Computing.2018:1–13. https://doi.org/10.1007/s12652-018-1154-z

      [3] Yu Liu, Beibei Dong, Benzhen Guo, Jingjing Yang and Wei Peng “Combination of Cloud Computing and Internet of Things (IOT) in Medical Monitoring Systems†Vol.8, No.12, pp. 367-376, International Journal of Hybrid Information Technology, 2015.

      [4] P. Mohamed Shakeel; Tarek E. El. Tobely; Haytham Al-Feel; Gunasekaran Manogaran; S. Baskar., “Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensorâ€, IEEE Access, 2019, Page(s): 1

      [5] Bing Sun, Yang Wang and Jacob Banda “Gait Characteristic Analysis and Identification Based on the iPhone’s Accelerometer and Gyrometerâ€, 2014.

      [6] Davrondzhon Gafurov, Einar Snekkenes and Patrick Bours “Gait Authentication and Identification Using Wearable Accelerometer Sensor†Conference Paper, July 2007.

      [7] Shakeel, P.M., Tolba, A., Al-Makhadmeh, Zafer Al-Makhadmeh, Mustafa Musa Jaber, “Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networksâ€, Neural Computing and Applications,2019,pp1-14.https://doi.org/10.1007/s00521-018-03972-2

      [8] Youngbum Lee and Myoungho Lee “Implementation of Accelerometer Sensor Module and Fall Detection Monitoring System based on Wireless Sensor Networkâ€, 2011.

      [9] Shakeel PM, Manogaran G., “Prostate cancer classification from prostate biomedical data using ant rough set algorithm with radial trained extreme learning neural networkâ€, Health and Technology, 2018:1-9.https://doi.org/10.1007/s12553-018-0279-6

      [10] Manogaran G, Shakeel PM, Hassanein AS, Priyan MK, Gokulnath C. Machine-Learning Approach Based Gamma Distribution for Brain Abnormalities Detection and Data Sample Imbalance Analysis. IEEE Access. 2018 Nov 9.DOI 10.1109/ACCESS.2018.2878276

      [11] Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, Marimuthu Palaniswami “A Vision, Architectural Elements, and Future Directions†.

      [12] Ryan P. Hubble1 *, Geraldine A. Naughton2 , Peter A. Silburn3 , Michael H. Cole

      [13] Ryan P. Hubble, Geraldine A. Naughton, Peter A. Silburn, Michael H. Cole “Wearable Sensor Use for Assessing Standing Balance and Walking Stability in People with Parkinson’s Disease: A Systematic Review†, 2015.

      [14] S.M. Riazul Islam, Daehan Kwak, Md. Humaun Kabir, Mahmud Hossain, and Kyung-Sup Kwak “The internet of things for health care: A comprehensive surverâ€

      [15] H. Shaheen, “Ardent Exactitude Based Agriculture Using Sensorsâ€, “International Journal of Scientific & Engineering Researchâ€, Volume 8, Issue 7, July 2017. ISSN 2229-5518, pp 7- 11.

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  • How to Cite

    H. Shaheen, D., Himabindu, E., T. Sreenivasulu, D., & Rajasekar Rangasamy, D. (2018). An IoT Based System to Detect a Person/Wheelchair Fall. International Journal of Engineering & Technology, 7(3.20), 735-737. https://doi.org/10.14419/ijet.v7i3.20.26739

    Received date: 2019-01-29

    Accepted date: 2019-01-29