Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Internet of things based multi-sensor patient fall detection system

Loading full text...

Full text loading...

/deliver/fulltext/htl/6/5/HTL.2018.5121.html;jsessionid=cm7jld19d6lpb.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fhtl.2018.5121&mimeType=html&fmt=ahah

References

    1. 1)
      • 8. SparkFun Triple Axis Accelerometer and Gyro Breakout - MPU-6050 - SEN-11028 SparkFun Electronics.: Sparkfun.com, 2017, available at https://www.sparkfun.com/products/11028, accessed: 9 December 2017.
    2. 2)
      • 14. Saedsayad.com.: ‘K-Nearest neighbors – classification’, available at http://www.saedsayad.com/k_nearest_neighbors.htm, accessed: 9 December 2017.
    3. 3)
      • 6. Harrou, F., Zerrouki, N., Sun, Y., et al: ‘A simple strategy for fall events detection’. 2016 IEEE 14th Int. Conf. on Industrial Informatics (INDIN), Poitiers, France, 2016, pp. 332336.
    4. 4)
      • 1. Telegraph.co.uk.: ‘90 patients die from falls in hospital in one year’, 2013, Available at http://www.telegraph.co.uk/news/health/news/10114014/90-patients-die-from-falls-in-hospital-in-one-year.html, [Accessed: 9 December 2017].
    5. 5)
      • 3. Kurniawan, A., Hermawan, A., Purnama, I.: ‘A wearable device for fall detection elderly people using tri dimensional accelerometer’. 2016 Int. Seminar on Intelligent Technology and Its Applications, Lombok, Indonesia, 2016, pp. 671674.
    6. 6)
      • 10. ThingSpeak for IoT’.: Thingspeak.com, 2019, available at: https://thingspeak.com/pages/commercial_learn_more, accessed: 1 May 2019.
    7. 7)
      • 20. Zhu, W., Zeng, N., Wang, N.: ‘Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations’. Health Care and Life Sciences, 14 November 2010, Baltimore, Maryland, USA: NorthEast SAS Users Group, 2010.
    8. 8)
      • 12. Vallabh, P., Malekian, R., Bogatinoska, N.: ‘Fall detection using machine learning algorithms’. 2016 24th Int. Conf. on Software, Telecommunications, and Computer Networks (SoftCOM), Split, Croatia, 2016.
    9. 9)
      • 21. Socialcompare. com.: ‘RaspberryPI models comparison’, available at http://socialcompare.com/en/comparison/raspberrypi-models-comparison, accessed: 29 April 2018.
    10. 10)
      • 11. Whatis.com.: ‘RESTful API’, available at: https://searchmicroservices.techtarget.com/definition/RESTful-API, accessed: 21 April 2018.
    11. 11)
      • 7. Raspberry Pi 3 - DEV-13825 - SparkFun Electronics’.: Sparkfun.com, 2017, available at: https://www.sparkfun.com/products/13825, [accessed: 9 December 2017].
    12. 12)
      • 13. Wikipedia.com.: ‘Instance-based learning’, available at https://en.wikipedia.org/wiki/Instance-based_learning, accessed: 8 December 2017.
    13. 13)
      • 5. Alwan, M., Rajendran, P., Kell, S., et al: ‘A smart and passive floorvibration based fall detector for elderly’. 2006 2nd Int. Conf. on Information & Communication Technologies, Damascus, Syria, 2006, pp. 10031007.
    14. 14)
      • 17. Wikipedia.com.: ‘Naïve Bayes classifier’, available at: https://en.wikipedia.org/wiki/Naive_Bayes_classifier, accessed: 9, December 2017.
    15. 15)
      • 4. Mezghani, N., Ouakrim, Y., Islam, M.D., et al: ‘Context aware adaptable approach for fall detection bases on smart textile’. 2017 IEEE EMBS Int. Conf. on Biomedical & Health Informatics, Orlando, FL, USA, 2017, pp. 473476.
    16. 16)
      • 2. Ncbi.nlm.nih.gov.: ‘Falls among adult patients hospitalized in the United States: prevalence and trends’, 2013, ,available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3572247/, accessed: 9 December 2017.
    17. 17)
      • 18. Wikipedia.com.: ‘Probabilistic classification’, available at: https://en.wikipedia.org/wiki/Probabilistic_classification, accessed: 9 December 2017.
    18. 18)
      • 9. Raspberry Pi Camera Module V2 - DEV-14028 - SparkFun Electronics’.: Sparkfun.com, 2017, available at https://www.sparkfun.com/products/14028, accessed: 9 December 2017.
    19. 19)
      • 15. Machinelearningmastery.com.: ‘Overfitting and underfitting with machine learning algorithms’, Available at: https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/, accessed: 9 December 2017.
    20. 20)
      • 16. Genomicsclass.github.io.: ‘Crossvalidation’, available at http://genomicsclass.github.io/book/pages/crossvalidation.html, accessed: 9 December 2017.
    21. 21)
      • 19. Wikipedia.com.: ‘Posterior probability’, available at: https://en.wikipedia.org/wiki/Posterior_probability, accessed: 9 December 2017.
http://iet.metastore.ingenta.com/content/journals/10.1049/htl.2018.5121
Loading

Related content

content/journals/10.1049/htl.2018.5121
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address