Ans-Assist: Robust Human Fall Detection for Unconstraint Smartphone Positions using Modified Long Short-Term Memory Cell
Maria Seraphina Astriani1, Yaya Heryadi2, Gede Putra Kusuma3, Edi Abdurachman4
1Maria Seraphina Astriani*, Computer Science Department, Faculty of Computing and Media, Bina Nusantara University, Jakarta, Indonesia.
2Yaya Heryadi, Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia Email:
3Gede Putra Kusuma, Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
4Edi Abdurachman, Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 5659-5663 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8011118419/2019©BEIESP | DOI: 10.35940/ijrte.D8011.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In many aging countries, where the population distribution has shifted to old ages, the need for automatic monitoring devices to help an elderly person when they fall is very crucial. Smartphone is one of the best candidate devices for detecting fall because accelerometer and gyroscope sensors embedded in it respond based on human movements. People usually carry their smartphone in any position and can make fall detection method difficult to detect when fall occurs. This research explored the model for unconstraint human fall detection by using the sensors embedded in smartphone for carried/wearable- sensor-based method. We proposed robust model called Ans-Assist using modified cell of Long Short-Term Memory based model as fall recognition model which can detect human fall from any smartphone position (unconstraint). Some experimental results showed that Ans-Assist achieved 0.95 (± 0.028) average accuracy value using unconstraint smartphone positions. This model can adapt the input from accelerometer and gyroscope sensors which are responsive when human fall.
Keywords: Fall Detection, Unconstraint Positions, Accelerometer, Gyroscope, Smartphone.
Scope of the Article: E-Governance.