Fall Detection Using Accelerometer, Gyroscope & Impact Force Calculation on Android Smartphones

被引:6
|
作者
Rungnapakan, Traitot [1 ]
Chintakovid, Thippaya [1 ]
Wuttidittachotti, Pongpisit [1 ]
机构
[1] King Mongkuts Univ Technol North Bangkok, Fac Informat Technol, 1518 Pracharat 1 Rd, Bangkok 10800, Thailand
关键词
Fall Detection; Aging Population; Accelerometer; Gyroscope; Impact Force;
D O I
10.1145/3205946.3205953
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Thailand has become an aging society. An accidental fall is a common problem found for the elderly. This research developed an Android mobile application for fall detection. The application compared threshold values of accelerometer and gyroscope together with impact force with the present values obtained from sensors to detect a fall. In this study, threshold values of impact force were computed based on an elder's weight to improve an accuracy of fall detection. Four types of elders' behaviors were studied. The first type was non-movement behavior (sitting and standing). The second group was constantly moving behavior (walking and running). Next group involved a change of movement (standing then sitting and sitting then standing). The last type were behaviors with falling (walking then falling on the buttocks, walking then falling on the back, running then falling on the back, and hopping then falling on the back). Results showed that the application could correctly identify the fall 97.33 percent.
引用
收藏
页码:49 / 53
页数:5
相关论文
共 50 条
  • [21] Malware Detection on Android Smartphones using API Class and Machine Learning
    Westyarian
    Rosmansyah, Yusep
    Dabarsyah, Budiman
    5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS 2015, 2015, : 294 - 297
  • [22] Fall Detection on Embedded Platform Using Kinect and Wireless Accelerometer
    Kepski, Michal
    Kwolek, Bogdan
    COMPUTERS HELPING PEOPLE WITH SPECIAL NEEDS, PT II, 2012, 7383 : 407 - 414
  • [23] Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm
    Zhang, Tong
    Wang, Jue
    Liu, Ping
    Hou, Jing
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2006, 6 (10): : 277 - 284
  • [24] Human Fall Detection using Built-in Smartphone Accelerometer
    Abdullah, Chowdhury Sayef
    Kawser, Masud
    Opu, Md Tawhid Islam
    Faruk, Tasnuva
    Islam, Md Kafiul
    PROCEEDINGS OF 2020 6TH IEEE INTERNATIONAL WOMEN IN ENGINEERING (WIE) CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (WIECON-ECE 2020), 2020, : 376 - 379
  • [25] Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks
    Wang, Gaojing
    Li, Qingquan
    Wang, Lei
    Zhang, Yuanshi
    Liu, Zheng
    ELECTRONICS, 2019, 8 (11)
  • [26] Energy Saving in Forward Fall Detection using Mobile Accelerometer
    Viet, Vo Quang
    Negera, Ali Fahmi Perwira
    Thang, Hoang Minh
    Choi, Deokjai
    INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES, 2013, 4 (01) : 78 - 94
  • [27] Improving Fall Detection Using an On-Wrist Wearable Accelerometer
    Khojasteh, Samad Barri
    Villar, Jose R.
    Chira, Camelia
    Gonzalez, Victor M.
    de la Cal, Enrique
    SENSORS, 2018, 18 (05)
  • [28] The MobiFall Dataset: An Initial Evaluation of Fall Detection Algorithms Using Smartphones
    Vavoulas, George
    Pediaditis, Matthew
    Spanakis, Emmanouil G.
    Tsiknakis, Manolis
    2013 IEEE 13TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE), 2013,
  • [29] An accelerometer based fall detection system using Deep Neural Network
    Garg, Sankalp
    Panigrahi, Bijaya Ketan
    Joshi, Deepak
    2019 IEEE 5TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2019,
  • [30] IOT Enabled Human Fall Detection Using Accelerometer and RFID Technology
    Kaudki, Bharati
    Surve, Anil
    PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), 2018, : 1094 - 1099