A Wearable IoT-Based Fall Detection System Using Triaxial Accelerometer and Barometric Pressure Sensor

The aim of this research work is to develop a wearable and IoT-based fall detection system that can potentially be integrated within a smart home or a community health center to improve the quality of life of the elderly. This system would enable caregivers to remotely monitor the activities of their dependents and to immediately be notified of falls as adverse events. The proposed hardware architecture includes a processor, a triaxial accelerometer, a barometric pressure sensor, a Wi-Fi module, and battery packs. This unobtrusive architecture causes no interference with daily living while monitoring the falls. The output of the fall detection algorithm is a two-state flag, transmitted to a remote server in real-time.

[1]  Andrew Sixsmith,et al.  Pyroelectric IR sensor arrays for fall detection in the older population , 2005 .

[2]  Nabil Zerrouki,et al.  Vision-based fall detection system for improving safety of elderly people , 2017, IEEE Instrumentation & Measurement Magazine.

[3]  Guang-Zhong Yang,et al.  Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Meysam Asgari,et al.  Context-aware fall detection using inertial sensors and time-of-flight transceivers , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[5]  Emmanuel Andrès,et al.  From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems , 2017, IEEE Sensors Journal.

[6]  Peter H. N. de With,et al.  Video-Based Fall Detection in the Home Using Principal Component Analysis , 2008, ACIVS.

[7]  Ondrej Biros,et al.  Implementation of wearable sensors for fall detection into smart household , 2014, 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[8]  Pietro Siciliano,et al.  Detecting falls and vital signs via radar sensing , 2017, 2017 IEEE SENSORS.

[9]  Mohammed Riyaz Ahmed,et al.  Architecture for IOT based geriatric care fall detection and prevention , 2017, 2017 International Conference on Intelligent Computing and Control Systems (ICICCS).

[10]  Kang-Hyun Jo,et al.  Human fall classification system for ceiling-mounted kinect depth images , 2017, 2017 17th International Conference on Control, Automation and Systems (ICCAS).

[11]  Victor R. L. Shen,et al.  The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net , 2015, Appl. Soft Comput..

[12]  Hao Chen,et al.  A Fall Detection System Based on Human Body Silhouette , 2013, 2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[13]  Guang-Zhong Yang,et al.  Elderly Risk Assessment of Falls with BSN , 2010, 2010 International Conference on Body Sensor Networks.

[14]  R. Baumgartner,et al.  Fear of falling and restriction of mobility in elderly fallers. , 1997, Age and ageing.

[15]  Pietro Siciliano,et al.  A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications , 2017, Biosensors.

[16]  Ping-Min Lin,et al.  A fall detection system using k-nearest neighbor classifier , 2010, Expert Syst. Appl..

[17]  Pietro Siciliano,et al.  An EMG-based system for pre-impact fall detection , 2015, 2015 IEEE SENSORS.

[18]  Baharak Shakeri Aski,et al.  Intelligent video surveillance for monitoring fall detection of elderly in home environments , 2008, 2008 11th International Conference on Computer and Information Technology.

[19]  A McIntosh,et al.  The design of a practical and reliable fall detector for community and institutional telecare , 2000, Journal of telemedicine and telecare.

[20]  Israel Gannot,et al.  A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls , 2009, IEEE Transactions on Biomedical Engineering.

[21]  Boualem Boashash,et al.  Range-Doppler radar sensor fusion for fall detection , 2017, 2017 IEEE Radar Conference (RadarConf).

[22]  Daniel A. Sterling,et al.  Geriatric falls: injury severity is high and disproportionate to mechanism. , 1998 .

[23]  Xiaojun Ma,et al.  Towards detecting and predicting fall events in elderly care using bidirectional electromyographic sensor network , 2016, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).

[24]  Miao Yu,et al.  A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment , 2012, IEEE Transactions on Information Technology in Biomedicine.

[25]  Nigel H. Lovell,et al.  Low-power operation of a barometric pressure sensor for use in an automatic fall detector , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[26]  B. Isaacs,et al.  How dangerous are falls in old people at home? , 1981, British medical journal.

[27]  Liang Liu,et al.  An automatic in-home fall detection system using Doppler radar signatures , 2016, J. Ambient Intell. Smart Environ..

[28]  Xiang Chen,et al.  A Framework for Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Accelerometer Signals , 2013, IEEE Journal of Biomedical and Health Informatics.

[29]  Graciela E. De Luca,et al.  IoT fall detection system for the elderly using Intel Galileo development boards generation I , 2016, 2016 IEEE Congreso Argentino de Ciencias de la Informática y Desarrollos de Investigación (CACIDI).

[30]  Yun Li,et al.  A Microphone Array System for Automatic Fall Detection , 2012, IEEE Transactions on Biomedical Engineering.

[31]  Huan-Wen Tzeng,et al.  Design of fall detection system with floor pressure and infrared image , 2010, 2010 International Conference on System Science and Engineering.

[32]  Hannu Tenhunen,et al.  IoT-based fall detection system with energy efficient sensor nodes , 2016, 2016 IEEE Nordic Circuits and Systems Conference (NORCAS).

[33]  Ethel Mitty,et al.  Fall prevention in assisted living: assessment and strategies. , 2007, Geriatric nursing.

[34]  Rui Liu,et al.  Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera , 2014, Signal Image Video Process..

[35]  Mitja Luštrek.  FALL DETECTION AND ACTIVITY RECOGNITION METHODS FOR THE CONFIDENCE PROJECT: A SURVEY , 2010 .

[36]  Enzo Baccarelli,et al.  P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks , 2017, The Journal of Supercomputing.

[37]  K. Wild,et al.  Unobtrusive In-Home Monitoring of Cognitive and Physical Health: Reactions and Perceptions of Older Adults , 2008 .

[38]  M. Yuwono,et al.  Fall detection using a Gaussian distribution of clustered knowledge, augmented radial basis neural-network, and multilayer perceptron , 2011, 7th International Conference on Broadband Communications and Biomedical Applications.

[39]  E. Thammasat,et al.  A simply fall-detection algorithm using accelerometers on a smartphone , 2012, The 5th 2012 Biomedical Engineering International Conference.