Sensing-Fi: Wi-Fi CSI and accelerometer fusion system for fall detection

Falling is a major cause of death among elders. To detect falls, numerous approaches have been proposed in the past decade, including computer-vision based image processing, wearable sensors, and acoustic signal processing, etc. Though the recent advancement in infra-red LED, depth camera, MEMS (Mic...

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Bibliographic Details
Published in2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) pp. 402 - 405
Main Authors Ramezani, Ramin, Xiao, Yubin, Naeim, Arash
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.03.2018
Subjects
Ac-celerometer
Accelerometers
Activity Recognition
Channel State Information (CSI)
Fall detection
Feature extraction
Floor Vibration
Predictive models
Receivers
Sensor Network
Sensors
Vibrations
WiFi
Wireless fidelity
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Summary:Falling is a major cause of death among elders. To detect falls, numerous approaches have been proposed in the past decade, including computer-vision based image processing, wearable sensors, and acoustic signal processing, etc. Though the recent advancement in infra-red LED, depth camera, MEMS (Micro-Electro-Mechanical Systems) sensors and machine learning algorithms may have enlarged the application scope, the privacy intrusion and lack of convenience still remain to be open issues which prevent these systems from large deployment. A novel Sensing-Fi system described in this paper aims to overcome such deficiencies. The system design is comprised of harnessing Wi-Fi Channel State Information (CSI) coupled with ground-mounted accelerometer to detect floor vibration, hence the system is completely passive and non­invasive, i.e., the user is not required to wear the technology contrary to wearable accelerometers. In addition, contrary to the existing standalone Wi-Fi CSI fall detection systems, it overcomes the constraint by which only one user can be present in the room. The system has shown promising results with 95% accuracy.
DOI:10.1109/BHI.2018.8333453