JOURNAL ARTICLE

A wearable pre-impact fall early warning and protection system based on MEMS inertial sensor and GPRS communication

Abstract

Fall is one of great threats affecting people with old age. Aimed at the falling issue of aged, the paper explored a pre-impact fall early warning and protection system. This system consists of an early fall alarm, protection airbags, a remote monitoring platform and a guardian's cellphone app. The early fall alarm and airbags are integrated in a belt, convenient for wearing and hip-protection. The inner of early fall alarm has a MEMS sensor, which collects 3-axis accelerated velocity and 3-axis angular velocity. A fall detection algorithm is applied to recognize falls from activities of daily living (ADL). When there is a dangerous movement approaching fall, the early fall alarm will warn the aged to stop the movement. When the fall happens, the early fall alarm will trigger the airbag system, then the airbags in the belt will inflate as soon as possible to reduce the damage to the aged. In addition, the early fall alarm will ring and send message to the guardian's cellphone for help. Meanwhile, the kinematics of the human body during falling time will be stored in TF card and sent to remote monitoring platform for storage. Then the monitoring platform can show the fall location where the fall incident happens in the electronic map. In order to test the reliability of this early fall alarm and protection system, a series of experiments have been designed. The results show that this system can be relatively accurate to detect falls, accomplishing functions including early fall warning and alarming, airbag inflation, statics transferring and storage, real-time location, which has significant benefit for reducing the direct damage and shortening the aiding time.

Keywords:
ALARM Warning system Accidental fall Computer security Accelerometer Computer science Simulation Fall prevention Wearable computer Real-time computing Engineering Aeronautics Telecommunications Poison control Embedded system Injury prevention Electrical engineering

Metrics

12
Cited By
0.83
FWCI (Field Weighted Citation Impact)
11
Refs
0.79
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Context-Aware Activity Recognition Systems
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Non-Invasive Vital Sign Monitoring
Physical Sciences →  Engineering →  Biomedical Engineering
Gait Recognition and Analysis
Physical Sciences →  Engineering →  Biomedical Engineering

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