A Smart Sensor to Detect the Falls of the Elderly
IEEE Pervasive Computing
Design and implementation of expressive footwear
IBM Systems Journal
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Location-Aware Fall Detection System for Medical Care Quality Improvement
MUE '09 Proceedings of the 2009 Third International Conference on Multimedia and Ubiquitous Engineering
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
A survey of mobile phone sensing
IEEE Communications Magazine
Mobile phone-based pervasive fall detection
Personal and Ubiquitous Computing
iPrevention: towards a novel real-time smartphone-based fall prevention system
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Scientific research on smartphone-based fall detection systems has recently been stimulated due to the growing elderly population and their risk of falls. Even though these systems are helpful for fall detection, the best way to reduce the number of falls and their consequences is to predict and prevent them from happening in the first place. To address the issue of fall prevention, in this paper, we propose a fall prediction system by integrating the sensor data of smartphones with a smartshoe. In our previous research, we designed and implemented a pair of sensing shoes (smartshoe) that contained four pressure sensors with a Wi-Fi communication module in each shoe to unobtrusively collect data in any environment. After assimilating the smartshoe and smartphone sensor data, we performed an extensive set of experiments in the lab environment to evaluate normal and abnormal walking patterns. In the smartphone, the system can generate an alert message to warn the user about the high-risk gait patterns and potentially save them from a forthcoming fall. We validated our approach using a decision tree with 10-fold cross validation and found 97.2% accuracy in gait abnormality detection.