Evaluation of a Time Reversal Method with Dynamic Time Warping Matching Function for Human Fall Detection Using Structural Vibrations

Abstract

Falls are one of the predominant concerns of the elderly living at home. Commercial systems, such as wearable pendants that are pressed in an emergency, provide a viable solution when a fall occurs. However, wearable systems have a low compliance, especially in patients with diseases such as Alzheimer’s or other forms of dementia. Monitoring changes in the environment provides the possibility of reducing the compliance challenges for those patients. Computer vision techniques is an example of environmental monitoring. However, some patients might be concerned about their privacy when having cameras in their homes. Monitoring the vibrations of the patient’s dwelling is another alternative. Classification of the acceleration recorded signals becomes important to determine if a fall has occurred. This paper proposes the use of the Time Reversal Method (TRM) with Dynamic Time Warping (DTW) for classifying structural accelerations produced by different human actions. The potential classification is studied by releasing objects at different heights. A statistical study is performed to determine the importance of different factors to the application of the proposed technique. These factors are distance to the sensor, type of object used to impact the floor and intensity of the impact. Results indicate that the technique is most sensitive to the type of object, indicating the potential for human fall detection. Results also show interaction between the height in which the object was released and the type of object. Distance between the location of impact and the sensor is not an important factor but has an effect on the standard deviation of results.