  The failure mode of the phone falling has a lot to do with the ground
material. At present, the research on ground material and mobile phone damage
is generally carried out through experiments, which is extremely costly. This
paper presents a method to identify the material of mobile phones falling on
the ground. The method determines the material of the mobile phone falling to
the ground according to the data of the mobile phone accelerometer and can
obtain the ground material of the mobile phone falling through a large number
of user data. By analyzing the physical process of mobile phone falling, the
accelerometer data interval which can reflect the characteristics of falling is
reasonably intercepted. And the data features that can reflect the collision
are extracted. Finally, based on the fully connected neural network, the method
of determining the material of mobile phones falling on the ground is
developed. The experimental results show that the method has a high
identification rate, and the average identification rate for different ground
materials is 96.75%. Instead of relying on auxiliary devices, the
identification process only relies on the phone's built-in acceleration sensor.
This paper fills the gap in the field of mobile phone falling ground material
identification. It has a great contribution to the collection of user drop
data, the analysis of the drop process, and the improvement of mobile phone
design defects.
