Automatic Phone Slip Detection System

TL;DR

This paper presents an automatic system that uses smartphone sensors and neural networks to detect when a phone is in a vulnerable position prone to slipping or falling.

Contribution

It introduces a novel approach combining accelerometer and gyroscope data with neural network classification for slip detection.

Findings

High accuracy in classifying vulnerable phone positions

Effective feature extraction from sensor data

Potential for real-time fall prevention applications

Abstract

Mobile phones are becoming increasingly advanced and the latest ones are equipped with many diverse and powerful sensors. These sensors can be used to study different position and orientation of the phone which can help smartphone manufacture to track about their customers handling from the recorded log. The inbuilt sensors such as the accelerometer and gyroscope present in our phones are used to obtain data for acceleration and orientation of the phone in the three axes for different phone vulnerable position. From the data obtained appropriate features are extracted using various feature extraction techniques. The extracted features are then given to classifier such as neural network to classify them and decide whether the phone is in a vulnerable position to fall or it is in a safe position .In this paper we mainly concentrated on various case of handling the smartphone and classified by training the neural network.