Accelerometer Surface Detection via High-Pass Filtering
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Solution Overview
Problem
Existing mobile devices lack the capability to differentiate between drops on hard and soft surfaces, which limits their ability to provide tailored responses and accurate damage assessment post-incident.
Innovation Solution
The implementation of a sensor system that includes an accelerometer, a finite state machine circuit, and a machine learning core to collect and analyze acceleration data after a free-fall event. This system calculates the number of crossing events or applies a high-pass filter to determine whether the fall occurred on a hard or soft surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sensor system with machine learning core is implemented to differentiate drop surfaces, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary processing system between the accelerometer and the final detection output. The finite state machine circuit and machine learning core act as mediators that process raw acceleration data through multiple stages (state machine analysis, machine learning classification) to differentiate hard vs. soft surfaces. This intermediary layer enables precise surface type detection while managing complexity through structured processing pipelines.
2Measurement precision
If acceleration data analysis with multiple processing stages is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The finite state machine circuit performs preliminary analysis of acceleration data before the machine learning core processes it. By pre-processing the data to identify key characteristics and filter relevant information, the system reduces the computational burden on the energy-intensive machine learning algorithms. This preliminary action enables accurate surface differentiation while managing power consumption through staged processing.
3Adaptability or versatility
If retrospective surface detection capability is added, then adaptability is improved, but device complexity increases
Solution Approach 1:
The sensor system is designed with multi-functionality to handle various detection scenarios. The same accelerometer, finite state machine, and machine learning core infrastructure supports both real-time drop detection and retrospective surface analysis. This universal design enables the device to adapt to different use cases (immediate response, post-incident review, insurance documentation) without requiring separate dedicated systems for each function.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables mobile devices to accurately differentiate between drops on hard and soft surfaces, allowing for tailored responses, improved damage assessment, and enhanced user safety and experience.
Implementation Method 1
An accelerometer collects acceleration data for a plurality of samples spanning a period of time before and during the free-fall event
Implementation Method 2
applying a high-pass filter on the N samples of acceleration data
Data Source
AI summary
According to an embodiment, a method for determining whether a fall of a device is on a hard surface or a soft surface is proposed. The method includes collecting N samples of acceleration data after detecting a free-fall event; applying a high-pass filter on the N samples of acceleration data; calculating a variance from the N samples of acceleration data after applying the high-pass filter on the N samples of acceleration data; determining that the fall is on the hard surface in response to the variance from the N samples of acceleration data after applying the high-pass filter on the N samples of acceleration data being greater than a threshold; and determining that the fall is on the soft surface in response to the variance from the N samples of acceleration data after applying the high-pass filter on the N samples of acceleration data being less than the threshold.