Microphone-Based Ambient Fall Detection Using Audio Energy Analysis

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Solution Overview

Problem

Existing ambient fall detection systems using cameras, WiFi, UWB, and mmWave are costly, power-intensive, and have limited accuracy due to the complexity of distinguishing falls from other motions.

Innovation Solution

The use of microphones in IoT devices for ambient fall detection, which involves determining the energy level of audio signals to identify potential falls and triggering a fall detection operation, while employing a binary classification XGBoost model and continuous learning to improve performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If cameras, WiFi, UWB, and mmWave are used for ambient fall detection, then detection capability is provided, but cost and power consumption increase significantly

Engineering Contradiction:

Improvefall detection capabilityVSAvoidpower consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent replaces complex electronic detection systems (cameras, WiFi, UWB, mmWave) with a simple acoustic detection system using a microphone. The fall detection is achieved by analyzing audio signals and their energy levels, substituting mechanical/electronic sensing with acoustic sensing that consumes significantly less power while maintaining detection capability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If cameras, WiFi, UWB, and mmWave are used for ambient fall detection, then detection capability is provided, but system cost increases

Engineering Contradiction:

Improvefall detection capabilityVSAvoidsystem cost

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

The patent employs a microphone, which is a low-cost component compared to cameras, WiFi modules, UWB modules, and mmWave technology. The microphone can be easily integrated into existing IoT devices without requiring expensive specialized hardware, thereby significantly reducing the overall system cost while maintaining fall detection functionality.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

3Measurement precision

If audio signal energy level is monitored continuously, then fall detection accuracy is improved, but power consumption increases

Engineering Contradiction:

Improvefall detection accuracyVSAvoidpower consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

Instead of continuous monitoring, the system uses periodic action by setting energy thresholds and only triggering fall detection operations when audio signal energy exceeds these thresholds. This allows the system to maintain high detection accuracy for actual falls while consuming minimal power during normal operation, as the processor remains in a low-power state until triggered.

Inventive Principle:

Principle #19Periodic action

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

This approach provides a low-cost, energy-efficient, and high-accuracy fall detection system that is easy to deploy, with the ability to adapt to real-world scenarios over time.

Implementation Method 1

determine an energy level of an audio signal captured by the microphone

Methodology Applied

Scientific EffectAcoustic energy detection: Sound

Data Source

PatentUS20250118188A1Ambient fall detection with microphones

Publication Date: 2025.04.10 SAMSUNG ELECTRONICS CO LTD

AI summary

An apparatus includes a microphone, a memory, and a processor operably coupled to the memory. The processor is configured to determine an energy level of an audio signal captured by the microphone and determine whether the audio signal indicates a possible fall. The processor is further configured to, upon a determination that the audio signal indicates the possible fall, poll a fall detection module.