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Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

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Published on: April 5, 2019

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.

Roschelle L Fritz¹, Marian Wilson¹, Gordana Dermody²

¹College of Nursing, Washington State University, Vancouver, WA, United States.

Journal of Medical Internet Research

|October 26, 2020

Summary

This summary is machine-generated.

Smart homes can detect pain-related behaviors for automated assessment in older adults. Clinician-guided machine learning models significantly improve pain behavior recognition accuracy in smart home environments.

Keywords:

multiple methods pain remote monitoring sensors smart homes

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Area of Science:

Gerontology and Health Informatics
Artificial Intelligence in Healthcare
Pain Management Technologies

Background:

Poorly managed pain has severe consequences, including mental health issues and increased healthcare utilization.
Current pain assessments are limited to clinical settings, missing real-world patient behaviors.
Smart home technology offers potential for in-home pain observation and functional interference quantification.

Purpose of the Study:

To evaluate if smart home systems can detect pain-related behaviors for automated pain assessment.
To explore the potential for smart homes to support interventions for individuals with chronic pain.
To assess the feasibility of using machine learning for pain behavior recognition in a home environment.

Main Methods:

Secondary analysis of ambient sensor and nursing assessment data from 11 older adults over 1-2 years.
Qualitative interpretation of sensor data for 27 pain events to guide machine learning model training.
Development of a clinician-guided random forest machine learning model to recognize pain-related behaviors from 550 extracted markers.

Main Results:

Identification of 13 clinically relevant behaviors and 6 pain-related qualitative themes.
Clinician-guided model achieved 0.70 classification accuracy, 0.72 sensitivity, and 0.69 specificity.
The model significantly outperformed standard anomaly detection (0.16 accuracy, P<.001) and showed moderate correlation in regression (r=0.42).

Conclusions:

Smart homes show promise in recognizing pain-related behaviors for automated pain assessment.
Incorporating clinical expertise in machine learning model development enhances performance for pain assessment.
Further research with larger studies is recommended to refine and validate pain-behavior-focused models.