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

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.