Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic...

Area of Science:

• Biomedical Engineering
• Digital Health
• Orthopedic Surgery Outcomes

Background:

• Increasing numbers of hip and knee replacements necessitate evolving outcome assessment methods.
• Current Patient Reported Outcome Measures (PROMs) have limitations in accuracy and interpretation.
• Demographic shifts and resource constraints impact post-surgical aftercare and recovery monitoring.

Purpose of the Study:

• To explore the potential of home-monitoring systems for assessing recovery after hip and knee replacements.
• To characterize the relationship between objective behavioral data and subjective PROMs.
• To develop passive monitoring methods for surgical outcomes and patient recovery.

Main Methods:

• Utilized a home-monitoring system for long-term, continuous observation of movement and sleep.
• Collected data from two hip and knee replacement patients over a 3-month post-surgery period.
• Compared sensor data (accelerometer, indoor localization) with PROMs and a control home's data.

Main Results:

• Accelerometer and indoor localization data accurately reflected long-term trends in sleep and movement quality.
• The system could predict sleep/wake times and measure routine variance.
• Indoor localization provided contextual information on patient mobility and domestic routines.

Conclusions:

• Passive home monitoring offers a viable method to objectively assess recovery post-orthopedic surgery.
• Sensor-derived data can complement or enhance traditional PROMs.
• Visualizing findings can facilitate communication between patients and healthcare professionals.