Design and Implementation of a Wearable Accelerometer-Based Motion/Tilt Sensing Internet of Things Module and Its...

Area of Science:

• Biomedical Engineering
• Internet of Things (IoT)
• Wearable Technology

Background:

• Fall detection systems primarily rely on accelerometers but cannot prevent falls.
• Bed falls constitute over half of patient falls and often have a precursor: attempting to exit the bed.
• Existing fall detection methods lack proactive intervention capabilities.

Purpose of the Study:

• To design and implement an IoT module for proactive bed fall prevention.
• To detect postural changes indicating an attempt to leave the bed.
• To enable timely caregiver intervention for individuals at high risk of bed falls.

Main Methods:

• Development of a Bluetooth low-energy-enabled Accelerometer-based Sensing In a Chip-packaging (BASIC) module.
• Implementation of a tilt-sensing algorithm using a low-complexity COordinate Rotation DIgital Computer (CORDIC)-based approach for accurate tilt angle conversion.
• Integration of motion and tilt sensing for detecting transitions from lying to sitting positions.

Main Results:

• Successful design and implementation of the BASIC module for detecting specific postural changes.
• Demonstration of the module's capability in identifying the initial stages of bed exit attempts.
• Validation of the tilt-sensing algorithm for reliable postural change detection.

Conclusions:

• The BASIC module offers a novel approach to bed fall prevention through early detection of movement.
• The technology can be miniaturized for wearable applications and integrated into smart healthcare systems.
• This system has the potential to significantly reduce bed fall incidents in healthcare settings and homes.