Multi-Linear Regressor for Static Posturography Estimation Through an Instrumented Cane.

Area of Science:

• Biomechanics
• Sensor Technology
• Geriatric Medicine

Background:

• Clinical balance assessments are infrequent.
• Continuous patient monitoring offers a more comprehensive view of balance.
• Existing methods lack long-term, real-world data collection.

Purpose of the Study:

• To present a novel method for quantifying balance ability using an instrumented cane.
• To predict postural sway velocity using motion and force sensors.
• To assess the method's potential for continuous patient monitoring and tracking proprioceptive function.

Main Methods:

• Developed a regression algorithm to predict postural sway velocity.
• Utilized motion and force sensors integrated into an instrumented cane.
• Validated the algorithm with young and older adult subjects under various balance-challenging conditions.

Main Results:

• Postural sway velocity was highly predictable using cane-based sensors (Younger Subjects R²=0.73, Older Subjects R²=0.47).
• Hand motion was identified as a key predictor of sway velocity.
• The method successfully estimated Romberg quotients in older adults (r=0.82), indicating potential for tracking proprioceptive function.

Conclusions:

• The instrumented cane method provides a promising approach for continuous, quantitative balance assessment.
• This technology can offer valuable long-term data for clinical insights beyond traditional assessments.
• The method has the potential to monitor balance and proprioceptive changes over time.