Wearable-Based Stair Climb Power Estimation and Activity Classification.

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Musculoskeletal Health

Background:

• Stair climb power (SCP) is a critical clinical assessment of leg muscular function.
• Current in-clinic Stair Climb Power Tests (SCPT) are prone to human error and lack continuous monitoring capabilities.
• Wearable sensors offer a potential solution for continuous, remote assessment of lower-limb function.

Purpose of the Study:

• To propose and validate an algorithm for classifying stair climbing and estimating SCP using a lower-back worn accelerometer.
• To compare the performance of accelerometer-only, gyroscope-only, and combined sensor modalities for SCP estimation.
• To assess the feasibility of an at-home, accelerometer-based SCP assessment.

Main Methods:

• Collected data from 65 healthy adults performing SCPT and walking assessments using instrumented (accelerometer + gyroscope) lower-back sensors.
• Developed and applied two ensemble machine learning algorithms to classify stair ascent periods.
• Extracted features from accelerometer and gyroscope signals to estimate SCP.

Main Results:

• The proposed algorithm demonstrated strong agreement with the clinical standard for SCP estimation (r = 0.92, ICC = 0.90).
• Stair climbing periods were identified with >89% accuracy using accelerometer-based machine learning models.
• Minimal performance degradation was observed using gyroscope alone compared to the accelerometer, and combined sensor use offered only a slight accuracy improvement.

Conclusions:

• An accelerometer-based algorithm can accurately estimate stair climb power (SCP) and classify stair climbing, correlating strongly with clinical measures.
• Wearable sensor technology, particularly accelerometer-based systems, holds significant potential for at-home, continuous monitoring of lower-limb muscular function.
• The findings support the development of accessible, remote assessments for leg strength and function, optimizing battery life for practical at-home use.