Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors

Area of Science:

• Biomechanics
• Wearable Technology
• Algorithm Development

Background:

• Falls are a major cause of injury, particularly during daily activities.
• Early detection of balance loss is crucial for fall prevention systems.
• Inertial Measurement Units (IMUs) offer a promising approach for wearable motion analysis.

Purpose of the Study:

• To evaluate an updated pre-impact detection algorithm for identifying loss of balance during tripping events.
• To assess the algorithm's performance using data from IMUs placed on lower limbs.
• To determine the algorithm's potential for integration into wearable injury prevention systems.

Main Methods:

• Eight young subjects experienced induced tripping while walking on a treadmill.
• An adaptive threshold-based algorithm processed elevation angle data from lower limb IMUs (thighs, shanks, feet).
• The algorithm was tuned to detect abrupt kinematic changes indicative of tripping and balance loss.

Main Results:

• The algorithm successfully identified loss of balance approximately 0.37 ± 0.11 seconds after tripping onset.
• Detection accuracy was high, with a low false alarm rate (<10%).
• Effective detection was achieved using data solely from the perturbed shank.

Conclusions:

• The developed algorithm effectively detects tripping-induced balance loss using wearable IMU data.
• Its multi-purpose nature allows for the identification of various perturbations like slippage and tripping.
• The algorithm is suitable for implementation in smart garments and wearable robots for on-demand injury prevention.