Individualized detection of ambulatory distress in the field using wearable sensors

The early onset of musculoskeletal injury during ambulation may be detectable due to changes in gait. Body worn accelerometers provide the ability for real-time monitoring and detection of these changes, thereby providing a means for avoiding further injury. We propose algorithms for extracting magnitude and pattern asymmetry features from accelerometers attached to each foot. By registering simultaneous acceleration differences between the two feet, these features provide robustness to a variety of confounding factors, such as changes in walking speed and load carriage. By computing only summary statistics from the acceleration signals, the algorithms can be easily implemented in real-time physiological status monitoring systems. We evaluate the algorithms on a field collection consisting of 32 subjects completing a series of 5 km marches under different loading conditions. We show that changes in the magnitude and pattern asymmetry features are predictive of subject ratings of physical pain and discomfort.