Original article: Validity and reliability of gait metrics derived from researcher-placed and self-placed wearable inertial sensors.

To compare the inter-session placement reliability for researcher-placed and self-placed sensors, and to evaluate the validity and reliability of waveforms and discrete variables from researcher-placed and self-placed sensors following a previously described alignment correction algorithm. Fourteen healthy, pain-free participants underwent gait analysis over two data collection sessions. Participants self-placed an inertial sensor on their left tibia and a researcher placed one on their right tibia, before completing 10 overground walking trials. Following an axis correction from a principal component analysis-based algorithm, validity and reliability were assessed within and between days for each sensor placement type through Euclidean distances, waveforms, and discrete outcomes. The placement location of researcher-placed sensors exhibited good inter-session reliability (ICC = 0.85) in comparison to self-placed sensors (ICC = 0.55). Similarly, waveforms from researcher-placed sensors exhibited excellent validity across all variables (CMC ≥ 0.90), while self-placed sensors saw high validity for most axes with reductions in validity for mediolateral acceleration and frontal plane angular velocity. Discrete outcomes saw good to excellent reliability across both sensor placement types. A simple alignment correction algorithm for inertial sensor gait data demonstrated good to excellent validity and reliability in self-placed sensors with no additional data or measures. This method can be used to align sensors easily and effectively despite sensor placement errors during straight, level walking to improve 3D gait data outcomes in data collected with self-placed sensors.