Daily life gait performance measures can provide ecologically valid gait characteristics, which are interesting for monitoring individuals with gait impairments. The first step in obtaining these gait characteristics is selecting walking periods from multiple day recordings. We developed and validated an algorithm for walking detection using inertial measurement units (IMU) on Both feet and compared the performance with two others in healthy individuals and those with neurologically impaired gait: 1) using Sacrum accelerometer data (Iluz et al. 2014); 2) using Single foot gyroscopic data (Ullrich et al. 2020). We also assessed which activities reduce the algorithms' specificity for walking detection. The Both feet algorithm consisted of three stages, 1) Selecting time periods potentially containing walking; 2) Excluding periods not containing walking; 3) Checking the selected periods for minimal walking bout requirements. For validation, 32 participants (12 healthy and 20 with neurologically impaired gait) performed 20- 30 minutes of daily life activities, while wearing IMUs on both feet and the sacrum. Using labelled video recordings as reference, we calculated each algorithm's specificity, sensitivity and accuracy for walking detection. Both feet outperformed the other algorithms on specificity (96.6% versus 92.1% and 72.1% for the Single foot en Sacrum respectively). Stair climbing was misclassified as walking most often by all algorithms. Sacrum outperformed the others on sensitivity (99.5%), but had low specificity and accuracy. The high specificity of the Both feet algorithm makes it suitable when spatiotemporal gait characteristics are of interest, and is applicable in populations with mild neurological conditions affecting gait.
Mierlo et al. (Thu,) studied this question.