Deep Learning-Based Temporal Gait Analysis Using a Smartphone IMU in Older Adults with and Without Non-Specific Low Back Pain

Accurate gait event detection using inertial measurement units (IMUs) is essential for temporal gait analysis, but frame-level detection is challenged by sparse initial contact (IC) and foot-off (FO) events. This study evaluated recurrent neural network architectures and training strategies for simultaneous IC and FO detection using a single shank-mounted smartphone IMU. The internal dataset included 28 healthy older adults and 18 individuals with non-specific low back pain (NSLBP). Temporal label expansion substantially improved validation performance for gated recurrent unit (GRU) and long short-term memory models, whereas point-label and class-weighted training performed poorly. The selected label-expanded GRU (LE-GRU) achieved F1 scores above 0.95 for both events and mean absolute temporal errors below 12 ms on held-out internal test folds, with high performance in both cohorts. On an external dataset with different sensor and acquisition characteristics, high performance required full-network fine-tuning, indicating the need for adaptation across datasets. Stance phase and stride time calculated from LE-GRU-predicted events showed high agreement with reference-derived values, with Lin’s concordance correlation coefficients from 0.980 to 0.994. These findings demonstrate that temporal label expansion enables accurate GRU-based gait event detection and temporal gait analysis from data collected with a single smartphone IMU.