Real-World Gait Analysis for Older Adults: Identifying Optimal Gait Bouts and Reconstructing Lower-Back Signals from Wrist-Worn Devices

Early detection and monitoring of cognitive impairment remain major challenges in the management of Alzheimer’s disease and related conditions. While traditional biomarkers provide valuable insights into underlying pathology, they are often costly, invasive, and limited to discrete clinical visits. Gait has emerged as a promising digital biomarker of cognitive function, reflecting the interaction between motor control and higher-order cognitive processes. Wearable sensors enable continuous, real-world gait monitoring; however, methodological challenges related to data heterogeneity, noise, and sensor placement have limited their clinical utility. This thesis investigates real-world gait assessment in clinical populations using wearable inertial sensors, with a focus on improving interpretability and scalability. First, an unsupervised framework is proposed to identify representative, lab-quality walking bouts from continuous real-world accelerometer data. This approach isolates steady-state gait cycles without reliance on predefined templates or labelled data, enabling robust gait characterization under naturalistic conditions. Second, frequency-domain methods are used to reconstruct lower-back kinematic gait information from wrist-worn sensor data, addressing the trade-off between biomechanical fidelity and user compliance. The proposed methods were evaluated using synchronized wrist and lower-back inertial measurement units collected during both controlled laboratory assessments and long-term real-world monitoring in older adults across clinical groups. Results demonstrate that representative gait patterns can be reliably extracted from real-world data and that wrist-derived signals capture key frequency-domain characteristics of trunk motion. Together, these findings support the feasibility of low-burden, wearable-based gait monitoring as a scalable approach for assessing mobility and cognitive health. This work contributes methodological advances toward the development of real-world gait-based digital biomarkers for cognitive impairment.