Gait Phase Detection of Stair Ambulation using Inertial Measurement of Lower Limb

Accurate and timely detection is a crucial component in the operation of wearable assistive devices. Stair climbing is an activity with fewer investigation than level walking, yet a crucial and physically more demanding gait in daily life. This thesis develops an adaptive approach in determining the gait phases of stair climbing in real-time. It is tested on healthy individuals, and its performance is compared to machine learning approaches commonly used in literature.