Human gait is a key health marker, offering critical insights into real-world mobility. Among research-grade dedicated hardware, wearable inertial measurement units (IMUs) are the most widely used for free-living assessments. In the European research initiative Mobilise-D, which aims to develop validated digital mobility outcomes (DMOs) for health monitoring, IMUs serve as the primary sensing technology, enabling the computation of high-accuracy DMOs through a dedicated computational pipeline. Smartphones with built-in inertial sensors offer a cheaper and ubiquitous alternative to IMUs but differences in positioning and metrological characteristics require re-validation of IMU-based algorithms for phone-derived data. The first objective of this thesis is to assess whether the python implementation of the pipeline, MobGap, originally optimized for lower back-mounted IMU, can achieve comparable results when applied to phone-derived inertial data from the same placement.