CBAS is proposing to develop a Prosthetic Interface Device (PID): Digital, an innovative, continuous, system that aids in collecting data remotely for patients with mobility impairments: patients with lower limb disorders and vulnerable elderly people. PID: Digital takes advantage of the CBAS machine-learning (ML) platform. This system is used to collect healthcare data from sensors worn by patients to enable remote assessment of their health. It provides clinicians with a true and complete picture of activity and mobility by representing patient conditions. This offers clinicians a clear tool to see that treatment is effective, progression of disease, and even clinical key performance indicators (treatment adherence/compliance measures). PID: Digital, can predict the need for in-house consultations with clinicians, potentially alleviating dependence on direct interaction between healthcare provider and patient, and supporting patient autonomy. The benefits include continuity of care, condition specific data, proactive intervention and reduced face-to-face assessment time via targeted patient engagement. This study will optimise existing ML algorithms for implementation in a cloud environment and build a system to scale these across multiple patients, clinicians and data types. These algorithms will provide clinically important information for identified patient groups, accessed via client end dashboards. All patients will be assessed in QMUL Gait Analysis Laboratory, providing gold standard validation. Clinical studies carried out by collaborators CUSH Health Ltd and Andiamo will trial PID: Digital alongside current best practise assessment methods. A regulatory and ethically compliant cloud environment and associated data storage will be designed and built with dashboards for identified for specified patients and associated user groups. The resulting system will be compliant to all medical device regulatory requirements to enable remote patient health assessment. On project completion, PID: Digital will have been trialled with two patient groups and be ready for regulatory submission as a class 1M medical device