The development of personalized rehabilitation strategies for patients with hemiparesis is fundamental to achieve the most effective outcome from the treatments. Clinicians are fully aware of the fact that the patients’ responsiveness to an intervention is extremely subjective and that the need to quantitatively track their motor-gains is evident. Wearable sensing technology can meet this demand through cost-effective and flexible solutions, enabling accurate assessments of movement quality and motor impairment. The approach proposed in this thesis relies on machine learning-based algorithms to estimate clinical scores through the analysis of wearable accelerometers data collected during the performance of Activities of Daily Living (ADLs).