Stroke, or cerebral-vascular accident is the leading cause of disabilities in the western world. It primarily affects mobility due to a degradation of principal motor functions such as balance and gait. This reduction of mobility directly impacts the spectrum of possible activities of daily-living and consequently the level of independence. Patients undergo intensive physical therapy for improving various aspects of mobility. To evaluate the rehabilitation strategy outcome and guide patients through the therapy, clinical instruments have been widely used including questionnaires and motor function tests. Questionnaires related to mobility and quality of life depend on the interpretation, the cognitive abilities and the state-of-mind of the patient during the interview and are therefore subject to the interpretation of the patient. Motor function tests, such as Berg Balance Scale and Timed-Up-and-Go for evaluating balance and gait disorders, evaluates the patient’s motor capacity of performing given motor tasks in clinical settings, under close supervision. They are consequently not necessarily representative of the patient’s motor performance in daily life. Providing a measure of mobility performance would allow clinicians to better understand the impact, in patient’s home environment, of various rehabilitation strategies. Furthermore, emerging rehabilitation techniques such as virtual reality games may evaluate the patients during the training to build a progression scheme. However, the in-game assessment is limited to their scope of rehabilitation, i.e. it is restricted to one single component of mobility. To overcome the limitations of current assessment tools, an objective evaluation tool of stroke patient’s recovery in their daily-life environment is required. Recent miniaturization of wearable technologies have enabled new possibilities for long-term monitoring of physical activity in daily life. The detailed measurement of physical activity includes: the characterization of activity events such as postural (sit-to-stand and stand-to-sit) transitions; the quantification of the intensity and the amount of activities; and a pattern analysis of these events/activities through their distributions and temporal structures. The aim of this thesis is to provide an objective assessment tool of stroke patients’ mobility performance in daily-life. According to the work performed, the manuscript is organized in four main parts: (i) selection of an appropriate barometric pressure sensor for improving postural transitions recognition, with validation on a group of healthy volunteers; (ii) improvement of a postural transition recognition in stroke patients using the selected barometric pressure sensor; (iii) design of an activity classification algorithm suitable for stroke patients by fusing barometric pressure and inertial sensor information; (iv) cross-sectional comparison (elderly controls vs stroke patients) of the mobility metrics (transition, activity, and pattern metrics) and their relationships with motor function tests. The wearable system devised in this thesis provides clinicians with a convenient (one sensor on the trunk) and more complete monitoring solution able to characterize the various aspects of stroke’s mobility in daily-life. This solution can be used to objectively and accurately assess the rehabilitation effectiveness and eventually to adapt/fit the rehabilitation program to the patient's needs.