Quantification of mobility is the key to monitor the progression of mobility disorders as well as the effect of an intervention. Inertial measurement units (IMUs) with dedicated algorithms can quantify postural transitions and gait as the two key aspects of mobility in an objective and continuous manner. IMU-based mobility assessments can be performed by either functional tests in the clinic or through daily activities. Assessments performed in the clinic are more indicative of peoples best performance or capacity, while assessments performed at home represent mostly their actual performance. Yet the relationship between these two settings is not fully understood, both due to the existing gaps in technical algorithms as well as challenges in comparing two inherently different domains. To this end, in this thesis, I firstly focused on developing and validating algorithms to quantify mobility in both clinical and domestic environments. The added clinical value of these IMU-based mobility assessments was shown in several populations with mobility impairments. Finally, by proposing novel approaches, I focused to bridge the gap between clinical and daily activity assessments. The previous approaches to quantify mobility are mostly based on algorithms that are validated only during clinical or lab-based assessments. Opposed to daily activities, lab assessments contain simple and single-task activities. Therefore, it is important to design algorithms robust to the complex context of daily life setting while being unobtrusive to daily activities. A new algorithm was introduced to detect and characterize postural transitions, i.e., sitting and standing. Next, machine-learning-based algorithms were developed to detect walking bouts and estimate gait speed. The proposed postural transition and gait quantification algorithms were based on a single IMU on the lower back which is unobtrusive to daily activities. The novelty of the algorithms is their robustness to sensor placement changes during daily activities. The proposed algorithms demonstrated high performance during both clinical and daily activity assessments whether on healthy individuals or participants with mobility impairments. Next, through several analyses, I demonstrated how such instrumented mobility assessments can discriminate different patient populations. For instance, IMU-derived mobility parameters could differentiate older adults with and without risk of falls as well as patients with moderate or severe stages of multiple sclerosis. Moreover, the aforementioned parameters were compared between clinical and daily activity assessments. By this comparison, clinicians can have a better understanding of patients capacity through a remote assessment of mobility. Finally, it was shown how clinical and daily activity assessments can provide complementary information to each other. For instance, by introducing novel approaches to compare gait speed between clinical and daily activity assessments, the effect of the medication in Parkinsons disease (PD) was traceable during daily activities. The findings can lead to better optimization of the medication dose in PD. Overall, this thesis provided a framework that can help clinicians with an objective assessment of mobility. Furthermore, the approaches introduced in this thesis can help for better management of intervention and tracking its effects where both clinical and daily activity assessments exist.