Lower-limb exoskeletons, regardless of their control strategies, have been shown to alter a user’s gait just by the exoskeleton’s own mass and inertia. Characterization of these differences in joint kinematics and kinetics under exoskeleton-like added mass is important for design of such devices and their control strategies. This study consists of three chapters. In Chapter I, 19 young, healthy participants walked overground at self-selected speeds with six added mass conditions and one zero-added-mass condition. The added mass conditions included +2/+4 lb on each shank or thigh or +8/+16 lb on the pelvis. Open-Sim-derived lower-limb sagittal-plane kinematics and kinetics were evaluated statistically with both peak analysis and statistical parametric mapping (SPM). Results showed that adding larger masses led to kinematic differences at the ankle and knee during early swing, and the hip throughout the gait cycle, as well as kinetic differences at the ankle during stance. In Chapter II, 29 young and middle-aged healthy participants walked on a treadmill at 100%, 115%, and 130% of the subject-specific comfortable walking speeds. Full factorial combinations of 1 to 3 tungsten bars each weighing 1.8 lb were placed around the pelvis and both thighs. Quasi-stiffness of the hip joint was evaluated statistically over two parts of the gait cycle. Results showed that the amount and distribution of added mass have statistically significant impacts on the hip joint quasi-stiffness and walking at a higher speed increases the quasi-stiffness. In Chapter III, using the same dataset presented in Chapter II, participant age groups in 10-year increments from 11 to 70, were classified by machine learning models, and predictive performance was compared among various feature sets. The feature sets contained time-series joint kinematics and kinetics at the hip, knee, and ankle joints. Results show that the proposed protocol is capable of achieving the same level of prediction accuracy as shown in previous studies. The highest classification accuracies were achieved by including the sagittal, frontal, and transverse plane joint kinematics, and further improvement can be achieved by including the joint kinetics as well. However, this further improvement has been shown to be limited for certain age groups. In-home or remote healthy monitoring may benefit from the classification protocol proposed and validated in this study, and future exoskeleton designs may implement the characterizations of gait biomechanics in response to added mass to inform exoskeleton hardware structure and cooperative control strategies.