Walking and sit-to-stand are the essential movements for human beings to sustain their lives and perform daily activities, but traumatic injuries and diseases can temporarily or permanently impair ambulatory ability, and rehabilitation is the sole existing solution to recover to some extent from such conditions. The work aims to design and implement a suitable driving system and to identify the best control strategy for the position control of the pneumatically actuated lower limb exoskeleton. The video analysis of human gait were carried out for healthy as well as some selected afflicted subjects. Suitable models were developed using neural network, fuzzy, neuro-fuzzy and the outputs of these models help in the generation of activating signals for the driving system of the exoskeleton. The error analysis validated that neuro-fuzzy could generalize well with different gait patterns. The customized pneumatic exoskeleton was experimented and tested with different control strategies. As part of the controller assessment of pneumatic exoskeleton, testing was done in eleven healthy volunteers and one individual affected by polio. While implementing PID controller, the observed error in the joints (knee and hip) were approximately in the range of 2.5° and 6° respectively. The response of the pneumatically powered exoskeleton was compared with healthy gait and it could replicate healthy gait with negligible errors.