Revealing human movement requires lightweight, flexible systems capable of detecting mechanical parameters (like strain and pressure) while being worn comfortably by the user, and not interfering with his/her activity. In this work we address such multifaceted challenge with the development of smart garments for lower limb motion detection, like a textile kneepad and anklet in which soft sensors and readout electronics are embedded for retrieving movement of the specific joint. Stretchable capacitive sensors with a three-electrode configuration are built combining conductive textiles and elastomeric layers, and distributed around knee and ankle. Results show an excellent behavior in the ~30% strain range, hence the correlation between sensors’ responses and the optically tracked Euler angles is allowed for basic lower limb movements. Bending during knee flexion/extension is detected, and it is discriminated from any external contact by implementing in real time a low computational algorithm. The smart anklet is designed to address joint motion detection in and off the sagittal plane. Ankle dorsi/plantar flexion, adduction/abduction, and rotation are retrieved. Both knee and ankle smart garments show a high accuracy in movement detection, with a RMSE less than 4° in the worst case.

One of the critical aspects in the design of an assistive wearable robot is the energy efficiency of the actuation system, since it affects significantly the weight and consequently the comfort of the system. Several strategies have been used in previous research, mostly based on energy harvesting, compliant elements for mechanical energy accumulation (springs or elastic cords), ratchets and clutches. However, the design of the optimal actuator arrangement is highly dependent on the task, which increases significantly the complexity of the design process. In this work we present an energy efficiency analysis and design optimization of an actuation system applied to a soft module lower limb exoskeleton. Instead of performing a comparison between predefined mechanism arrangements, we solve a full optimization problem which includes not only the mechanism parameters, but also the mechanism architecture itself. The optimization is performed for a walking task using gait data from a stroke subject, and the result is a set of actuator arrangements with optimal parameters for the analyzed task and selected user. The optimized mechanism is able to reduce the energy requirements by 20-65%, depending of the joint. The proposed mechanism is currently under development within the XoSoft EU project, a modular soft lower-limb exoskeleton to assist people with mobility impairments.

Detection of human movement requires lightweight, flexible systems to detect mechanical parameters (like strain and pressure) not interfering with user activity, and that he/she can wear comfortably. In this work we address such multifaceted challenge with the development of smart garments for lower limb motion detection, like a textile kneepad and anklet in which soft sensors and readout electronics are embedded for detecting movement of the specific joint. Stretchable capacitive sensors with a three-electrode configuration are built combining conductive textiles and elastomeric layers, and distributed at knee and ankle. They show an excellent behavior in the ~30% strain range, hence the correlation between their responses and the optically tracked Euler angles is allowed for basic lower limb movements. Bending during knee flexion/extension is detected, and it is discriminated from any external contact by implementing in real time a low computational algorithm. The smart anklet is designed to address joint motion detection in and off the sagittal plane. In this work, ankle dorsi/plantar flexion, adduction/abduction, and rotation are retrieved. Both smart garments show a high accuracy in movement detection, with a RMSE less than 4° in the worst case.