Soft Smart Garments for Lower Limb Joint Position Analysis

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

Online estimation of laser incision depth for transoral microsurgery: approach and preliminary evaluation

The use of lasers in transoral surgery enables precise tissue incision with minimal adverse effects on surrounding structures. Nonetheless, the lack of haptic feedback during laser cutting impairs the surgeon's perception of the incision depth, potentially leading to undesired tissue damage. This paper presents a novel approach, based on statistical regression analysis, to estimate the laser incision depth in soft tissue. User trials were conducted in a laser surgery set-up, to verify the effectiveness of online estimation of incision depth in supporting precise tissue cutting. The estimation accuracy was verified on ex vivo muscle tissue, revealing a root mean squared error (RMSE) of 0.1 mm for depths ranging up to 1.4 mm. Online estimation of depth has the potential to significantly improve the incision control of users. The proposed approach was successful in producing estimations of laser cutting depth in ex vivo muscle tissue. Further investigation is required to validate this approach on other types of tissue. Providing depth estimation during laser cutting allows users to perform more precise incisions