Modern lower limb prostheses have the capability to replace missing body parts and improve the patients’ quality of life. However, missing environmental information often makes a seamless adaptation to transitions between different forms of locomotion challenging. The aim of this review is to identify the progress made in this area over the last decade, addressing two main questions: which types of novel sensors for environmental awareness are used in lower limb prostheses, and how do they enhance device control towards more comfort and safety. A literature search was conducted on two Internet databases, PubMed and IEEE Xplore. Based on the criteria for inclusion and exclusion, 32 papers were selected for the review analysis, 18 of those are related to explicit environmental sensing and 14 to implicit environmental sensing. Characteristics were discussed with a focus on update rate and resolution as well as on computing power and energy consumption. Our analysis identified numerous state-of-the-art sensors, some of which are able to “look through” clothing or cosmetic covers. Five control categories were identified, how “next generation prostheses” could be extended. There is a clear tendency towards more upcoming object or terrain prediction concepts using all types of distance and depth-based sensors. Other advanced strategies, such as bilateral gait segmentation from unilateral sensors, could also play an important role in movement-dependent control applications. The studies demonstrated promising accuracy in well-controlled laboratory settings, but it is unclear how the systems will perform in real-world environments, both indoors and outdoors. At the moment the main limitation proves to be the necessity of having an unobstructed field of view.

Modern lower limb prostheses neither measure nor incorporate healthy residual leg information for intent recognition or device control. In order to increase robustness and reduce misclassification of devices like these, we propose a vision-based solution for real-time 3D human contralateral limb tracking (CoLiTrack). An inertial measurement unit and a depth camera are placed on the side of the prosthesis. The system is capable of estimating the shank axis of the healthy leg. Initially, the 3D input is transformed into a stabilized coordinate system. By splitting the subsequent shank estimation problem into two less computationally intensive steps, the computation time is significantly reduced: First, an iterative closest point algorithm is applied to fit circular models against 2D projections. Second, the random sample consensus method is used to determine the final shank axis. In our study, three experiments were conducted to validate the static, the dynamic and the real-world performance of our CoLiTrack approach. The shank angle can be tracked at 20 Hz for one sixth of the entire human gait cycle with an angle estimation error below $$2.8\pm 2.1^{\circ }$$ 2.8 ± 2 . 1 ∘ . Our promising results demonstrate the robustness of the novel CoLiTrack approach to make “next-generation prostheses” more user-friendly, functional and safe.