Aerosol jet printing (AJP) technology has emerged as a transformative tool in neuroprosthetic device development, offering high accuracy and versatility in fabricating complex and miniaturized structures, which are essential for advanced neural interfaces. This review explores the fundamental principles of AJP, highlighting its unique aerosol generation and concentrated deposition mechanisms, which facilitate the use of different materials on a variety of substrates. The advantages of AJP, including its device scalability, ability to print on flexible and stretchable substrates, and compatibility with a wide range of biocompatible materials, are examined in the context of neuroprosthetic applications. Key implementations, such as the fabrication of neural interfaces, the development of microelectrode arrays, and the integration with flexible electronics, are discussed, showcasing the potential of AJP to revolutionize neuroprosthetic devices. Additionally, this review addresses the challenges of biocompatibility and technical limitations, such as the long-term stability of electroconductive traces. The review concludes with a discussion of future directions and innovations, emphasizing the realization of sensorized prosthetic limbs through the incorporation of tactile sensors, the integration of biosensors for monitoring physiological parameters, and the development of intelligent prostheses. These prospects underscore the role of AJP in the advancement of neuroprosthetic applications and its pathway toward clinical translation and commercialization.

Accurate modeling of residual limb geometry is essential for prosthetic socket design, yet current scanning techniques can be costly, operator-dependent, or impractical for repeated clinical use. This study presents a fully automated, low-cost photogrammetry workflow capable of generating metrically accurate 3D models of lower-limb residual limbs using video and still images acquired with a standard smartphone or a full-frame digital camera. The pipeline integrates adaptive frame selection, deep learning-based background removal, robust metric scaling via ArUco markers, and open-source Structure-from-Motion and Multi-View Stereo reconstruction, requiring no manual post-processing or proprietary software. Accuracy and repeatability were evaluated using four 3D-printed limb phantoms and high-resolution CT-derived meshes as ground truth. Smartphone video and full-frame camera acquisitions achieved sub-millimeter surface accuracy, volume and perimeter errors within ±1%, and high inter-session repeatability, all within clinically accepted thresholds for prosthetic socket fabrication. In contrast, smartphone still-photo reconstructions showed larger deviations and reduced stability. Acquisition time was under five minutes, and complete reconstruction required approximately 1 h and 30 min. These results demonstrate that smartphone video-based photogrammetry provides a practical, scalable, and clinically viable alternative for residual limb modeling, particularly in resource-constrained or remote care settings.