Objectives : This study presents SmartProbe, an electrical bioimpedance (EBI) sensing system based on a concentric needle electrode (CNE). The system allows the use of commercial CNEs for accurate EBI measurement, and was specially developed for in-vivo real-time cancer detection. Approach : Considering the uncertainties in EBI measurements due to the CNE manufacturing tolerances, we propose a calibration method based on statistical learning. This is done by extracting the correlation between the measured impedance value | Z |, and the material conductivity σ, for a group of reference materials. By utilizing this correlation, the relationship of σ and | Z | can be described as a function and reconstructed using a single measurement on a reference material of known conductivity. Main results : This method simplifies the calibration process, and is verified experimentally. Its effectiveness is demonstrate by results that show less than 6% relative error. An additional experiment is conducted for evaluating the system’s capability to detect cancerous tissue. Four types of ex-vivo human tissue from the head and neck region, including mucosa, muscle, cartilage and salivary gland, are characterized using SmartProbe. The measurements include both cancer and surrounding healthy tissue excised from 10 different patients operated on for head and neck cancer. The measured data is then processed using dimension reduction and analyzed for tissue classification. The final results show significant differences between pathologic and healthy tissues in muscle, mucosa and cartilage specimens. Significance : These results are highly promising and indicate a great potential for SmartProbe to be used in various cancer detection tasks.

In this paper, we attempt to develop a reusable framework of cooperative perception for vehicle control on the road that can extend perception range beyond line-of-sight and beyond field-of-view. For this goal, the following problems are addressed: map merging, vehicle identification, sensor multi-modality, impact of communications, and impact on path planning. We provide experimental results using a self-driving vehicle and manned vehicles equipped with the cooperative perception systems that we propose and implement.

The integration of intra-operative sensors into surgical robots is a hot research topic since this can significantly facilitate complex surgical procedures by enhancing surgical awareness with real-time tissue information. However, currently available intra-operative sensing technologies are mainly based on image processing and force feedback, which normally require heavy computation or complicated hardware modifications of existing surgical tools. This paper presents the design and integration of electrical bio-impedance sensing into a commercial surgical robot tool, leading to the creation of a novel smart instrument that allows the identification of tissues by simply touching them. In addition, an advanced user interface is designed to provide guidance during the use of the system and to allow augmented-reality visualization of the tissue identification results. The proposed system imposes minor hardware modifications to an existing surgical tool, but adds the capability to provide a wealth of data about the tissue being manipulated. This has great potential to allow the surgeon (or an autonomous robotic system) to better understand the surgical environment. To evaluate the system, a series of ex-vivo experiments were conducted. The experimental results demonstrate that the proposed sensing system can successfully identify different tissue types with 100% classification accuracy. In addition, the user interface was shown to effectively and intuitively guide the user to measure the electrical impedance of the target tissue, presenting the identification results as augmented-reality markers for simple and immediate recognition.

Intravenous catheterization is frequently required for numerous medical treatments. However, this process is characterized by a high failure rate, especially when performed on difficult patients such as newborns and infants. Very young patients have small veins, and that increases the chances of accidentally puncturing the catheterization needle directly through them. In this article, we present the design, development and experimental evaluation of a novel hand-held robotic device for improving the process of peripheral intravenous catheterization by facilitating the needle insertion procedure. To our knowledge, this design is the first hand-held robotic device for assisting in the catheterization insertion task. Compared to the other available technologies, it has several unique advantages such as being compact, low-cost and able to reliably detect venipuncture. The system is equipped with an electrical impedance sensor at the tip of the catheterization needle, which provides real-time measurements used to supervise and control the catheter insertion process. This allows the robotic system to precisely position the needle within the lumen of the target vein, leading to enhanced catheterization success rate. Experiments conducted to evaluate the device demonstrated that it is also effective to deskill the task. Naïve subjects achieved an average catheterization success rate of 88% on a 1.5 mm phantom vessel with the robotic device versus 12% with the traditional unassisted system. The results of this work prove the feasibility of a hand-held assistive robotic device for intravenous catheterization and show that such device has the potential to greatly improve the success rate of these difficult operations.