The reduced access conditions of minimally invasive surgery and therapy (MIST) impair or completely eliminate the feel of tool—tissue interaction forces. Many researchers have been working actively on the development of force sensors and sensing techniques to address this problem. The goal of this survey article is to summarize the state of the art in force sensing techniques for medical interventions in order to identify existing limitations and future directions. A literature search was performed from January to July 2009 using a combination of keywords relevant to the area, including force, sensor, sensing, haptics, and minimally invasive surgery. The literature search resulted in 126 articles with valuable content. This article presents a summary of the force sensing technologies, design specifications for force sensors in clinical applications, force sensors and sensing instruments that have been developed for MIST, and the experiments performed to determine the need for force information. Open areas of research include force sensor design, development of alternative methods of sensing, assessment of the impact of force information on performance, determination of the benefits of haptic information, and evaluation of the human factors involved in the processing and use of force information.

Minimally invasive surgery (MIS) is carried out using long, narrow instruments and significantly reduces trauma to the body, postoperative pain, and recovery time. Unfortunately, the restricted access conditions, limited instrument motion, and degraded sense of touch inherent in MIS result in new perceptual-motor relationships, which are unfamiliar to the surgeon and require training to overcome. Current training methods do not adequately address the needs of surgeons interested in acquiring these skills. Although a significant amount of research has been focused on the development of sensorized systems for surgery, there is still a need for a system that can be used in any training scenario (laparoscopic trainer, animal laboratories, or real surgery) for the purpose of skills assessment and training. A sensorized laparoscopic instrument has been designed that is capable of noninvasively measuring its interaction with tissue in the form of forces or torques acting in all five degrees-of-freedom (DOFs) available during MIS. Strain gauges attached to concentric shafts within the instrument allow the forces acting in different directions to be isolated. An electromagnetic tracking system is used for position tracking. Two prototypes of the sensorized instrument were constructed. Position calibration shows a maximum root mean square (RMS) error of 1.3 mm. The results of the force calibration show a maximum RMS error of 0.35 N for the actuation force, 0.07 N in the $x$ and $y$ directions, and $1.5Nmm$ for the torque calibration with good repeatability and low hysteresis. Axial measurements were significantly affected by drift, noise, and coupling leading to high errors in the readings. Novel sensorized instruments for skills assessment and training have been developed and a patent has been filed for the design and operation. The instruments measure forces and torques acting at the tip of the instrument corresponding to all five DOFs available during MIS and provide position feedback in six DOFs. The instruments are similar in shape, size, and weight to traditional laparoscopic instruments allowing them to be used in any training environment. Furthermore, replaceable tips and handles allow the instruments to be used for a variety of different tasks.

Recent progress in wearable technology has made wearable tremor suppression devices (WTSDs) for Parkinson's patients a potentially viable alternative solution for tremor management. So far, in contrast to wrist and elbow tremor, finger tremors have not been studied in depth despite the huge impact that they have on a patient's daily life. In addition, more evidence has been found showing that the performance of current tremor estimators may be limited by their model order due to the multiple harmonics present in tremor. The aim of this paper is to characterize finger and wrist tremor in both the time and frequency domains, and to propose a high-order tremor estimation algorithm. Tremor magnitudes are reported in the forms of linear acceleration, angular velocity, and angular displacement. The activation of forearm flexor and extensor muscles is also investigated. The frequency analysis shows that Parkinsonian tremors produce oscillations of the hand with pronounced harmonics. At last, a high-order weighted-frequency Fourier linear combiner (WFLC)-based Kalman filter is proposed. The percentage estimation accuracy achieved from the proposed estimator is 96.3 ± 1.7%, showing average improvements of 28.5% and 48.9% over its lower-order counterpart and the WFLC. The proposed estimator shows promise for use in a WTSD.

Objective: The complexity of minimally invasive surgery (MIS) requires that trainees practice MIS skills in numerous training sessions. The goal of these training sessions is to learn how to move the instruments smoothly without damaging the surrounding tissue and achieving operative tasks with accuracy. In order to enhance the efficiency of these training sessions, the proficiency of the trainees should be assessed using an objective assessment method. Several performance metrics have been proposed and analyzed for MIS tasks. The differentiation of various levels of expertise is limited without the presence of an external evaluator. Methods: In this study, novel objective performance metrics are proposed based on mechanical energy expenditure and work. The three components of these metrics are potential energy, kinetic energy, and work. These components are optimally combined through both one-step and two-step methods. Evaluation of these metrics is accomplished for suturing and knot-tying tasks based on the performance of 30 subjects across four levels of experience. Results: The results of this study show that the one-step combined metric provides 47% and 60% accuracy in determining the level of expertise of subjects for the suturing and knot-tying tasks, respectively. The two-step combined metric provided 67% accuracy for both of the tasks studied. Conclusion: The results indicate that energy expenditure is a useful metric for developing objective and efficient assessment methods. Significance: These metrics can be used to evaluate and determine the proficiency levels of trainees, provide feedback and, consequently, enhance surgical simulators.

Wearable tremor suppression devices (WTSD) have been considered as a viable solution to manage parkinsonian tremor. WTSDs showed their ability to improve the quality of life of individuals suffering from parkinsonian tremor, by helping them to perform activities of daily living (ADL). Since parkinsonian tremor has been shown to be nonstationary, nonlinear, and stochastic in nature, the performance of the tremor models used by WTSDs is affected by their inability to adapt to the nonlinear behaviour of tremor. Another drawback that the models have is their limitation to estimate or predict one step ahead, which introduces delay when used in real time with WTSDs, which compromises performance. To address these issues, this work proposes a deep neural network model that learns the correlations and nonlinearities of tremor and voluntary motion, and is capable of multi-step prediction with minimal delay. A generalized model that is task and user-independent is presented. The model achieved an average estimation percentage accuracy of 99.2%. The average future voluntary motion prediction percentage accuracy with 10, 20, 50, and 100 steps ahead was 97.0%, 94.0%, 91.6%, and 89.9%, respectively, with prediction time as low as 1.5 ms for 100 steps ahead. The proposed model also achieved an average of 93.8% ± 1.5% in tremor reduction when it was tested in an experimental setup in real time. The tremor reduction showed an improvement of 25% over the Weighted Fourier Linear Combiner (WFLC), an estimator commonly used with WTSDs.