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.

The lack of haptic feedback in robotics-assisted surgery can result in tissue damage or accidental tool-tissue hits. This paper focuses on exploring the effect of haptic feedback via direct force reflection and visual presentation of force magnitudes on performance during suturing in robotics-assisted minimally invasive surgery (RAMIS). For this purpose, a haptics-enabled dual-arm master-slave teleoperation system capable of measuring tool-tissue interaction forces in all seven Degrees-of-Freedom (DOFs) was used. Two suturing tasks, tissue puncturing and knot-tightening, were chosen to assess user skills when suturing on phantom tissue. Sixteen subjects participated in the trials and their performance was evaluated from various points of view: force consistency, number of accidental hits with tissue, amount of tissue damage, quality of the suture knot, and the time required to accomplish the task. According to the results, visual force feedback was not very useful during the tissue puncturing task as different users needed different amounts of force depending on the penetration of the needle into the tissue. Direct force feedback, however, was more useful for this task to apply less force and to minimize the amount of damage to the tissue. Statistical results also reveal that both visual and direct force feedback were required for effective knot tightening: direct force feedback could reduce the number of accidental hits with the tissue and also the amount of tissue damage, while visual force feedback could help to securely tighten the suture knots and maintain force consistency among different trials/users. These results provide evidence of the importance of 7-DOF force reflection when performing complex tasks in a RAMIS setting.

Recently, it has been proven that targeting motor impairments as early as possible while using wearable mechatronic devices for assisted therapy can improve rehabilitation outcomes. However, despite the advanced progress on control methods for wearable mechatronic devices, the need for a more natural interface that allows for better control remains. To address this issue, electromyography (EMG)-based gesture recognition systems have been studied as a potential solution for human–machine interface applications. Recent studies have focused on developing user-independent gesture recognition interfaces to reduce calibration times for new users. Unfortunately, given the stochastic nature of EMG signals, the performance of these interfaces is negatively impacted. To address this issue, this work presents a user-independent gesture classification method based on a sensor fusion technique that combines EMG data and inertial measurement unit (IMU) data. The Myo Armband was used to measure muscle activity and motion data from healthy subjects. Participants were asked to perform seven types of gestures in four different arm positions while using the Myo on their dominant limb. Data obtained from 22 participants were used to classify the gestures using three different classification methods. Overall, average classification accuracies in the range of 67.5–84.6% were obtained, with the Adaptive Least-Squares Support Vector Machine model obtaining accuracies as high as 92.9%. These results suggest that by using the proposed sensor fusion approach, it is possible to achieve a more natural interface that allows better control of wearable mechatronic devices during robot assisted therapies.

Minimally Invasive Surgery (MIS) has been limited from its inception by insufficient haptic feedback to surgeons. To address this, various force sensing methods have been developed that can provide information about tool–tissue interaction forces. However, the results have been unreliable in most of these studies since there are many sources of error and uncertainty in the force acquisition method. Furthermore, none of the force sensing devices described in the literature can be fully cleaned or sterilized for use in surgery. To address this, a sterilizable needle-driver type grasper is presented in this paper that incorporates fiber Bragg gratings to measure axial and grasping forces. The results of its performance evaluation show that the 2-degrees of freedom grasper is capable of measuring grasping and axial forces with an accuracy of 0.27 N and 0.3 N, respectively. Integration of this device into a needle driver instrument will result in a sterilizable instrument capable of measuring tool– tissue interaction forces during MIS.