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Parkinson's disease (PD) and essential tremor are two major causes of pathological tremor among people over 60 years old. Due to the side effects and complications of traditional tremor management methods such as medication and deep brain surgery, non invasive tremor suppression methods have become more popular in recent years. Functional electrical stimulation (FES) is one of the methods used to reduce tremor in several studies. However, the effect of different FES parameters on tremor suppression and discomfort level, including amplitude, the number of pulses in each stimulation burst, frequency, and pulse width is yet to be studied for longer stimulation durations. Therefore, in this work, experiments were performed on 14 participants with PD to evaluate the effect of thirty seconds of out-of-phase electrical stimulation on wrist tremor at rest. Trials were conducted by varying the stimulation amplitude and the number of pulses while keeping the frequency and pulse width constant. Each test was repeated three times for each participant. The results showed an overall tremor suppression for 11 out of 14 participants and no average positive effects for three participants. It is concluded that despite the effectiveness of FES in tremor suppression, each set of FES parameters showed different suppression levels among participants due to the variability of tremor over time. Thus, for this method to be effective, an adaptive control system would be required to tune FES parameters in real time according to changes in tremor during extended stimulation periods.

Hand tremor is one of the dominating symptoms of Parkinson's disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.

In this work, a novel multi-modal device that allows data to simultaneously be collected from three noninva-sive sensor modalities was created. Force myography (FMG), surface electromyography (sEMG), and inertial measurement unit (IMU) sensors were integrated into a wearable armband and used to collect signal data while subjects performed gestures important for the activities of daily living (ADL). An established machine learning algorithm was used to decipher the signals to predict the user's intent/gesture being held, which could be used to control a prosthetic device. Using all three modalities provided statistically-significant improvements over most other modality combinations, as it provided the most accurate and consistent classification results. Clinical relevance-The use of three sensing modalities can improve gesture-based control of upper-limb prosthetics.

In this paper, a head-worn wearable haptic feedback device (WHFD) is developed to communicate sensory information from an upper-limb prosthesis. A 14-week, 6-stage, between-subjects study was created to investigate the learning trajectory as participants were stimulated with haptic patterns conveying joint proprioception. Healthy participants were divided into three groups, with each group using a different haptic stimulation methods. 18 participants completed the study within 7-14 weekly sessions, demonstrating that participants were, in fact, learning to interpret the haptic information. Participants in the spatiotemporal stimulation group had some advantages in interpreting the haptic information over the others; however, each stimulation method has advantages that can be exploited and hybridized for future models of the WHFD. Overall, the proposed WHFD is an effective non-invasive device that can promote greater sensory awareness for wearers of prostheses.

Introduction: Parkinsonian tremor has severely impacted the lives of 65% of individuals with Parkinson's disease, and nearly 25% do not respond to traditional treatments. Although wearable tremor suppression devices (WTSDs) have become a promising alternative approach, this technology is still in the early stages of development, and no studies have reported the stakeholders' opinions on this technology and their desired design requirements., Methods: An online survey was distributed to affected Canadians and Canadian movement disorder specialists (MDS) to acquire information on demographics, the current state of treatments, opinions on the WTSDs, and the desired design requirements of future WTSDs., Results: A total of 101 affected individuals and 24 MDS completed the survey. It was found that both groups are generally open to using WTSDs to manage tremor. The most important design requirement to end users is the adaptability to lifestyle, followed by weight and size, accurate motion, comfort, safety, quick response, and cost. Lastly, most of the participants (65%) think that the device should cost under $500., Conclusions: The findings from this study can be used as guidelines for the development of future WTSDs, such that the future generations could be evaluated and accepted by the end users., Competing Interests: Declaration of conflicting interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

The side effects and complications of traditional treatments for treating pathological tremor have led to a growing research interest in wearable tremor suppression devices (WTSDs) as an alternative approach. Similar to how the human brain coordinates the function of the human system, a tremor estimator determines how a WTSD functions. Although many tremor estimation algorithms have been developed and validated, whether they can be implemented on a cost-effective embedded system has not been studied; furthermore, their effectiveness on tremor signals with multiple harmonics has not been investigated. Therefore, in this study, four tremor estimators were implemented, evaluated, and compared: Weighted-frequency Fourier Linear Combiner (WFLC), WFLC-based Kalman Filter (WFLC-KF), Band-limited Multiple FLC, and enhanced High-order WFLC-KF (eHWFLC-KF). This study aimed to evaluate the performance of each algorithm on a bench-top tremor suppression system with 18 recorded tremor motion datasets; and compare the performance of each estimator. The experimental evaluation showed that the eHWFLC-KF-based WTSD achieved the best performance when suppressing tremor with an average of 89.3% reduction in tremor power, and an average error when tracking voluntary motion of 6.6°/s. Statistical analysis indicated that the eHWFLC-KF-based WTSD is able to reduce the power of tremor better than the WFLC and WFLC-KF, and the BMFLC-based WTSD is better than the WFLC. The performance when tracking voluntary motion is similar among all systems. This study has proven the feasibility of implementing various tremor estimators in a cost-effective embedded system, and provided a real-time performance assessment of four tremor estimators.

Intraoperative tumor localization in a deflated lung in minimally invasive surgery (MIS) is challenging as the lung cannot be manually palpated through small incisions. To do so remotely, an articulated multisensory imaging device combining tactile and ultrasound sensors was developed. It visualizes the surface tactile map and the depth of the tissue. However, with little maneuverability in MIS, localizing tumors using instrumented palpation is both tedious and inefficient. In this paper, a texture- based image guidance system that classifies tactile-guided ultrasound texture regions and provides beliefs on their types is proposed. The resulting interactive feedback allows directed palpation in MIS. A k-means classifier is used to first cluster gray-level co-occurrence matrix (GLCM)-based texture features of the ultrasound regions, followed by hidden Markov model-based belief propagation to establish confidence about the clustered features observing repeated patterns. When the beliefs converge, the system autonomously detects tumor and nontumor textures. The approach was tested on 20 ex vivo soft tissue specimens in a staged MIS. The results showed that with guidance, tumors in MIS could be localized with 98% accuracy, 99% sensitivity, and 97% specificity.Clinical Relevance- Texture-based image guidance adds efficiency and control to instrumented palpation in MIS. It renders fluidity and accuracy in image acquisition using a hand-held device where fatigue from prolonged handling affects imaging quality.

Objective: Approximately 25% of individualsliving with parkinsonian tremor do not respond to traditional treatments. Wearable tremor suppression devices (WTSD) provide an alternative approach, however, tremor in the fingers has not been given as much attention as tremor in the elbow and the wrist. Therefore, the objective of this study is to design a wearable tremor suppression glove that can suppress tremor simultaneously, but independently, in multiple hand joints without restricting the user's voluntary motion., Methods: A WTSD was designed for managing tremor in the index finger metacarpophalangeal (MCP) joint, thumb MCP joint, and the wrist. The prototype was tested and assessed on a participant living with parkinsonian tremor., Results: The experimental evaluation showed an overall suppression of 73.1%, 80.7%, and 85.5% in resting tremor, 70.2%, 79.5%, and 81% in postural tremor, and 60.0%, 58.7%, and 65.0% in kinetic tremor in the index finger MCP joint, the thumb MCP joint, and the wrist, respectively., Conclusion: This first assessment of a WTSD for people living with Parkinson's disease provides confirmation of the feasibility of the approach. The next step requires a comprehensive validation on a broader population in order to evaluate the performance of the WTSD., Significance: This study demonstrates the feasibility of using a WTSD to manage hand and finger tremor. The device enriches the field of upper-limb tremor management, as the first WTSD for multiple joints of the hand.

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.

Parkinson's Disease (PD) is considered to be the second most common age-related neuroegenerative disorder, and it is estimated that seven to ten million people worldwide have PD. One of the symptoms of PD is tremor, and studies have shown that wearable assistive devices have the potential to assist in suppressing it. However, despite the progress in the development of these devices, their performance is limited by the tremor estimators they use. Thus, a need for a tremor model that helps the wearable assistive devices to increase tremor suppression without impeding voluntary motion remains. In this work, a user-independent and task-independent tremor and voluntary motion detection method based on neural networks is proposed. Inertial measurement units (IMUs) were used to measure acceleration and angular velocity from participants with PD, these data were then used to train the neural network. The achieved estimation percentage accuracy of voluntary motion was 99.0%, and the future prediction percentage accuracy was 97.3%, 93.7%, 91.4% and 90.3% for 10 ms, 20 ms, 50 ms and 100 ms ahead, respectively. The root mean squared error (RMSE) achieved for tremor estimation was an average of 0.00087 ° /s on new unseen data, and the future prediction average RMSE across the different tasks achieved was 0.001 ° /s, 0.002 ° /s, 0.020 ° /s and 0.049 ° /s for 1 ms, 2 ms, 5 ms, and 10 ms ahead, respectively. Therefore, the proposed method shows promise for use in wearable suppression devices.

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.

Background: Orthopaedic training programs are incorporating arthroscopic simulations into their residency curricula. There is a need for a physical shoulder simulator that accommodates lateral decubitus and beach chair positions, has realistic anatomy, allows for an objective measure of performance and provides feedback to trainees., Methods: A physical shoulder simulator was developed for training basic arthroscopic skills. Sensors were embedded in the simulator to provide a means to assess performance. Subjects of varying skill level were invited to use the simulator and their performance was objectively assessed., Results: Novice subjects improved their performance after practice with the simulator. A survey completed by experts recognized the simulator as a valuable tool for training basic arthroscopic skills., Conclusions: The physical shoulder simulator helps train novices in basic arthroscopic skills and provides objective measures of performance. By using the physical shoulder simulator, residents could improve their basic arthroscopic skills, resulting in improved patient safety., (Copyright © 2017 John Wiley & Sons, Ltd.)

Background: Few studies compare the effectiveness of blocked vs random practice conditions in minimally invasive surgery training, and none have evaluated these in robotic surgery training., Methods: The dV-Trainer ® and the da Vinci ® Surgical System (dVSS) were used to compare practice conditions. Forty-two participants were randomized into blocked and random practice groups. Each participant performed five tasks: Ring Walk, Thread the Rings, Needle Targeting, Suture Sponge and Tubes Level 2. Transfer to the dVSS was also assessed., Results: No significant differences were observed between the two groups, except for a few instances. For example, during Ring Walk, the random group performed significantly faster than the blocked group (100.78 ± 5.26 s vs 121.59 ± 5.26 s, p < 0.01)., Conclusions: The study results do not follow the current evidence presented in the education literature. This is the first time that blocked versus random practice was tested for robotic surgery training., (Copyright © 2017 John Wiley & Sons, Ltd.)

Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency., Competing Interests: The authors declare no conflict of interest.

The design of a tremor estimator is an important part of designing mechanical tremor suppression orthoses. A number of tremor estimators have been developed and applied with the assumption that tremor is a mono-frequency signal. However, recent experimental studies have shown that Parkinsonian tremor consists of multiple frequencies, and that the second and third harmonics make a large contribution to the tremor. Thus, the current estimators may have limited performance on estimation of the tremor harmonics. In this paper, a high-order tremor estimation algorithm is proposed and compared with its lower-order counterpart and a widely used estimator, the Weighted-frequency Fourier Linear Combiner (WFLC), using 18 Parkinsonian tremor data sets. The results show that the proposed estimator has better performance than its lower-order counterpart and the WFLC. The percentage estimation accuracy of the proposed estimator is 85±2.9%, an average improvement of 13% over the lower-order counterpart. The proposed algorithm holds promise for use in wearable tremor suppression devices.

Minimally invasive surgery prevents surgeons from manually palpating organs to locate subsurface tumors and other structures. One solution is to use ultrasound; however, it is not always reliable. Various minimally invasive surgery instruments that provide tactile feedback have been proposed to augment ultrasound sensing for tumor localization; however, current designs have limitations such as cumbersome wiring, difficulty in manipulation, lack of sterilizability and high cost. This paper presents a novel, autoclavable, wireless, hand-held palpation instrument that uses a custom, low-cost, disposable tactile sensor to provide tactile and kinesthetic force feedback. The use of a replaceable, disposable tactile sensor avoids deterioration in sensor performance due to repeated autoclaving. The instrument features a passive joint in the end effector that allows the sensor to self-align to the palpation surface in a wide range of orientations. All of the electronics are packaged in a removable module that allows the rest of the instrument to be easily cleaned and autoclaved. Two versions of the tactile sensor, using piezoresistive sensing and capacitive sensing respectively, have been designed for use with this instrument. The instrument is shown to be able to detect 6 mm diameter spherical tumors at a depth of 9-10 mm in ex vivo tissue samples.

The growing popularity of minimally invasive surgery (MIS) can be attributed to its advantages, which include reduced post-operative pain, a shorter hospital stay, and faster recovery. However, MIS requires extensive training for surgeons to become experts in their field of practice. Different assessment methods have been proposed for evaluating the performance of surgeons and residents on surgical simulators. Nonetheless, optimal objective performance measures are still lacking. In this study, three metrics for minimally invasive skills assessment are proposed based on energy expenditure: work, potential energy and kinetic energy. In order to evaluate these metrics, two laparoscopic tasks consisting of suturing and knot-tying are investigated, involving expert and novice subjects. This study shows that measures based on energy expenditure can be used for skills assessment: all three metrics can discriminate between experts and novices for the two tasks investigated here. These measures can also reflect the efficiency of subjects when performing MIS tasks. Further modification and investigation of these metrics can extend their use to different tasks and for discriminating between various levels of experience.

Introduction: Training surgeons in minimally invasive surgery (MIS) requires surgical residents to operate under the direction of a consultant. The inability of the instructing surgeon to point at the laparoscopic monitor without releasing the instruments remains a barrier to effective instruction. The wireless hands-free surgical pointer (WHaSP) has been developed to aid instruction during MIS., Methods: The objective of this study was to evaluate the effectiveness and likeability of the WHaSP as an instructional tool compared with the conventional methods. Data were successfully collected during 103 laparoscopic cholecystectomy procedures, which had been randomized to use or not use the WHaSP as a teaching tool. Audio and video from the surgeries were recorded and analyzed. Instructing surgeons, operating surgeons, and camera assistants provided feedback through a post-operative questionnaire that used a five-level Likert scale. The questionnaire results were analyzed using a Mann-Whitney U test., Results: There were no negative effects on surgery completion time or instruction practice due to the use of the WHaSP. The number of times an instructor surgeon pointed to the laparoscopic screen with their hand was significantly reduced when the WHaSP was utilized (p < 0.001). The questionnaires showed that WHaSP users found it to be comfortable, easy to use, and easy to control. Compared to when the WHaSP was not used, users found that communication was more effective (p = 0.002), locations were easier to communicate (p < 0.001), and instructions were easier to follow (p = 0.005)., Conclusions: The WHaSP system was successfully used in surgery. It integrated seamlessly into existing equipment within the operating room and did not affect flow. The positive outcomes of utilizing the WHaSP were improved communication in the OR, improved efficiency and safety of the surgery, easy to use, and comfortable to wear. The surgeons showed a preference for utilizing the WHaSP if given a choice.

In this paper, a novel formulation for robust surgical planning of robotics-assisted minimally invasive cardiac surgery based on patient-specific preoperative images is proposed. In this context, robustness is quantified in terms of the likelihood of intraoperative collisions and of joint limit violations. The proposed approach provides a more accurate and complete formulation than existing deterministic approaches in addressing uncertainty at the task level. Moreover, it is demonstrated that the dexterity of robotic arms can be quantified as a cross-entropy term. The resulting planning problem is rendered as a chance-constrained entropy maximization problem seeking a plan with the least susceptibility toward uncertainty at the task level, while maximizing the dexterity (cross-entropy term). By such treatment of uncertainty at the task level, spatial uncertainty pertaining to mismatches between the patient-specific anatomical model and that of the actual intraoperative situation is also indirectly addressed. As a solution method, the unscented transform is adopted to efficiently transform the resulting chance-constrained entropy maximization problem into a constrained nonlinear program without resorting to computationally expensive particle-based methods.

Many challenges exist when teaching and learning arthroscopic surgery, carrying a high risk of damaging the joint during the learning process. To minimize risk, the use of arthroscopy simulators allows trainees to learn basic skills in a risk-free environment before entering the operating room. A high-fidelity physical knee arthroscopy simulator is proposed to bridge the gap between surgeons and residents. The simulator is composed of modular and replaceable elements and can measure applied forces, instrument position and hand motion, in order to assess performance in real time. A construct validity study was conducted in order to assess the performance improvement of novices after practicing with the simulator. In addition, a face validity study involving expert surgeons indicated that the simulator provides a realistic scenario suitable for teaching basic skills. Future work involves the development of better metrics to assess user performance.

In this paper, a computational framework for patient-specific preoperative planning of Robotics-Assisted Minimally Invasive Cardiac Surgery (RAMICS) is presented. It is expected that preoperative planning of RAMICS will improve the success rate by considering robot kinematics, patient-specific thoracic anatomy, and procedure-specific intraoperative conditions. Given the significant anatomical features localized in the preoperative computed tomography images of a patients thorax, port locations and robot orientations (with respect to the patients body coordinate frame) are determined to optimize qualities such as dexterity, reachability, tool approach angles and maneuverability. To address intraoperative geometric uncertainty, the problem is formulated as a Generalized Semi-Infinite Program (GSIP) with a convex lower-level problem to seek a plan that is less sensitive to geometric uncertainty in the neighborhood of surgical targets. It is demonstrated that with a proper formulation of the problem, the GSIP can be replaced by a tractable constrained nonlinear program that uses a multi-criteria objective function to balance between the nominal task performance and robustness to collisions and joint limit violations. Finally, performance of the proposed formulation is demonstrated by a comparison between the plans generated by the algorithm and those recommended by an experienced surgeon for several case studies.

The complexity of knee arthroscopy makes it difficult to teach and assess skill level during real surgery. Simulator-based training is ideal for this complex procedure. To address the limitations of existing systems, a physical simulator, capable of providing skills assessment and feedback has been developed. The simulator measures the forces applied on the femur and acting on the tools. An experimental evaluation was conducted to assess the differences in task completion time and applied forces for fourteen tasks performed by trainees and expert surgeons. Initial results show that the simulator, together with well-chosen tasks, can potentially be used to assess user performance.

Minimally invasive surgical training is complicated due to the constraints imposed by the surgical environment. Sensorized laparoscopic instruments capable of sensing force in five degrees of freedom and position in six degrees of freedom were evaluated. Novice and expert laparoscopists performed the complex minimally invasive surgical task of suturing using the novel instruments. Their force and position profiles were compared. The novel minimally invasive surgical instrument proved to be construct valid and capable of detecting differences between novices and experts in a laparoscopic suturing task with respect to force and position. Further evaluation is mandated for a better understanding of the ability to predict performance based on force and position as well as the potential for new metrics in minimally invasive surgical education.

In this paper, a planning strategy for robotics-assisted interventions is formulated in terms of uncertainty at the task level. The proposed formulation attempts to increase the chance of success by maximizing robustness with respect to the task uncertainty. It is assumed that the instrument tip pose has a Gaussian distribution in the vicinity of the desired task frame, and the planner is formulated as a chance-constrained programming problem in terms of the chance of collisions and joint limit violations based on the inverse kinematics of the arms. The proposed objective function addresses the robustness as well as the performance of the robotic arms. As an illustrative example, the planning strategy is implemented for LIMA harvesting in minimally invasive coronary artery bypass with the da Vinci robot.

Background: Natural orifice transluminal endoscopic surgery (NOTES) may represent the next frontier for therapeutic minimally invasive surgery; however, its feasibility is currently limited by the lack of suitable instruments. Identifying the forces required to manipulate tissue during NOTES is a necessary first step in the development of better instrumentation., Methods: Sensorized instruments were used to measure the forces acting at the tip of the instruments during transgastric and transperineal NOTES procedures performed in two female pigs. The maximum and average forces when handling tissue were determined and compared., Results: The results show that, for the transgastric approach, the average forces required are significantly less than in the transperineal approach (43% less), and that the maximum forces required are almost 8 and 16 N in the transgastric and transperineal approaches, respectively. The forces were higher than 5 N in 1.6% of the measurements in the transgastric approach and 2.9% in the transperineal approach., Conclusions: This study presents an experimental measurement of tissue manipulation forces in a NOTES procedure. This information may be valuable for research groups interested in developing NOTES instruments and devices. It is recommended that NOTES instruments be designed to easily handle forces as high as 16 N.

Objective: Robotics-assisted endoscopic atraumatic coronary artery bypass has been shown to be effective in reducing surgical morbidity and length of hospital stay. Unfortunately, the criteria for selecting eligible patients for this procedure are still primitive. This has motivated the use of preoperative computed tomography scans to establish patient eligibility. The objective of this study is to establish which image measurements can be correlated to procedure success., Methods: A retrospective study was performed in 144 patients who underwent robotics-assisted coronary bypass surgery. After an initial set of 55 patients, preoperative computed tomography scans of the other patients were used to obtain patient specific measurements: the lateral distance between the midline of the sternum to the left anterior descending coronary artery and its depth from the skin surface, anteroposterior diameter of the thoracic cavity, and the transverse diameter of the thoracic cavity. The procedures were rated as successful if completed in a minimally invasive manner. Different combinations of the variables were evaluated and correlated with success., Results: A strong correlation was found between success rate and the ratio of the lateral distance to the transverse diameter in the female patients only (0.532, P = 0.006). A ratio of less than 0.20 significantly increased the occurrence of conversion during this procedure in female cases., Conclusions: The lateral distance of the left anterior descending coronary artery from the midline divided by the transverse thoracic width of a female patient shows a significant correlation with procedure success. No significant correlations were found for male patients.

Background: Minimally invasive surgery (MIS) suffers from the inability to directly palpate organs for tumour localization. A tactile sensing system (TSS), consisting of a probe and a visualization interface, was developed to present an active pressure map of the contact surface to locate tumours during MIS., Methods: The TSS performance was compared to MIS graspers to locate occult 10 mm phantom tumours in ex vivo bovine liver and ex vivo porcine lung. Performance assessment included applied pressure, localization distance and accuracy., Results: The TSS realized a relative 71% reduction in maximum applied pressure and a 31% increase in detection accuracy in liver tissue (when compared to MIS graspers) and demonstrated no significant differences in performance when palpating lung tissue., Conclusions: The TSS may help surgeons to identify occult tumours during surgery by restoring some of the haptic information lost during MIS., ((c) 2010 John Wiley & Sons, Ltd.)

This study compares 3 different saw types to determine which is best suited for integration into a minimally invasive bone saw. A handheld electric jigsaw, a coping saw, and a Gigli saw were used to cut into porcine ilium. Heat generated was measured using a thermocouple, and forces applied during cutting were recorded using a force/torque sensor. The coping saw generated an average maximum temperature that was 26 degrees C less than that generated using the jigsaw (P < .001) and 14 degrees C less than that for the Gigli saw (P < .001). On average, the maximum force applied through the coping saw was 14 N less than that through the jigsaw (P < .001) and 18 N less than that through the Gigli saw (P < .001). Out of the 3 saws tested, the coping saw is optimal for cutting bone based on heat generation and required force.

By combining a number of simple transducer modules, an arbitrarily complex sensing system may be produced to accommodate a wide range of applications. This work outlines a novel software architecture and knowledge representation scheme that has been developed to support this type of flexible and reconfigurable modular sensing system. Template algorithms are used to embed intelligence within each module. As modules are added or removed, the composite sensor is able to automatically determine its overall geometry and assume an appropriate collective identity. A virtual machine-based middleware layer runs on top of a real-time operating system with a pre-emptive kernel, enabling platform-independent template algorithms to be written once and run on any module, irrespective of its underlying hardware architecture. Applications that may benefit from easily reconfigurable modular sensing systems include flexible inspection, mobile robotics, surveillance, and space exploration.

Background: Localizing lung tumours during minimally invasive surgery is difficult, since restricted access precludes manual palpation and pre-operative imaging cannot map directly to the intra-operative lung. This study analyses the force-sensing performance that would allow an instrumented kinaesthetic probe to localize tumours based on stiffness variations of the lung parenchyma., Methods: Agar injected into ex vivo porcine lungs produced a model approximating commonly encountered tumours. Force-deformation data were collected from multiple sites at various palpation depths and velocities, before and after the tumours were injected., Results: Analysis showed an increase in force after the tumours were injected, in the range 0.07-0.16 N at 7 mm (p < 10(-4)). A 2 mm/s palpation velocity minimized exponential stress decay at constant depths, facilitating easier comparisons between measurements., Conclusion: A sensing range of 0-2 N, with 0.01 N resolution, should allow a kinaesthetic palpation probe to resolve local tissue stiffness changes that suggest an underlying tumour.