Quantification of Forces During a Neurosurgical Procedure: A Pilot Study

Development of a single port dual arm robotically steerable endoscope for neurosurgical applications

Single-port surgical robots have gained popularity due to less patient trauma and quicker post-surgery recovery. However, due to limited access provided by a single incision, the miniaturization and maneuverability of these robots still needs to be improved. In this paper, we propose the design of a single-port, dual-arm robotically steerable endoscope containing one steerable major cannula and two steerable minor cannulas. By integrating the proposed nine degrees-of-freedom (DoFs) robotically steerable endoscope with an industrial robotic arm and a joystick controller, this robotic system can potentially achieve intuitive, and remote multi-arm manipulation capability. We present the design of the robotically steerable endoscope consisting of tendon-driven joints controlled by a compact actuation system and derive the kinematic and static models. We validate the derived models using different kinematic trajectories with an average RMSE value of 0.98 mm and 0.66 mm for the distal tip position errors of the two steerable minor cannulas.

Medical student perception of force application: An accuracy assessment and pilot training program

BACKGROUND

It is unclear how accurately students can reproduce specific forces that are often required for physical examination maneuvers. This study aimed to determine the baseline accuracy of force application for preclinical medical students, evaluate the effectiveness of a quantitative visual feedback intervention, and investigate whether certain demographics influence accuracy.

MATERIALS AND METHODS

First- and second-year medical students were enrolled and demographic data were collected. Students blindly applied their estimation of 15 lbs (6.8 kg), 3 lbs (1.4 kg), 10 lbs (4.5 kg), 1.5 lbs (0.7 kg), and 6 lbs (2.7 kg) of force on a scale. Visual feedback training was then performed wherein students applied a series of additional forces unblinded five times, and then blindly administered the same five initial forces 12 minutes and one week later. Accuracy was compared at each time point and a regression analysis was evaluated for predictors of accuracy.

RESULTS

Thirty-three students participated. The mean baseline accuracy was 38.3%, 41.1% immediately following intervention, and 35.6% one week later (P = 0.66). Accuracy was significantly higher at higher intended forces compared to lower forces (P < 0.05). The number of prior occupations was a positive independent predictor (P = 0.04), and the number of sports played was noted to be a negative predictor (P = 0.01), of baseline accuracy.

CONCLUSIONS

Medical students' ability to accurately reproduce clinically relevant forces is poor. There is a clear need to implement a robust training program in medical education, and students may need multiple training sessions to refine this skill.

Electrotactile proprioception training improves finger control accuracy and potential mechanism is proprioceptive recalibration

This study presents a novel training technique, visual + electrotactile proprioception training (visual + EP training), which provides additional proprioceptive information via tactile channel during motor training to enhance the training effectiveness. In this study, electrotactile proprioception delivers finger aperture distance information in real-time, by mapping frequency of electrical stimulation to finger aperture distance. To test the effect of visual + EP training, twenty-four healthy subjects participated in the experiment of matching finger aperture distance with distance displayed on screen. Subjects were divided to three groups: the first group received visual training and the other two groups received visual + EP training with or without a post-training test with electrotactile proprioception. Finger aperture control error was measured before and after the training (baseline, 15-min post, 24-h post). Experimental data suggest that both training methods decreased finger aperture control error at 15-min post-training. However, at 24-h post-training, the training effect was fully retained only for the subjects who received visual + EP training, while it washed out for the subjects with visual training. Distribution analyses based on Bayesian inference suggest that the most likely mechanism of this long-term retention is proprioceptive recalibration. Such applications of artificially administered sense have the potential to improve motor control accuracy in a variety of applications.

Design and Analysis of a Hand-Held Surgical Forceps with a Force-Holding Function

Physiological hand tremors, twitching, and the nonlinear characteristics of the relationship between surgical forceps clamping force and operating force seriously affect the clamping accuracy of surgical instruments. To address this problem, a new type of surgical forceps with a force-holding function was developed to replace traditional forceps, which was studied in terms of structural design, statics, and dynamics. The overall structure of the surgical forceps was designed based on the lever principle, the kinematic model of the clamping part of the surgical forceps was established by the geometrical method, and the correctness of the kinematic model was verified by ADAMS. To address the clamping accuracy of the surgical forceps, a stress analysis was performed, its dynamics model was established, a finite element simulation was performed, the modal of the forceps was optimized using the Box-Behnken method, and, finally, an experimental platform was built to perform the accuracy test. The results demonstrate that the designed surgical forceps exhibit high clamping accuracy and fulfill the design specifications for surgical operations.

Force Profile as Surgeon-Specific Signature

Objective

To investigate the notion that a surgeon's force profile can be the signature of their identity and performance.

Summary background data

Surgeon performance in the operating room is an understudied topic. The advent of deep learning methods paired with a sensorized surgical device presents an opportunity to incorporate quantitative insight into surgical performance and processes. Using a device called the SmartForceps System and through automated analytics, we have previously reported surgeon force profile, surgical skill, and task classification. However, an investigation of whether an individual surgeon can be identified by surgical technique has yet to be studied.

Methods

In this study, we investigate multiple neural network architectures to identify the surgeon associated with their time-series tool-tissue forces using bipolar forceps data. The surgeon associated with each 10-second window of force data was labeled, and the data were randomly split into 80% for model training and validation (10% validation) and 20% for testing. Data imbalance was mitigated through subsampling from more populated classes with a random size adjustment based on 0.1% of sample counts in the respective class. An exploratory analysis of force segments was performed to investigate underlying patterns differentiating individual surgical techniques.

Results

In a dataset of 2819 ten-second time segments from 89 neurosurgical cases, the best-performing model achieved a micro-average area under the curve of 0.97, a testing F1-score of 0.82, a sensitivity of 82%, and a precision of 82%. This model was a time-series ResNet model to extract features from the time-series data followed by a linearized output into the XGBoost algorithm. Furthermore, we found that convolutional neural networks outperformed long short-term memory networks in performance and speed. Using a weighted average approach, an ensemble model was able to identify an expert surgeon with 83.8% accuracy using a validation dataset.

Conclusions

Our results demonstrate that each surgeon has a unique force profile amenable to identification using deep learning methods. We anticipate our models will enable a quantitative framework to provide bespoke feedback to surgeons and to track their skill progression longitudinally. Furthermore, the ability to recognize individual surgeons introduces the mechanism of correlating outcome to surgeon performance.

Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance

Surgical data quantification and comprehension expose subtle patterns in tasks and performance. Enabling surgical devices with artificial intelligence provides surgeons with personalized and objective performance evaluation: a virtual surgical assist. Here we present machine learning models developed for analyzing surgical finesse using tool-tissue interaction force data in surgical dissection obtained from a sensorized bipolar forceps. Data modeling was performed using 50 neurosurgery procedures that involved elective surgical treatment for various intracranial pathologies. The data collection was conducted by 13 surgeons of varying experience levels using sensorized bipolar forceps, SmartForceps System. The machine learning algorithm constituted design and implementation for three primary purposes, i.e., force profile segmentation for obtaining active periods of tool utilization using T-U-Net, surgical skill classification into Expert and Novice, and surgical task recognition into two primary categories of Coagulation versus non-Coagulation using FTFIT deep learning architectures. The final report to surgeon was a dashboard containing recognized segments of force application categorized into skill and task classes along with performance metrics charts compared to expert level surgeons. Operating room data recording of > 161 h containing approximately 3.6 K periods of tool operation was utilized. The modeling resulted in Weighted F1-score = 0.95 and AUC = 0.99 for force profile segmentation using T-U-Net, Weighted F1-score = 0.71 and AUC = 0.81 for surgical skill classification, and Weighted F1-score = 0.82 and AUC = 0.89 for surgical task recognition using a subset of hand-crafted features augmented to FTFIT neural network. This study delivers a novel machine learning module in a cloud, enabling an end-to-end platform for intraoperative surgical performance monitoring and evaluation. Accessed through a secure application for professional connectivity, a paradigm for data-driven learning is established.

A Sensorised Surgical Glove to Analyze Forces During Neurosurgery

BACKGROUND

Measuring intraoperative forces in real time can provide feedback mechanisms to improve patient safety and surgical training. Previous force monitoring has been achieved through the development of specialized and adapted instruments or use designs that are incompatible with neurosurgical workflow.

OBJECTIVE

To design a universal sensorised surgical glove to detect intraoperative forces, applicable to any surgical procedure, and any surgical instrument in either hand.

METHODS

We created a sensorised surgical glove that was calibrated across 0 to 10 N. A laboratory experiment demonstrated that the sensorised glove was able to determine instrument-tissue forces. Six expert and 6 novice neurosurgeons completed a validated grape dissection task 20 times consecutively wearing the sensorised glove. The primary outcome was median and maximum force (N).

RESULTS

The sensorised glove was able to determine instrument-tissue forces reliably. The average force applied by experts (2.14 N) was significantly lower than the average force exerted by novices (7.15 N) ( P = .002). The maximum force applied by experts (6.32 N) was also significantly lower than the maximum force exerted by novices (9.80 N) ( P = .004). The sensorised surgical glove's introduction to operative workflow was feasible and did not impede on task performance.

CONCLUSION

We demonstrate a novel and scalable technique to detect forces during neurosurgery. Force analysis can provide real-time data to optimize intraoperative tissue forces, reduce the risk of tissue injury, and provide objective metrics for training and assessment.

Tool-Tissue Forces in Hemangioblastoma Surgery

OBJECTIVE

Surgical resection of intracranial hemangioblastoma poses technical challenges that may be difficult to impart to trainees. Here, we introduce knowledge of tool-tissue forces in Newton (N), observed during hemangioblastoma surgery.

METHODS

Seven surgeons (2 groups: trainees and mentor), with mentor (n = 1) and trainees (n = 6, PGY 1-6 including clinical fellowship), participated in 6 intracranial hemangioblastoma surgeries. Using sensorized bipolar forceps, we evaluated tool-tissue force profiles of 5 predetermined surgical tasks: 1) dissection, 2) coagulation, 3) retracting, 4) pulling, and 5) manipulating. Force profile for each trial included force duration, average, maximum, minimum, range, standard deviation (SD), and correlation coefficient. Force errors including unsuccessful trial bleeding or incomplete were compared between surgeons and with successful trials.

RESULTS

Force data from 718 trials were collected. The mean (standard deviation) of force used in all surgical tasks and across all surgical levels was 0.20 ± 0.17 N. The forces exerted by trainee surgeons were significantly lower than those of the mentor (0.15 vs. 0.24; P < 0.0001). A total of 18 (4.5%) trials were unsuccessful, 4 of them being unsuccessful trial-bleeding and the rest, unsuccessful trial-incomplete. The force in unsuccessful trial-bleeding was higher than successful trials (0.3 [0.09] vs. 0.17 [0.11]; P = 0.0401). Toward the end of surgery, higher force was observed (0.17 vs. 0.20; P < 0.0001).

CONCLUSIONS

The quantification of tool-tissue forces during hemangioblastoma surgery with feedback to the surgeon, could well enhance surgical training and allow avoidance of bleeding associated with high force error.

A data-driven performance dashboard for surgical dissection

Surgical error and resulting complication have significant patient and economic consequences. Inappropriate exertion of tool-tissue force is a common variable for such error, that can be objectively monitored by sensorized tools. The rich digital output establishes a powerful skill assessment and sharing platform for surgical performance and training. Here we present SmartForceps data app incorporating an Expert Room environment for tracking and analysing the objective performance and surgical finesse through multiple interfaces specific for surgeons and data scientists. The app is enriched by incoming geospatial information, data distribution for engineered features, performance dashboard compared to expert surgeon, and interactive skill prediction and task recognition tools to develop artificial intelligence models. The study launches the concept of democratizing surgical data through a connectivity interface between surgeons with a broad and deep capability of geographic reach through mobile devices with highly interactive infographics and tools for performance monitoring, comparison, and improvement.

Three-Dimensional Force Perception of Robotic Bipolar Forceps for Brain Tumor Resection

Three-dimensional force perception is critically important in the enhancement of human force perception to minimize brain injuries resulting from excessive forces applied by surgical instruments in robot-assisted brain tumor resection. And surgeons are not responsive enough to interpret tool-tissue interaction forces. In previous studies, various force measurement techniques have been published. In neurosurgical scenarios, there are still some drawbacks to these presented approaches to forces perception. Because of the narrow, and slim configuration of bipolar forceps, three-dimensional contact forces on forceps tips are not easy to be traced in real-time. Five fundamental acts of handling bipolar forceps are poking, opposing, pressing, opening, and closing. The first three acts independently correspond to the axial force of z, x, y. So, in this paper, typical interactions between bipolar forceps and brain tissues have been analyzed. A three-dimensional force perception technique to collect force data on bipolar forceps tips by installing three fiber Bragg grating sensors (FBGs) on each prong of bipolar forceps in real-time is proposed. Experiments using a tele-neurosurgical robot were performed on an in vitro pig brain. In the experiments, three-dimensional forces were tracked in real-time. It is possible to experience forces at a minimum of 0.01 N. The three-dimensional force perception range is 0–4 N. The calibrating resolution on x, y, and z, is 0.01, 0.03, 0.1 N, separately. According to our observation, the measurement accuracy precision is over 95%.

Calibration of surgical tools using multilevel modeling with LINEX loss function: Theory and experiment

Quantifying the tool–tissue interaction forces in surgery can be used in the training process of novice surgeons to help them better handle surgical tools and avoid exerting excessive forces. A significant challenge concerns the development of proper statistical learning techniques to model the relationship between the true force exerted on the tissue and several outputs read from sensors mounted on the surgical tools. We propose a nonparametric bootstrap technique and a Bayesian multilevel modeling methodology to estimate the true forces. We use the linear exponential loss function to asymmetrically penalize the over and underestimation of the applied forces to the tissue. We incorporate the direction of the force as a group factor in our analysis. A weighted approach is used to account for the nonhomogeneity of read voltages from the surgical tool. Our proposed Bayesian multilevel models provide estimates that are more accurate than those under the maximum likelihood and restricted maximum likelihood approaches. Moreover, confidence bounds are much narrower and the biases and root mean squared errors are significantly smaller in our multilevel models with the linear exponential loss function.

Tool-tissue forces in surgery: A systematic review

Background

Excessive tool-tissue interaction forces often result in tissue damage and intraoperative complications, while insufficient forces prevent the completion of the task. This review sought to explore the tool-tissue interaction forces exerted by instruments during surgery across different specialities, tissues, manoeuvres and experience levels.

Materials & methods

A PRISMA-guided systematic review was carried out using Embase, Medline and Web of Science databases.

Results

Of 462 articles screened, 45 studies discussing surgical tool-tissue forces were included. The studies were categorized into 9 different specialities with the mean of average forces lowest for ophthalmology (0.04N) and highest for orthopaedic surgery (210N). Nervous tissue required the least amount of force to manipulate (mean of average: 0.4N), whilst connective tissue (including bone) required the most (mean of average: 45.8). For manoeuvres, drilling recorded the highest forces (mean of average: 14N), whilst sharp dissection recorded the lowest (mean of average: 0.03N). When comparing differences in the mean of average forces between groups, novices exerted 22.7% more force than experts, and presence of a feedback mechanism (e.g. audio) reduced exerted forces by 47.9%.

Conclusions

The measurement of tool-tissue forces is a novel but rapidly expanding field. The range of forces applied varies according to surgical speciality, tissue, manoeuvre, operator experience and feedback provided. Knowledge of the safe range of surgical forces will improve surgical safety whilst maintaining effectiveness. Measuring forces during surgery may provide an objective metric for training and assessment. Development of smart instruments, robotics and integrated feedback systems will facilitate this.

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This review explores tool-tissue forces during surgery, a new and expanding field.

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Forces were lowest in ophthalmology (0.04N) and highest in orthopaedics (210N).

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Forces were lowest during sharp dissection (0.03N) and highest when drilling (14N).

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Being an expert (vs. novice) and having feedback mechanisms (e.g. haptic) reduced exerted forces.

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Development of force metrics will facilitate training, assessment & novel technology.

Challenges in developing a magnetic resonance-compatible haptic hand-controller for neurosurgical training

A haptic device is an actuated human-machine interface utilized by an operator to dynamically interact with a remote environment. This interaction could be virtual (virtual reality) or physical such as using a robotic arm. To date, different mechanisms have been considered to actuate the haptic device to reflect force feedback from the remote environment. In a low-force environment or limited working envelope, the control of some actuation mechanisms such as hydraulic and pneumatic may be problematic. In the development of a haptic device, challenges include limited space, high accuracy or resolution, limitations in kinematic and dynamic solutions, points of singularity, dexterity as well as control system development/design. Furthermore, the haptic interface designed to operate in a magnetic resonance imaging environment adds additional challenges related to electromagnetic interference, static/variable magnetic fields, and the use of magnetic resonance-compatible materials. Such a device would allow functional magnetic resonance imaging to obtain information on the subject's brain activity while performing a task. When used for surgical trainees, functional magnetic resonance imaging could provide an assessment of surgical skills. In this application, the trainee, located supine within the magnet bore while observing the task environment on a graphical user interface, uses a low-force magnetic resonance-compatible haptic device to perform virtual surgical tasks in a limited space. In the quest to develop such a device, this review reports the multiple challenges faced and their potential solutions. The review also investigates efforts toward prototyping such devices and classifies the main components of a magnetic resonance-compatible device including actuation and sensory systems and materials used.

Perceptual Surgical Knife with Wavelet Denoising

Robotic surgery is a new technology in medical applications and has been undergoing rapid development. The surgical knife, essential for robotic surgery, has the ability to determine the success of an operation. In this paper, on the basis of the principle of field-effect transistors (FETs), a perceptual surgical knife is proposed to detect the electrons or electric field of the human body with distinguishable signals. In addition, it is difficult to discriminate between the motions of surgical knives from the perceptual signals that are disturbed by high-frequency Gaussian white noise. Therefore, the wavelet denoising approach is chosen to reduce the high-frequency noise. The proposed perceptual surgical knife with the wavelet denoising method has the characteristics of high sensitivity, low cost, and good repeatability.

Nonparametric bootstrap technique for calibrating surgical SmartForceps: theory and application

Knowledge of forces, exerted on the brain tissue during the performance of neurosurgical tasks, is critical for quality assurance, case rehearsal, and training purposes. Quantifying the interaction forces has been made possible by developing SmartForceps, a bipolar forceps retrofitted by a set of strain gauges. The forces are estimated using voltages read from strain gauges. We therefore need to quantify the force-voltage relationship to estimate the interaction forces during microsurgery. This problem has been addressed in the literature by following the physical and deterministic properties of the force-sensing strain gauges without obtaining the precision associated with each estimate. In this paper, we employ a probabilistic methodology by using a nonparametric Bootstrap approach to obtain both point and interval estimates of the applied forces at the tool tips, while the precision associated with each estimate is provided. To show proof-of-concept, the Bootstrap technique is employed to estimate unknown forces, and construct necessary confidence intervals using observed voltages in data sets that are measured from the performance of surgical tasks on a cadaveric brain. Results indicate that the Bootstrap technique is capable of estimating tool-tissue interaction forces with acceptable level of accuracy compared to the linear regression technique under the normality assumption.

The force pyramid: a spatial analysis of force application during virtual reality brain tumor resection

OBJECTIVE

Virtual reality simulators allow development of novel methods to analyze neurosurgical performance. The concept of a force pyramid is introduced as a Tier 3 metric with the ability to provide visual and spatial analysis of 3D force application by any instrument used during simulated tumor resection. This study was designed to answer 3 questions: 1) Do study groups have distinct force pyramids? 2) Do handedness and ergonomics influence force pyramid structure? 3) Are force pyramids dependent on the visual and haptic characteristics of simulated tumors?

METHODS

Using a virtual reality simulator, NeuroVR (formerly NeuroTouch), ultrasonic aspirator force application was continually assessed during resection of simulated brain tumors by neurosurgeons, residents, and medical students. The participants performed simulated resections of 18 simulated brain tumors with different visual and haptic characteristics. The raw data, namely, coordinates of the instrument tip as well as contact force values, were collected by the simulator. To provide a visual and qualitative spatial analysis of forces, the authors created a graph, called a force pyramid, representing force sum along the z-coordinate for different xy coordinates of the tool tip.

RESULTS

Sixteen neurosurgeons, 15 residents, and 84 medical students participated in the study. Neurosurgeon, resident and medical student groups displayed easily distinguishable 3D “force pyramid fingerprints.” Neurosurgeons had the lowest force pyramids, indicating application of the lowest forces, followed by resident and medical student groups. Handedness, ergonomics, and visual and haptic tumor characteristics resulted in distinct well-defined 3D force pyramid patterns.

CONCLUSIONS

Force pyramid fingerprints provide 3D spatial assessment displays of instrument force application during simulated tumor resection. Neurosurgeon force utilization and ergonomic data form a basis for understanding and modulating resident force application and improving patient safety during tumor resection.

Tool-Tissue Interaction Forces in Brain Arteriovenous Malformation Surgery

OBJECTIVE

Surgical resection of a brain arteriovenous malformation (AVM) poses a technical challenge because of the fragility and number of small feeding and draining vessels around the nidus. Acquiring knowledge of the optimal force applied to such tissue is important in surgical performance and education.

METHODS

A force-sensing bipolar forceps was developed through installation of strain gauge sensors, and force profiles were obtained from 2 AVM surgeries. The force data associated with vessel injury, unsuccessful trial, was compared with that from successful trials. Receiver operating curve analysis was used for determining optimal force threshold and evaluating the discriminative accuracy of measurement.

RESULTS

Force data from 519 trials was collected, of which 16 (3.1%) were unsuccessful. The mean and maximum forces in successful trials were 0.23 ± 0.06 N and 0.35 ± 0.11 N compared with unsuccessful trials of 0.33 ± 0.05 N and 0.53 ± 0.11 N, respectively (P < 0.001). There was a strong association of mean and maximum force peaks with unsuccessful trials as reflected by the area under the curve of 0.91 and 0.87, respectively. Threshold analysis showed that the rate of unsuccessful trials and error forces tended to increase with surgical time.

CONCLUSIONS

Excessive force at the tool tip may result in injury to fragile vessels during AVM surgery. A quantifiable metric through force sensing instruments can detect and predict the occurrence of such injury. Such an instrument may be ideal for resident training and evaluation.

A "Smart" Force-Limiting Instrument for Microsurgery: Laboratory and In Vivo Validation

Residents are required to learn a multitude of skills during their microsurgical training. One such skill is the judicious application of force when handling delicate tissue. An instrument has been developed that indicates to the surgeon when a force threshold has been exceeded by providing vibrotactile feedback. The objective of this study was to validate the use of this “smart” force-limiting instrument for microsurgery. A laboratory and an in vivo experiment were performed to evaluate the force-limiting instrument. In the laboratory experiment, twelve novice surgeons were randomly allocated to use either the force-limiting instrument or a standard instrument. Surgeons were then asked to perform microsurgical dissection in a model. In the in vivo experiment, an intermediate surgeon performed microsurgical dissection in a stepwise fashion, alternating every 30 seconds between use of the force-limiting instrument and a standard instrument. The primary outcomes were the forces exerted and the OSATS scores. In the laboratory experiment, the maximal forces exerted by novices using the force-limiting instrument were significantly less than using a standard instrument, and were comparable to intermediate and expert surgeons (0.637N versus 4.576N; p = 0.007). In the in vivo experiment, the maximal forces exerted with the force-limiting instrument were also significantly less than with a standard instrument (0.441N versus 0.742N; p <0.001). Notably, use of the force-limiting instrument did not significantly impede the surgical workflow as measured by the OSATS score (p >0.1). In conclusion, the development and use of this force-limiting instrument in a clinical setting may improve patient safety.

Force-Sensing Silicone Retractor for Attachment to Surgical Suction Pipes

This paper presents a novel force-sensing silicone retractor that can be attached to a surgical suction pipe to improve the usability of the suction and retraction functions during neurosurgery. The retractor enables simultaneous utilization of three functions: suction, retraction, and retraction-force sensing. The retractor also reduces the number of tool changes and ensures safe retraction through visualization of the magnitude of the retraction force. The proposed force-sensing system is based on a force visualization mechanism through which the force is displayed in the form of motion of a colored pole. This enables surgeons to estimate the retraction force. When a fiberscope or camera is present, the retractor enables measurement of the retraction force with a resolution of 0.05 N. The retractor has advantages of being disposable, inexpensive, and easy to sterilize or disinfect. Finite element analysis and experiments demonstrate the validity of the proposed force-sensing system.

Quantifying force and positional frequency bands in neurosurgical tasks

To establish the design requirements for an MR-compatible haptic hand-controller, this paper measures magnitudes and frequency bands of three mechanical motion and interaction components during the performance of neurosurgical tasks on a cadaveric brain. The hand-controller would allow the performance of virtual neurosurgical tasks within the bore of a high field magnet during image acquisition, i.e., functional MRI. The components are the position and the orientation of a surgical tool, and the force interaction between the tool and the brain tissue. A bipolar forceps was retrofitted with a tracking system and a set of force sensing components to measure displacements and forces, respectively. Results showed working positional, rotational, and force frequency bands of 3, 3 and 5 Hz, respectively. Peak forces of 1.4, 2.9 and 3.0 N were measured in the Cartesian coordinate system. A workspace of 50.1 × 39.8 × 58.2 mm(3) and orientation ranges of 40.4°, 60.1° and 63.1° for azimuth, elevation, and roll angles were observed. The results contribute in providing information specific to neurosurgery that can be used to effectively design a compact and customized haptic hand-controller reflecting characteristics of neurosurgical tasks.