Characterization of Parkinsonian Hand Tremor and Validation of a High-Order Tremor Estimator.

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.

Design and validation of a high-order weighted-frequency fourier linear combiner-based Kalman filter for parkinsonian tremor estimation.

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.

A semi-infinite programming approach to preoperative planning of robotic cardiac surgery under geometric uncertainty.

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.

Toward construct validity for a novel sensorized instrument-based minimally invasive surgery simulation system.

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.

New tactile sensing system for minimally invasive surgical tumour localization.

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.

Feasibility of locating tumours in lung via kinaesthetic feedback.

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.