Due to its monolithic structure and high dexterity, the compliant mechanism becomes an emerging solution to miniaturize surgical forceps for minimally invasive procedures. However, it is complicated and inefficient to use traditional rigid-link-based kinematic method to synthesize compliant forceps. In this paper, we present a topology-optimization-based method to automatically synthesize compliant forceps for robot-assisted minimally invasive surgery (RMIS). The basic geometry modeling tool and the automatic synthesis algorithm were both implemented in Matlab. Several synthesis examples were presented to show the performance of the proposed method. The realized forceps and a continuum manipulator have been constructed and 3D-printed, which demonstrated the application of the automatic synthesis method in RMIS.
This paper presents the proof-of-concept of a home-based gamified wrist rehabilitation training system for children with cerebral palsy (CCP). We describe the user-centered design process of this system, which is composed of a wrist-worn inertial measurement unit (IMU) and a tangible device with an embedded IMU. The system employs a quaternion-based algorithm for automatic real-time estimation of the range of motion (RoM) covered by adduction/abduction and flexion/extension motions of the wrist. Experimental validation shows that the RoM can be determined with sufficient accuracy to control a game and that the algorithm is applicable in CCP. A serious game, which uses the presented algorithm and enables feedback as well as motivating stimuli, is implemented and evaluated by physiotherapists.
Correct torqueing of bone screws is important for orthopaedic surgery. Surgeons mainly tighten screws ad hoc, risking inappropriate torqueing. An adaptive torque-limiting screwdriver may be able to measure the torque-rotation response and use parameter identification of key material properties to recommend optimal torques. This paper analyses the identifiability and sensitivity of a model of the bone screwing process. The accuracy with which values of the Young modulus (E) of the bone were identified depended on the value of E, with larger values being less accurately identified. The error in identified (Tensile strength) values was less than 0.5 % over all the cases tested, with no discernible dependence on the co-identified values of E. Experimental validation is still required for the model and identification process, but this approach is feasible and promising from a theoretical perspective.
Based on a model of three coupled oscillators describing the influence of respiration, namely respiratory sinus arrhythmia (RSA), and so-called Mayer waves on the heart rate, an unscented Kalman filter (UKF) is designed to perform sensor fusion of multimodal cardiorespiratory sensor signals. The aim is to implicitly use redundancy between the sensor signals to improve the estimated heart rate while utilising model knowledge. The effectiveness of the approach is shown by estimations of the heart rate on synthesised data as well as patient data from the Fantasia dataset and a Sleep laboratory which provide two, three or six sensor channels for resting individuals. It could be shown that the approach is able to fuse multimodal sensor signals on signal level to achieve more accurate estimations. For real data, errors in mean heart rate as small as 1.56 % were achieved.