No abstract

Robotic-assisted orthopaedic procedures demand accurate spatial joint measurements. Tracking of human joint motion is challenging in many applications, such as in sport motion analyses. In orthopaedic surgery, these challenges are even more prevalent, where small errors may cause iatrogenic damage in patients-highlighting the need for robust and precise joint and instrument tracking methods. In this study, we present a novel kinematic modelling approach to track any anatomical points on the femur and / or tibia by exploiting optical tracking measurements combined with a priori computed tomography information. The framework supports simultaneous tracking of anatomical positions, from which we calculate the pose of the leg (joint angles and translations of both the hip and knee joints) and of each of the surgical instruments. Experimental validation on cadaveric data shows that our method is capable of measuring these anatomical regions with sub-millimetre accuracy, with a maximum joint angle uncertainty of ±0.47 o. This study is a fundamental step in robotic orthopaedic research, which can be used as a ground-truth for future research such as automating leg manipulation in orthopaedic procedures.

We report a study that developed algorithms to measure the dimension and uncertainty range of free space inside the knee joint for the purpose of minimally invasive surgery. During knee arthroscopy, the patient's leg position is continuously adjusted to create the space for surgical instruments inside the joint. Surgeons 'feel' the force they apply to the leg and estimate the joint space from a 2D video. In many cases, they overestimate the instrument gap, resulting in damaging to the knee joint by pushing instruments through a gap that is too small. We used cadaveric experiments to inform the noise induced by the sensors and image processing steps, to derive an error point-cloud in a simulated environment. From the point-cloud, we calculate the instrument gap range inside the knee joint. For a selected surgical instrument gap size, the measurement algorithm is accurate to less than a millimetre. However, measurement errors introduce an uncertainty of 14%. The performance of our algorithms demonstrates the use of a single-lens arthroscope to measure the instrument gap to provide feedback to a surgeon or enable control of a robotic leg manipulator.