Abstract
Hip arthroplasty is commonly used as the final treatment approach for patients experiencing end-stage osteoarthritis. The number of these patients needing this treatment is expected to grow significantly by year 2030 to more than 572000 patients [Kurtz et al., 2007]. One of the important outcomes of hip arthroplasty is to improve patients' functions postoperatively. The evaluation of walking can provide a wealth of information regarding the efficiency of this treatment in improving a patient's mobility. Assessing the kinematic features of gait collected with a motion capture system combined with the aid of a motor-driven treadmill provides the advantage of enabling the evaluator to collect precise information about a large number of strides in a short period of time. Body segment kinematics (i.e. joint motion) are most often represented in the form of time series data with the abscissa (X axis) representing time and the ordinate (y axis) representing the motion of a particular joint. Although a great deal of information can be gained from the analyses of time series data, non-linear analyses tools can provide an additional and important dimension to a clinician's assessment of gait recovery. In this study eight patients (4 females, mean age 64.9, SD 11.1) have currently been assessed after unilateral hip arthroplasty. All surgeries were conducted by direct anterior approach by using two different approaches; three of the patients were treated by bone preservation technique and received Minihip short stem implant (Corin Ltd., Cirencester, UK) and five were treated by using a press fit stem implant Accolade II (Stryker, Mahwah, NJ USA). Patients performed a single three-minute trial of walking on a motor-driven treadmill at a self-selected pace. Using a 12 camera system, bilateral lower limb joint motion was collected prior to the surgery, at three and six weeks and at three and 6 months after the surgery. Depending upon the patient's preferred walking pace; between 40 and 45 strides were collected during each trial. Kinematic data obtained from force plates embedded in the treadmill were used to identify the heel strike and toe off events for each stride. After time normalizing the each of the joint angles (i.e. hip, knee, ankle) for each stride to 100 data points the data were then amplitude normalized to the initial point of the pre-surgery data. The non-linear tools of angle-angle and phase plane were used to explore relationships that are not readily apparent with linear wave form analyses. Angle-angle diagrams between a variety of joints angles both within a single limb or bilaterally enabled us to explore segmental coordination patterns and how they changed over the six months after surgery. Phase plane analyses included comparing joint motion relative to the velocity of that motion. This technique provided insights into the nature of the control of the joint. The additional information that results from the use of non-linear analyses provides an additional dimension of that can aide the clinician in understanding the recovery curve. This additional insight can be used to guide therapeutic decision making.
