In a clinical setting, there is a need for simple gait kinematic measurements to facilitate objective unobtrusive patient monitoring. The objective of this study is to determine if a learned classification model's output can be used to monitor a person's recovery status post-TKA. The gait kinematics of 20 asymptomatic and 17 people with TKA were measured using a full-body Xsens model1. The experimental group was measured at 6 weeks, 3, 6, and 12 months post-surgery. Joint angles of the ankle, knee, hip, and spine per stride (10 strides) were extracted from the Xsens software (MVN Awinda studio 4.4)1. Statistical features for each subject at each evaluation moment were derived from the kinematic time-series data. We normalised the features using standard scaling2. We trained a logistic regression (LR) model using L1-regularisation on the 6 weeks post-surgery data2–4. After training, we applied the trained LR- model to the normalised features computed for the subsequent timepoints. The model returns a score between 0 (100% confident the person is an asymptomatic control) and 1 (100% confident this person is a patient). The decision boundary is set at 0.5. The classification accuracy of our LR-model was 94.58%. Our population's probability of belonging to the patient class decreases over time. At 12 months post-TKA, 38% of our patients were classified as asymptomatic.
