Classification of asymptomatic and osteoarthritic knee gait patterns using gait analysis via deterministic learning

Assessment of human gait after total knee arthroplasty by dynamic time warping algorithm

Today, the elderly population is increasing, and there are many drawbacks for them, especially defects in their knee joints which lead to improper gait. To solve this problem, their knee joint can be replaced with knee arthroplasty. In this letter, level of improvement in the human gait before and after total knee arthroplasty (TKA) surgery is investigated using the dynamic time warping (DTW) algorithm. For this purpose, several volunteers who have problems with their knees are incorporated in a test before and after TKA surgery. Then, the data of gait analysis is collected and the data is compared with a reference using the DTW algorithm. The outcome results illustrate an improvement of 89%-97% by the proposed algorithm after TKA surgery. Therefore, patients can see improvement with high accuracy and very fast that result in more use this technique in TKR surgery.

Healthy Knee Kinematic Phenotypes Identification Based on a Clustering Data Analysis

The purpose of this study is to identify healthy phenotypes in knee kinematics based on clustering data analysis. Our analysis uses the 3D knee kinematics curves, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation, measured via a KneeKG™ system during a gait task. We investigated two data representation approaches that are based on the joint analysis of the three dimensions. The first is a global approach that is considered a concatenation of the kinematic data without any dimensionality reduction. The second is a local approach that is considered a set of 69 biomechanical parameters of interest extracted from the 3D kinematic curves. The data representations are followed by a clustering process, based on the BIRCH (balanced iterative reducing and clustering using hierarchies) discriminant model, to separate 3D knee kinematics into homogeneous groups or clusters. Phenotypes were obtained by averaging those groups. We validated the clusters using inter-cluster correlation and statistical hypothesis tests. The simulation results showed that the global approach is more efficient, and it allows the identification of three descriptive 3D kinematic phenotypes within a healthy knee population.

Gait acquisition and analysis system for osteoarthritis based on hybrid prediction model

Osteoarthritis (OA) is the most common type of joint-related diseases, which affects millions of people worldwide. Expensive and time-consuming medical imaging can provide precise structural description of knee joints, but lacks the functional descriptions. Gait analysis can provide functional descriptions of knee joints. However, orthopedic surgeons always observe the patient's gait qualitatively and perform subjective assessments through rating scales at present due to the lack of a quantitative gait analysis system. To solve these problems, a gait acquisition and analysis system is developed to provide a cheap, easy-to-use solution for quantitative recording and functional description of OA patients. Firstly, an automatic gait acquisition platform is designed for the clinical setting based on the RGB-D camera and the developed software of gait data recording. In addition, the effective working space of gait acquisition platform is evaluated for clinical applications by comparing with the ground-truth from infrared optical trackers. Secondly, the acquired gait data is analyzed with a novel hybrid prediction model to assess the gait anomalies quantitatively and objectively. In the hybrid model, the extracted features of gait data contain the manually-extracted features and the automatically-extracted features from Long Short-Term Memory network. Experimental results on real patients demonstrate that the proposed gait analysis system can quantitatively predict gait anomalies with a high accuracy of 98.77 %. Therefore, this gait acquisition and analysis system achieves quantitative recording and objective assessment of gait anomalies for clinical OA treatments.

Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals

The analysis of knee kinematic data, which come in the form of a small sample of discrete curves that describe repeated measurements of the temporal variation of each of the knee three fundamental angles of rotation during a subject walking cycle, can inform knee pathology classification because, in general, different pathologies have different kinematic data patterns. However, high data dimensionality and the scarcity of reference data, which characterize this type of application, challenge classification and make it prone to error, a problem Duda and Hart refer to as the curse of dimensionality. The purpose of this study is to investigate a sample-based classifier which evaluates data proximity by the two-sample Hotelling T 2 statistic. This classifier uses the whole sample of an individual’s measurements for a better support to classification, and the Hotelling T 2 hypothesis testing made applicable by dimensionality reduction. This method was able to discriminate between femero-rotulian (FR) and femero-tibial (FT) knee osteoarthritis kinematic data with an accuracy of 88.1 % , outperforming significantly current state-of-the-art methods which addressed similar problems. Extended to the much harder three-class problem involving pathology categories FR and FT, as well as category FR-FT which represents the incidence of both diseases FR and FT in a same individual, the scheme was able to reach a performance that justifies its further use and investigation in this and other similar applications.