The effectiveness of home rehabilitation based on BPMpathway exercise rehabilitation system for patients after total knee arthroplasty in China.

Background: There is significant interest in the use of web-based technologies for rehabilitation of patients after total knee arthroplasty (TKA). BPMpathway is a combination of a wireless BPMpro sensor and mobile app to provide a personalized post-operative support programme for TKA patients.

Objective: To investigate the impact of the BPMpathway exercise rehabilitation system on home rehabilitation for TKA patients.

Analysis of Bone Loss around Cemented and Biologic Prostheses after Hemiarthroplasty.

Objective: To perform hemiarthroplasty (HA) on elderly patients with femoral neck fractures using cemented and biologic prostheses and then compare the bone loss around the two types of prostheses after surgery.

Methods: A total of 60 patients aged over 75 years (with a mean age of 83.5 years) and suffering from femoral neck fracture (Garden types III and IV) from January 2018 to December 2020 were selected; they were randomly divided into group A ( = 30, cemented prostheses) and group B ( = 30, biologic prostheses) and received HA.

Subspace Clustering via Structured Sparse Relation Representation.

Due to the corruptions or noises that existed in real-world data sets, the affinity graphs constructed by the classical spectral clustering-based subspace clustering algorithms may not be able to reveal the intrinsic subspace structures of data sets faithfully. In this article, we reconsidered the data reconstruction problem in spectral clustering-based algorithms and proposed the idea of "relation reconstruction." We pointed out that a data sample could be represented by the neighborhood relation computed between its neighbors and itself.

Improved linear classifier model with Nyström.

Most data sets consist of interlaced-distributed samples from multiple classes and since these samples always cannot be classified correctly by a linear hyperplane, so we name them nonlinearly separable data sets and corresponding classifiers are named nonlinear classifiers. Traditional nonlinear classifiers adopt kernel functions to generate kernel matrices and then get optimal classifier parameters with the solution of these matrices. But computing and storing kernel matrices brings high computational and space complexities.