Neurodegenerative diseases damage neuromuscular tissues and deteriorate motor neurons which affects the motor capacity of the patient. Particularly the walking gait is greatly influenced by the deterioration process. Early detection of anomalous gait patterns caused by neurodegenerative diseases can help the patient to prevent associated risks. Previous studies in this domain relied on either features extracted from gait parameters or the Ground Reaction Force (GRF) signal. In this work, we aim to combine both GRF signals and extracted features to provide a better analysis of walking gait patterns. For this, we designed NDDNet, a novel neural network architecture to process both of these data simultaneously to detect 3 different Neurodegenerative Diseases (NDDs). We have done several experiments on the data collected from 64 participants and got 96.75% accuracy on average in detecting 3 types of NDDs. The proposed method might provide a way to get the most out of the data in hand while working with GRF signals and help diagnose patients with an anomalous gait more effectively.

In this paper, a surface acoustic wave (SAW) sensor for hip implant geometry was proposed for the application of total hip replacement. A two-port SAW device was numerically investigated for implementation with an operating frequency of 872 MHz that can be used in more common radio frequency interrogator units. A finite element analysis of the device was developed for a lithium niobate (LiNBO3) substrate with a Rayleigh velocity of 3488 m/s on COMSOL Multiphysics. The Multiphysics loading and frequency results highlighted a good uniformity with numerical results. Afterwards, a hip implant geometry was developed. The SAW sensor was mounted at two locations on the implant corresponding to two regions along the shaft of the femur bone. Three discrete conditions were studied for the feasibility of the implant with upper- and lower-body loading. The loading simulations highlighted that the stresses experienced do not exceed the yield strengths. The voltage output results indicated that the SAW sensor can be implanted in the hip implant for hip implant-loosening detection applications.

Total hip replacement (THR) is a common orthopedic surgery technique that helps thousands of individuals to live normal lives each year. A hip replacement replaces the shattered cartilage and bone with an implant. Most hip implants fail after 10–15 years. The material selection for the total hip implant systems is a major research field since it affects the mechanical and clinical performance of it. Stress shielding due to excessive contact stress, implant dislocation due to a large deformation, aseptic implant loosening due to the particle propagation of wear debris, decreased bone remodeling density due to the stress shielding, and adverse tissue responses due to material wear debris all contribute to the failure of hip implants. Recent research shows that pre-clinical computational finite element analysis (FEA) can be used to estimate four mechanical performance parameters of hip implants which are connected with distinct biomaterials: von Mises stress and deformation, micromotion, wear estimates, and implant fatigue. In vitro, in vivo, and clinical stages are utilized to determine the hip implant biocompatibility and the unfavorable local tissue reactions to different biomaterials during the implementation phase. This research summarizes and analyses the performance of the different biomaterials that are employed in total hip implant systems in the pre-clinical stage using FEA, as well as their performances in in vitro, in vivo, and in clinical studies, which will help researchers in gaining a better understanding of the prospects and challenges in this field.