This paper presents the development of a wearable accelerometry system for real-time gait cycle parameter recognition. Using a tri-axial accelerometer, the wearable motion detector is a single waist-mounted device to measure trunk accelerations during walking. Several gait cycle parameters, including cadence, step regularity, stride regularity and step symmetry can be estimated in real-time by using autocorrelation procedure. For validation purposes, five Parkinson’s disease (PD) patients and five young healthy adults were recruited in an experiment. The gait cycle parameters among the two subject groups of different mobility can be quantified and distinguished by the system. Practical considerations and limitations for implementing the autocorrelation procedure in such a real-time system are also discussed. This study can be extended to the future attempts in real-time detection of disabling gaits, such as festinating or freezing of gait in PD patients. Ambulatory rehabilitation, gait assessment and personal telecare for people with gait disorders are also possible applications.

Background: This study was designed to derive the theoretical formulae to predict the pullout strength of pedicle screws with an inconstant outer and/or inner diameter distribution (conical screws). For the transpedicular fixation, one of the failure modes is the screw loosening from the vertebral bone. Hence, various kinds of pedicle screws have been evaluated to measure the pullout strength using synthetic and cadaveric bone as specimens. In the literature, the Chapman's formula has been widely proposed to predict the pullout strength of screws with constant outer and inner diameters (cylindrical screws). Methods: This study formulated the pullout strength of the conical and cylindrical screws as the functions of material, screw, and surgery factors. The predicted pullout strength of each screw was compared to the experimentally measured data. Synthetic bones were used to standardize the material properties of the specimen and provide observation of the loosening mechanism of the bone/screw construct. Results: The predicted data from the new formulae were better correlated with the mean pullout strength of both the cylindrical and conical screws within an average error of 5.0% and R ² = 0.93. On the other hand, the average error and R ² value of the literature formula were as high as -32.3% and -0.26, respectively. Conclusion: The pullout strength of the pedicle screws was the functions of bone strength, screw design, and pilot hole. The close correlation between the measured and predicted pullout strength validated the value of the new formulae, so as avoid repeating experimental tests.

Evaluations are vital to quantify the functionalities of athletic footwear, such as the performance of slip resistance, shock absorption, and rebound. Computational technology has progressed to become a promising solution for accelerating product development time and providing customized products in order to keep up with the competitive contemporary footwear market. In this research, the effects of various tread pattern designs on traction performance in a normal gait were analyzed by employing an approach that integrated computational simulation and gait analysis. A state-of-the-art finite element (FE) model of a shoe was developed by digital sculpting technology. A dynamic plantar pressure distribution was automatically applied to interpret individualized subject conditions. The traction performance and real contact area between the shoe and the ground during the gait could be characterized and predicted. The results suggest that the real contact area and the structure of the outsole tread design influence the traction performance of the shoe in dry conditions. This computational process is more efficient than mechanical tests in terms of both cost and time, and it could bring a noticeable benefit to the footwear industry in the early design phases of product development.