Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.

A simple analytical approach using conditions of conservation of the mass is proposed for the 1-D “negative squeeze” lubrication problem in order to calculate the cavitation boundary position during oscillatory motion of two plates. The same geometrical case is analyzed using Bonneau’s finite element code. Good agreements between analytical and numerical results support validation of Bonneau’s algorithm. As an example of application of this algorithm to squeeze motion case, the EHD lubrication of an elastic connecting-rod small end bearing is analyzed. Influences of the shaft elasticity and lubricant piezoviscosity are presented. [S0742-4787(00)02101-9]

The structures and dielectric properties of La _1.6 Sr _0.4 Ni _1−x Al _x O ₄ ( x = 0, 0.2 and 0.4) ceramics elaborated using the Pechini method were studied for the first time.