In sports science, the automation of performance analysis and assessment is urgently required to increase the evaluation accuracy and decrease the performance analysis time of a subject. Existing methods of performance analysis and assessment are either performed manually based on human experts’ opinions or using motion analysis software, i.e., biomechanical analysis software, to assess only one side of a subject. Therefore, we propose an automated system for performance analysis and assessment that can be used for any human movement. The performance of any skill can be described by a curve depicting the joint angle over the time required to perform a skill. In this study, we focus on only 14 body joints, and each joint comprises three angles. The proposed system comprises three main stages. In the first stage, data are obtained using motion capture inertial measurement unit sensors from top professional fighters/players while they are performing a certain skill. In the second stage, the collected sensor data obtained are input to the biomechanical software to extract the player’s joint angle curve. Finally, each joint angle curve is processed using a continuous wavelet transform to extract the main curve points (i.e., peaks and valleys). Finally, after extracting the joint curves from several top players, we summarize the players’ curves based on five statistical indicators, i.e., the minimum, maximum, mean, and mean ± standard deviation. These five summarized curves are regarded as standard performance curves for the joint angle. When a player’s joint curve is surrounded by the five summarized curves, the performance is considered acceptable. Otherwise, the performance is considered unsatisfactory. The proposed system is evaluated based on four different karate skills. The results of the proposed system are identical to the decisions of the expert panels and are thus suitable for real‐time decisions.

The thermal issues generated from friction are the key obstacle in the high‐performance machining of titanium alloys. The friction between the workpiece being cut and the cutting tool is the dominant parameter that affects the heat generation during the machining processes, i.e., the temperature inside the cutting zone and the consumed cutting energy. Besides, the complexity is associated with the nature of the friction phenomenon. However, there are limited efforts to forecast the friction coefficient during the machining operations. In this work, the friction coefficients between the titanium alloy against zirconia ceramics lubricated by minimum quantity lubrication were recorded and measured using a universal mechanical tester pin‐on‐disc tribometer. Then, we proposed two models for forecasting the friction coefficient which are trained and tested on the recorded data. The two predictive models are based on autoregressive integrated moving average and gated recurrent unit deep neural network methods. The proposed models are evaluated through a set of exhaustive experiments. These experiments demonstrated that the proposed models can efficiently be used to reduce power consumption dedicated to monitoring the friction coefficients. Besides, they can reduce or avoid surface thermal damage by predicting the high level of friction coefficients in advance, which can be used as an alert to enable or readjust the lubrication parameters (fluid pressure, fluid flow rate, etc.) to maintain lower ranges of friction coefficients and power consumption.