Traditional methods for collecting ground reaction forces (GRFs) mainly use lab force plates. Previous research broke this pattern by predicting GRFs with deep learning and data from IMUs like joint acceleration. Joint angle, as a geometric, is easier to collect than acceleration outdoors with cameras. LSTM is one of the deep learning models that have shown good performance in biomechanical studies. xLSTM, as an optimized version of LSTM, has not been used in biomechanical studies and no research has predicted GRFs during running solely using lower limb joint angles. This study collected lower-limb joint angle and vertical ground reaction force data at five speeds from 12 healthy male runners with Xsens sensors. Datasets including three joints and three planes were set as the inputs of four deep learning models for vertical-GRF prediction. CNN-xLSTM consistently performed best in the four deep learning models when different datasets were input (R² = 0.909 ± 0.064, MAPE = 2.18 ± 0.09, rMSE = 0.061 ± 0.008), and the performance was at a relatively high level at the five speeds. The current findings may contribute to a new GRF measurement and provide a reference for future real-time motion detection and sport injury prediction.

Ankle sprains are a common injury among athletes and the general population, with chronic ankle instability (CAI) being a frequent complication. CAI patients often display altered neuromuscular control adaptations. This study analyzed muscle synergy patterns in 20 CAI patients during anticipated and unanticipated landing tasks to understand their neuromuscular adaptation strategies. Using Nesterov non-negative matrix factorization and K-means clustering, the study identified distinct muscle activation patterns. Results indicated that during unanticipated landings, the gluteus maximus and vastus lateralis showed increased activation weight, while the medial gastrocnemius was more active in anticipated landings. This study highlights that CAI patients display unique muscle synergy patterns during unanticipated landings, relying more on proximal muscles such as the gluteus maximus and vastus lateralis. This adaptation reflects the proximal muscle strategy to enhance stability and compensate for impaired ankle function in unpredictable situations.