Background: Human locomotion involves the coordinated activation of a finite set of modules, known as muscle synergy, which represent the motor control strategy of the central nervous system. However, most prior studies have focused on isolated muscle activation, overlooking the modular organization of motor behavior. Therefore, to enhance comprehension of muscle coordination dynamics during multi-joint movements in chronic ankle instability (CAI), exploring muscle synergies during landing in CAI patients is imperative. Methods: A total of 22 patients with unilateral CAI and 22 healthy participants were recruited for this research. We employed a recursive model for second-order differential equations to process electromyographic (EMG) data after filtering preprocessing, generating the muscle activation matrix, which was subsequently inputted into the non-negative matrix factorization model for extraction of the muscle synergy. Muscle synergies were classified utilizing the K-means clustering algorithm and Pearson correlation coefficients. Statistical parameter mapping (SPM) was employed for temporal modular parameter analyses. Results: Four muscle synergies were identified in both the CAI and healthy groups. In Synergy 1, only the gluteus maximus showed significantly higher relative weight in CAI compared to healthy controls (p = 0.0035). Synergy 2 showed significantly higher relative weights for the vastus lateralis in the healthy group compared to CAI (p = 0.018), while in Synergy 4, CAI demonstrated significantly higher relative weights of the vastus lateralis compared to healthy controls (p = 0.030). Furthermore, in Synergy 2, the CAI group exhibited higher weights of the tibialis anterior compared to the healthy group (p = 0.042). Conclusions: The study suggested that patients with CAI exhibit a comparable modular organizational framework to the healthy group. Investigation of amplitude adjustments within the synergy spatial module shed light on the adaptive strategies employed by the tibialis anterior and gluteus maximus muscles to optimize control strategies during landing in patients with CAI. Variances in the muscle-specific weights of the vastus lateralis across movement modules reveal novel biomechanical adaptations in CAI, offering valuable insights for refining rehabilitation protocols.

The running biomechanics of unstable shoes have been well investigated, however, little is known about how traditional neutral shoes in combination with unstable design elements and scientifically (bionic) designed shoes influence prolonged running biomechanics. The purpose of this study was to investigate biomechanical changes for a typical 5 km run and how footwear technology may affect outcomes. Sixteen healthy male recreational heel strike runners participated in this study, and completed two prolonged running sessions (neutral shoe session and bionic shoe session), with 7 to 10 days interval between sessions. A two-way repeated-measures analysis of variance (ANOVA, shoe × time) was conducted to determine any differences in joint biomechanics. Main effects for shoe type were observed at the ankle, knee and hip joints during the stance phase. In particular, decreased range of motion (ROM) was observed using the bionic shoes for all three joints, and the joint moments also had significant changes except for the frontal plane of the hip. Main effects for time were also observed at the ankle, knee and hip joints. The ROM of the sagittal plane in the knee and hip decreased post-5 km running. The reduction of ankle dorsiflexion, hip flexion, hip adduction and hip internal rotation angles were observed post-5 km running, as well as the increase of ankle eversion and external rotation, knee adduction and internal rotation angles. The kinetics also exhibited significant differences between pre-5 km running and post-5 km running. The interaction effects only existed in the ROM of the hip sagittal plane, hip adduction angle and hip internal rotation angle. The results suggested that bionic shoes could be beneficial for strengthening muscle control, enhancing postural stability and proprioceptive ability. Footwear personalization could be a solution that benefits runners, reduces injury risk and improves running performance.

The original idea for bionic shoes (BSs) involves combining the function of unstable foot conditions and the structure of the human plantar. The purpose of this study was to investigate the differences between the normal shoes (NS) and the BS during the stance phases of walking and running. A total of 15 Chinese males from Ningbo University were recruited for this study (age: 24.3 ± 2.01 years; height: 176.25 ± 7.11 cm, body weight (BW): 75.75 ± 8.35 kg). The participants were asked to perform a walking and running task. Statistical parametric mapping (SPM) analysis was used to investigate any differences between NSs and BSs during the walking and running stance phases. The results demonstrated that there were significant differences found (21.23–28.24%, p = 0.040; 84.47–100%, p = 0.017) in hip extension and flexion between the NS and the BS during the walking stance phase. There were no significant differences found in ankle and moment during the running stance phase. Significant differences were found in the rectus femoris (5.29–6.21%; p = 0.047), tibialis anterior (14.37–16.40%; p = 0.038), and medial gastrocnemius (25.55–46.86%; p < 0.001) between the NS and the BS during the walking stance phase. Significant differences were found in rectus femoris (12.83–13.10%, p = 0.049; 15.89–80.19%, p < 0.001), tibialis anterior (15.85–18.31%, p = 0.039; 21.14–24.71%, p = 0.030), medial gastrocnemius (80.70–90.44%; p = 0.007), and lateral gastrocnemius (11.16–27.93%, p < 0.001; 62.20–65.63%, p = 0.032; 77.56–93.45%, p < 0.001) between the NS and the BS during the running stance phase. These findings indicate that BSs are more efficient for muscle control than unstable shoes and maybe suitable for rehabilitation training.

Running-related injuries are common among runners. Recent studies in footwear have shown that designs of shoes can potentially affect sports performance and risk of injury. Bionic shoes combine the functions of barefoot running and foot protection and incorporate traditional unstable structures based on bionic science. The purpose of this study was to investigate ground reaction force (GRF) differences for a 5 km run and how bionic shoes affect GRFs. Sixteen male recreational runners volunteered to participate in this study and finished two 5 km running sessions (a neutral shoe session and a bionic shoe session). Two-way repeated-measures ANOVAs were performed to determine the differences in GRFs. In the analysis of the footwear conditions of runners, bionic shoes showed significant decreases in vertical impulse, peak propulsive force, propulsive impulse, and contact time, while the braking impulse and vertical instantaneous loading rate (VILR) increased significantly compared to the neutral shoes. Main effects for a 5 km run were also observed at vertical GRFs and anterior–posterior GRFs. The increases of peak vertical impact force, vertical average loading rate (VALR), VILR, peak braking force and braking impulse were observed in post-5 km running trials and a reduction in peak propulsive force and propulsive impulse. The interaction effects existed in VILR and contact time. The results suggest that bionic shoes may benefit runners with decreasing injury risk during running. The findings of the present study may help to understand the effects of footwear design during prolonged running, thereby providing valuable information for reducing the risk of running injuries.

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.

The stop-jumping task is one of the most important technical actions in basketball. A previous study showed 70% probability of non-contact ACL injuries during stop-jumping tasks. Therefore, the present study aimed to investigate the differences in lower extremity biomechanical changes between the rear foot as the initial contact area to terminate the jump (SJR) and the fore foot as the initial contact area to also terminate the jump (SJF) during the horizontal landing during a stop-jumping phase. In total, 25 male amateur Ningbo University basketball athletes from China were recruited for this study. The participants were asked to jump vertically by using two different stop-jumping strategies. Kinematic and kinetics data were amassed during a stop-jumping task. Statistical parametric mapping (SPM) analysis was used to find the differences between SJR and SJF. Our results indicated that the change of different ankle range of motion caused significantly different values for knee angle (p < 0.001), velocity (p = 0.003) (p = 0.023) (p < 0.001), moment (p = 0.04) (p < 0.001), (p = 0.036) and power (p = 0.015) (p < 0.001) during the stop-jumping phase and the horizontal landing phase. The same biomechanical parameters of the hip joint were also significantly different for hip angle (p < 0.001), moment (p = 0.012) (p < 0.001) (p < 0.001), and power (p = 0.01) (p < 0.001) (p < 0.001). These findings indicate that altering the primary contact at the ankle angle might effectively reduce the risk of a knee injury.