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From biomechanical point of view, strike pattern plays an important role in preventing potential injury risk in running. Traditionally, strike pattern determination was conducted by using 3D motion analysis system with cameras. However, the procedure is costly and not convenient. With the rapid development of technology, sensors have been applied in sport science field lately. Therefore, this study was designed to determine the algorithm that can identify landing strategies with a wearable sensor. Six healthy male participants were recruited to perform heel and forefoot strike strategies at 7, 10, and 13 km/h speeds. The kinematic data were collected by Vicon 3D motion analysis system and 2 inertial measurement units (IMU) attached on the dorsal side of both shoes. The data of each foot strike were gathered for pitch angle and strike index analysis. Comparing the strike index from IMU with the pitch angle from Vicon system, our results showed that both signals exhibited highly correlated changes between different strike patterns in the sagittal plane (r=0.98). Based on the findings, the IMU sensors showed potential capabilities and could be extended beyond the context of sport science to other fields, including clinical applications.

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Reducing the thickness of the midsole layer rather than other components may enable shoe minimalism in manufacture to be more efficiently realized. The effect of midsole thickness on the biomechanical characteristics of lunges is limited. The current study investigated the effect of midsole thickness on temporal-spatial movement characteristics, ground reaction force variables and frontal foot and ankle kinematics during lunge maneuvers performed in a controlled laboratory setting. Sixteen badminton athletes were recruited to perform lunge footwork while wearing shoes with three midsole thicknesses. A force plate embedded in the floor was used to collect ground reaction force data. A kinematic analysis was conducted using a multisegment foot model. Temporal-spatial parameters, ground reaction force variables and frontal foot and ankle kinematics were calculated. Comparisons of the calculated variables among shoes with three midsole thicknesses were compared using a three-by-one repeated-measure analysis of variance ( = 0.05). Elite badminton players maintained similar temporal-spatial characteristics while wearing shoes with midsoles of various thicknesses (P>0.05). The peaks of the initial impact, early loading, and later propulsive forces did not vary among the midsole thickness (P>0.05). Greater pronation at the midtarsal joint during lunges performed while wearing shoes with thick midsoles (P<0.05) may be associated with a greater risk of adverse stress on soft tissue because of overuse. The kinematic evidence suggested that foot support can be enhanced by thickening the midsole. Furthermore, sport shoe models with thicker midsoles should feature an antipronation design to minimize the risk of injury from overuse.

Previous gait phase detection as convolutional neural network (CNN) based classification task requires cumbersome manual setting of time delay or heavy overlapped sliding windows to accurately classify each phase under different test cases, which is not suitable for streaming Inertial-Measurement-Unit (IMU) sensor data and fails to adapt to different scenarios. This paper presents a segmentation based gait phase detection with only a single six-axis IMU sensor, which can easily adapt to both walking and running at various speeds. The proposed segmentation uses CNN with gait phase aware receptive field setting and IMU oriented processing order, which can fit to high sampling rate of IMU up to 1000Hz for high accuracy and low sampling rate down to 20Hz for real time calculation. The proposed model on the 20Hz sampling rate data can achieve average error of 8.86 ms in swing time, 9.12 ms in stance time and 96.44% accuracy of gait phase detection and 99.97% accuracy of stride detection. Its real-time implementation on mobile phone only takes 36 ms for 1 second length of sensor data.

The purpose of this study was to investigate the effects of the 8-week dynamic moment of inertia (DMOI) bat training on swing velocity, batted-ball speed, hitting distance, muscle power, and grip force. The DMOI bat is characterized in that the bat could be swung more easily by reducing the moment of inertia at the initial stage of swing without decreasing the bat weight and has a faster swing velocity and lower muscle activity. Seventeen varsity baseball players were randomly assigned to the DMOI bat training group (n = 9) and the normal bat training group (n = 8). The training protocol was 7 swings each set, 5-8 sets each time, 3 times each week, and 8 weeks' training period. The results showed that the swing training with the DMOI bat for 8 weeks significantly increased swing velocity by about 6.20% (96.86 ± 8.48 vs. 102.82 ± 9.93 km·h(-1)), hitting distance by about 6.69% (80.06 ± 9.16 vs. 84.99 ± 7.26 m), muscle power of the right arm by about 12.04% (3.34 ± 0.41 vs. 3.74 ± 0.61 m), and muscle power of the left arm by about 8.23% (3.36 ± 0.46 vs. 3.61 ± 0.39 m) (p < 0.05). Furthermore, the DMOI bat training group had a significantly better change percentage in swing velocity, hitting distance, and grip force of the left hand than did the normal bat training group (p < 0.05). The findings suggested that the swing training with the DMOI bat has a positive benefit on swing performance and that the DMOI bat could be used as a new training tool in baseball.

This study quantified the strength of the relationship between the percentage of heart rate reserve (%HRR) and two acceleration-based intensity metrics (AIMs) at three sensor-positions during three sport types (running, basketball, and badminton) under three intensity conditions (locomotion speeds). Fourteen participants (age: 24.9 ± 2.4 years) wore a chest strap HR monitor and placed three accelerometers at the left wrist (non-dominant), trunk, and right shank, respectively. The %HRR and two different AIMs (Player Load per minute [PL/min] and mean amplitude deviation [MAD]) during exercise were calculated. During running, both AIMs at the shank and PL at the wrist had strong correlations (r = 0.777–0.778) with %HRR; while other combinations were negligible to moderate (r = 0.065–0.451). For basketball, both AIMs at the shank had stronger correlations (r = 0.604–0.628) with %HRR than at wrist (r = 0.536–0.603) and trunk (r = 0.403–0.463) with %HRR. During badminton exercise, both AIMs at shank had stronger correlations (r = 0.782–0.793) with %HRR than those at wrist (r = 0.587–0.621) and MAD at trunk (r = 0.608) and trunk (r = 0.314). Wearing the sensor on the shank is an ideal position for both AIMs to monitor external intensity in running, basketball, and badminton, while the wrist and using PL-derived AIM seems to be the second ideal combination.