Search Results (3)

This study investigated kinetic and physiological load characteristics of Self-Manual Resistance Training (SMRT) lat pulldown. SMRT lat pulldown is a training method in which practitioner generates resistance manually using their own muscular force by gripping a towel with both hands and pulling it outward in a horizontal direction. We analyzed shoulder and elbow joint moments in frontal plane (2D) and muscle activity levels of latissimus dorsi (LD), posterior deltoid (PD), biceps brachii (BB), and triceps brachii (TB) during 10 maximal-effort repetitions of SMRT lat pulldown in 11 resistance-trained men. For comparison, we also measured muscle activity levels during a machine lat pulldown for 10 reps at 75% 1 RM load in same participants. Peak shoulder adduction and elbow extension moments during SMRT lat pulldown were both approximately 70% MVC. Mean rectified EMG of LD was significantly greater during machine lat pulldown than SMRT lat pulldown, whereas that of PD was significantly greater during SMRT than machine version. Mean rectified EMG of TB was high during SMRT, and that of BB was high in machine version. SMRT lat pulldown appears to produce relatively large shoulder adduction and elbow extension moments, increasing PD and TB activation and limiting LD activation. Full article

The detection of change in remote-sensing images is broadly applicable to many fields. In recent years, both supervised and unsupervised methods have demonstrated excellent capacity to detect changes in high-resolution images. However, most of these methods are sensitive to noise, and their performance significantly deteriorates when dealing with remote-sensing images that have been contaminated by mixed random noises. Moreover, supervised methods require that samples are manually labeled for training, which is time-consuming and labor-intensive. This study proposes a new unsupervised change-detection (CD) framework that is resilient to mixed random noise called self-supervised denoising network-based unsupervised change-detection coupling FCM_SICM and EMD (SSDNet-FSE). It consists of two components, namely a denoising module and a CD module. The proposed method first utilizes a self-supervised denoising network with real 3D weight attention mechanisms to reconstruct noisy images. Then, a noise-resistant fuzzy C-means clustering algorithm (FCM_SICM) is used to decompose the mixed pixels of reconstructed images into multiple signal classes by exploiting local spatial information, spectral information, and membership linkage. Next, the noise-resistant Earth mover’s distance (EMD) is used to calculate the distance between signal-class centers and the corresponding fuzzy memberships of bitemporal pixels and generate a map of the magnitude of change. Finally, automatic thresholding is undertaken to binarize the change-magnitude map into the final CD map. The results of experiments conducted on five public datasets prove the superior noise-resistant performance of the proposed method over six state-of-the-art CD competitors and confirm its effectiveness and potential for practical application. Full article

During the basketball training for beginner children, sensors are needed to count the number of times the basketball hits the target area in a certain period of time to evaluate the training effect. This study proposes a self-powered basketball training sensor, based on a triboelectric nanogenerator. The designed sensor with a rectangular floor shape will output a pulse signal with the same frequency as the basketball impact to achieve the measurement function through the mutual contact of the internal copper (Cu) and polytetrafluoroethylene (PTFE). Test results show that the working frequency of the sensor is 0 to 5 Hz, the working environment temperature should be less than 75 °C, the working environment humidity should be less than 95%, and which has high reliability. Further tests show that the maximum output voltage, current, and power of the sensor can reach about 52 V, 4 uA, and 26.5 uW with a 10 MΩ resistance in series, respectively, and the output power can light up 12 light-emitting diode (LED) lights in real-time. Compared with the traditional statistical method of manual observation, the sensor can automatically count data in a self-powered manner, and also can light up the LED lights in real-time as an indicator of whether the basketball impacts the target area, to remind beginner children in real-time. Full article