Search Results (2,563)

Exercise is a front-line intervention to increase functional capacity and reduce pain and disability in people with low strength levels or disorders. However, there is a lack of validated field-based tests to check the initial status and, more importantly, to control the process and make tailored adjustments in load, intensity, and recovery. We aimed to determine the test–retest reliability of a submaximal, resistance-band test to evaluate the strength of the trunk stability muscles using a portable force sensor in middle-aged adults (48 ± 13 years) with medically diagnosed chronic low back pain and healthy peers (n = 35). Participants completed two submaximal progressive tests of two resistance-band exercises (unilateral row and Pallof press), consisting of 5 s maintained contraction, progressively increasing the load. The test stopped when deviation from the initial position by compensation movements occurred. Trunk muscle strength (CORE muscles) was monitored in real time using a portable force sensor (strain gauge). Results revealed that both tests were highly reliable (intra-class correlation [ICC] > 0.901) and presented low errors and coefficients of variation (CV) in both groups. In particular, people with low back pain had errors of 14–19 N (CV = 9–12%) in the unilateral row test and 13–19 N (CV = 8–12%) in the Pallof press. No discomfort or pain was reported during or after the tests. These two easy-to-use and technology-based tests result in a reliable and objective screening tool to evaluate the strength and trunk stability in middle-aged adults with chronic low back pain, considering an error of measurement < 20 N. This contribution may have an impact on improving the individualization and control of rehabilitation or physical training in people with lumbar injuries or disorders. Full article

Lane detection is a crucial visual perception task in the field of autonomous driving, serving as one of the core modules in advanced driver assistance systems (ADASs).To address the insufficient real-time performance of current segmentation-based models and the conflict between the demand for high inference speed and the excessive parameters in resource-constrained edge devices (such as onboard hardware, mobile terminals, etc.) in complex real-world scenarios, this paper proposes an efficient and lightweight auxiliary branch network (CBGA-Auxiliary) to tackle these issues. Firstly, to enhance the model’s capability to extract feature information in complex scenarios, a row anchor-based feature extraction method based on global features was adopted. Secondly, employing ResNet as the backbone network and CBGA (Conv-Bn-GELU-SE Attention) as the fundamental module, we formed the auxiliary segmentation network, significantly enhancing the segmentation training speed of the model. Additionally, we replaced the standard convolutions in the branch network with lightweight GhostConv convolutions. This reduced the parameters and computational complexity while maintaining accuracy. Finally, an additional enhanced structural loss function was introduced to compensate for the structural defect loss issue inherent in the row anchor-based method, further improving the detection accuracy. The model underwent extensive experimentation on the Tusimple dataset and the CULane dataset, which encompass various road scenarios. The experimental results indicate that the model achieved the highest F1 scores of 96.1% and 71.0% on the Tusimple and CULane datasets, respectively. At a resolution of 288 × 800, the ResNet18 and ResNet34 models achieved maximum inference speeds of 410FPS and 280FPS, respectively. Compared to existing SOTA models, it demonstrates a significant advantage in terms of inference speed. The model achieved a good balance between accuracy and inference speed, making it suitable for deployment on edge devices and validates the effectiveness of the model. Full article

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize–soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent (BWE) biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The N₀ parameter exhibited a linear relationship with BWE, suggesting that a straightforward vegetation correction factor may be suitable (f_b). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m⁻² (or mm of water) increase in BWE. This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community. Full article