The purpose of the present study was to examine the visual perception system in expert climbers through a psychophysical optical test in a cross-sectional study. Twenty-seven male participants with an International Rock Climbing Research Association (IRCRA) best on-sight lead skill level ranging between 18 and 27 and a best red-point level ranging between 18 and 29 completed a series of psychophysical optic tests assessing their visual field, visual acuity, and contrast sensitivity. Climbers were divided by their best red-pointed lead level, and, following IRCRA recommendations, two groups were created: an advanced group (IRCRA redpoint level between 18 and 23), and an elite-high elite group (IRCRA redpoint level between 24 and 29). The elite group presented more training days per week (5.25 ± 1.28), best on-sighted lead level (24.63 ± 1.92 IRCRA), and best red-pointed lead level (26.63 ± 2.56 IRCRA) than the advanced group (3.67 ± 0.91 training days per week, 19.50 ± 1.04 IRCRA on-sighted level and 20.67 ± 1.57 IRCRA red-pointed level). Better visual perception outputs were produced by the group of elite climbers in visual field tests; no differences were observed between the two groups for visual acuity and contrast sensitivity tests. Overall, findings indicate that best climbers performed better at the visual perception tasks that tested their visual field. Such better perception from best climbers is discussed given (1) the greater time they spend coercing the visual system during practicing climbing and (2) the specific complexity of the stimuli as they are confronted to harder routes where holds are less perceptible and the time to find best hold sequences is constrained.

Forklifts are commonly used in industrial supply chains to transport heavy loads. Forklift drivers have the risk of developing musculoskeletal discomfort derived from the movement pattern required at work. This research aimed to investigate the spinal range of motion (ROM) and musculoskeletal discomfort of forklift drivers and compare it with a control group. Forklift drivers (39 males) and office workers (31 males) were recruited to assess cervical, thoracic, and lumbar ROM with an electronic double inclinometer. Additionally, musculoskeletal discomfort was registered with the Cornell Discomfort Musculoskeletal Questionnaire. Forklift drivers showed a higher cervical discomfort and ROM of lateral lumbar bending than office workers. Both groups reported lower ROM in cervical and lumbar lateral bending on the right side versus the left side. No differences of asymmetry were reported for any variable between groups. Specific exercise programs may correct these mobility imbalances.

The aim of this current work is to identify three different gears of cross-country skiing utilizing embedded inertial measurement units and a suitable deep learning model. The cross-country style studied was the skating style during the uphill, which involved three different gears: symmetric gear pushing with poles on both sides (G3) and two asymmetric gears pushing with poles on the right side (G2R) or to the left side (G2L). To monitor the technique, inertial measurement units (IMUs) were affixed to the skis, recording acceleration and Euler angle data during the uphill tests performed by two experienced skiers using the gears under study. The initiation and termination points of the tests were controlled via Bluetooth by a smartphone using a custom application developed with Android Studio. Data were collected on the smartphone and stored on the SD memory cards included in each IMU. Convolutional neural networks combined with long short-term memory were utilized to classify and extract spatio-temporal features. The performance of the model in cross-user evaluations demonstrated an overall accuracy of 90%, and it achieved an accuracy of 98% in the cross-scene evaluations for individual users. These results indicate a promising performance of the developed system in distinguishing between different ski gears within skating styles, providing a valuable tool to enhance ski training and analysis.

The advent of portable power meters has revolutionized training in cycling, allowing an accurate field-based assessment of athletes. In a similar way, researchers have recently developed low-cost gait analysis equipment to assess running power in a more natural environment. The purpose of this study was to evaluate the absolute reliability of two different power meters and the agreement between these two wearable devices (i.e., Stryd™ and RunScribe™) for measuring power during treadmill running. About 49 endurance runners performed a running protocol on a treadmill at self-selected comfortable velocities. Power output (W) was measured using the Stryd™ and RunScribe™ systems, which were attached to the same shoe. The absolute reliability, based on coefficient of variation, was 0.32 ± 0.29% for Stryd™ and 1.68 ± 1.49% for RunScribe™, while the standard error of the mean were 0.3 ± 0.2 W and 2.6 ± 2.5 W for Stryd™ and RunScribe™, respectively. Data from both devices showed significant correlations ( r = 0.783, p < 0.001) and the ICC ( r = 0.855) reported an almost perfect reliability. Bland–Altman plots revealed no heteroscedasticity of error ( r² = 0.030), although a moderate systematic bias (−12.3 ± 26.6 W), and wide limits of agreement (39.8–64.3 W) were found. Considering the increased popularity of using power meter devices in running, scientists, coaches, athletes, and general users should be aware that data from these devices are reliable, but not interchangeable, due to the variation shown for running power output data.

This work presents a comparative study of low cost and low invasiveness sensors (plantar pressure and inertial measurement units) for classifying cross-country skiing techniques. A dataset was created for symmetrical comparative analysis, with data collected from skiers using instrumented insoles that measured plantar pressure, foot angles, and acceleration. A deep learning model based on CNN and LSTM was trained on various sensor combinations, ranging from two specific pressure sensors to a full multisensory array per foot incorporating 4 pressure sensors and an inertial measurement unit with accelerometer, magnetometer, and gyroscope. Results demonstrate an encouraging performance with plantar pressure sensors and classification accuracy closer to inertial sensing. The proposed approach achieves a global average accuracy of 94% to 99% with a minimal sensor setup, highlighting its potential for low-cost and precise technique classification in cross-country skiing and future applications in sports performance analysis.