The aim of this study was to compare the musculature activity and kinematics of knee and hip joints during front and back squat with maximal loading. Two-dimensional kinematical data were collected and electromyographic activities of vastus lateralis, vastus medialis, rectus femoris, semitendinosus, biceps femoris, gluteus maximus and erector spinae were measured while participants (n = 12, 21.2 ± 1.9 years old) were completing front and back squat exercises with maximum loading. Paired sample t-test was used for comparisons between two techniques. Results showed that the electromyographic activity of vastus medialis was found to be greater in the front squat compared to the back squat during the ascending phase (P < 0.05, d = 0.62; 95% CI, -15.0/-4.17) and the whole manoeuvre (P < 0.05, d = 0.41; 95% CI, -12.8/-0.43), while semitendinosus (P < 0.05, d = -0.79; 95% CI, 0.62/20.59) electromyographic activity was greater in the back squat during the ascending phase. Compared to the front squat version, back squat exhibited significantly greater trunk lean, with no differences occurring in the knee joint kinematics throughout the movement. Results may suggest that the front squat may be preferred to the back squat for knee extensor development and for preventing possible lumbar injuries during maximum loading.

The aim of this study was to investigate the possible kinematic and muscular activity changes with maximal loading during squat maneuver. Fourteen healthy male individuals, who were experienced at performing squats, participated in this study. Each subject performed squats with 80%, 90%, and 100% of the previously established 1 repetition maximum (1RM). Electromyographic (EMG) activities were measured for the vastus lateralis, vastus medialis, rectus femoris, semitendinosus, biceps femoris, gluteus maximus, and erector spinae by using an 8-channel dual-mode portable EMG and physiological signal data acquisition system (Myomonitor IV, Delsys Inc., Boston, MA, USA). Kinematical data were analyzed by using saSuite 2D kinematical analysis program. Data were analyzed with repeated measures analysis of variance (p < 0.05). Overall muscle activities increased with increasing loads, but significant increases were seen only for vastus medialis and gluteus maximus during 90% and 100% of 1RM compared to 80% while there was no significant difference between 90% and 100% for any muscle. The movement pattern in the hip joint changed with an increase in forward lean during maximal loading. Results may suggest that maximal loading during squat may not be necessary for focusing on knee extensor improvement and may increase the lumbar injury risk.

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Precision agriculture uses precise sensor data collected throughout farmland to give farmers better insight into their land, allowing for greater crop yields and reduced resource usage. However, existing solutions require high hardware costs thus limiting large scale deployments. To address that, we propose a low-cost and scalable solution for sensing physical attributes of soil using IoT based WiFi sensing devices. By understanding variations in WiFi radio signals with channel state information (CSI) and machine learning models, we evaluate the proposed soil sensing system through experiments on physical soil traits such as soil moisture content, soil texture and position. Moreover, we also demonstrate how a mesh network of WiFi sensing devices allows us to predict the physical traits of the soil in the area between each pair of sensors, allowing for an increase in sensing area coverage as nodes are added.

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