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Background: Musculoskeletal disorders (MSDs) are among the most prevalent pains which could be ignited via material handling. Backpack wearers are among those who may carry heavy loads. Since MSDs mainly originate in school-aged children, students (as the largest group of backpack wearers) are considered more important. The purpose of this study is to design a backpack based on minimization of shearing force. Methods: At first the relevant patents were investigated at national and international levels. Then the appropriate ones selected for optimizing. The main selection criterion was based on reduction of pressure on body. As an extra function we used a generator to produce electricity. The criterion for electricity generator selection was: 1) small dimension, 2) low weight, and 3) safe voltage. Results: A suspending system used to eliminate shearing forces. Since a wearing backpack student is under the shearing force caused by walking, a spring system was used as a reducing device for shearing force. Physical spring calculation revealed that springs with stiffness coefficient equal to 588 was appropriate. Conclusion: In comparison with existing designs, suggested backpack design can reduce forces on upper limb during handling the backpack.

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

This study investigates the impact of increasing backpack load on the gait of adolescents during stair descent. Sixteen healthy male students (age = 12.9 ± 0.6 years) were required to descend the stairs in 4 loaded conditions. The kinematic, kinetic, and EMG data were collected synchronously and gait parameters, especially indicators of balance control, were analyzed. The posterior tilt angles (COM-COP IA in the sagittal plane) (0 %-42 %, 48 %-53 %, 58 %-91 %, p < 0.01), trunk anterior tilt angles (9–33 %, 51–65 %, p < 0.01), and CV of stride length (p < 0.01) increased with the backpack load. The COM-Step edge separation decreased with the increased backload (p < 0.01). In addition, the hip flexion torque (25–40 %, 45–51 %, p < 0.01), the rectus femoris activation, and the hip stiffness increased significantly as the load up to 15 % Body Weight (BW)and 20 % BW. The increasing backpack load may affect adolescent’s stair descent gait. Especially as the load was up to 15 % BW, the adolescents’ bodies tended to tilt backwards relative to the support foot during the single stance phase. They may activate the hip flexors and tilt forward the trunk to recover from the balance perturbation, which was associated with increased hip flexion torques. This adjustment was more pronounced with the increasing backpack load. However, excessive forward flexion may increase the risk of forward falls. The boundaries of adjustment need further research in the future. Findings from this study provide baseline information on the intrinsic mechanisms of balance control during stair descent.

Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually.

LiDAR technology is rapidly evolving as various new systems emerge, providing unprecedented data to characterize forest vertical structure. Data from different LiDAR systems present distinct characteristics owing to a combined effect of sensor specifications, data acquisition strategies, as well as forest conditions such as tree density and canopy cover. Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions, and thus is of crucial importance. This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios. The results suggest that the proximal Backpack LiDAR can provide the finest level of information, followed by UAV LiDAR, Geiger-mode LiDAR, and linear LiDAR. The emerging Geiger-mode LiDAR can capture a significantly higher level of detail while operating at a higher altitude as compared to the traditional linear LiDAR. The results also show: (1) canopy cover percentage has a critical impact on the ability of aerial and terrestrial systems to acquire information corresponding to the lower and upper portions of the tree canopy, respectively; (2) all the systems can obtain adequate ground points for digital terrain model generation irrespective of canopy cover conditions; and (3) point clouds from different systems are in agreement within a ±3 cm and ±7 cm range along the vertical and planimetric directions, respectively.

Many people need to carry heavy loads in a backpack to perform occupational, military, or recreational tasks. Suspended-load backpacks have been shown to reduce dynamic peak forces acting on the body and lower an individual’s metabolic cost during walking. However, little is known about the physiological and biomechanical effects of a suspended-load backpack on the human musculoskeletal system. The goal of this study was to determine the impact of different types of backpacks on metabolic cost, joint kinetics, gait kinematics, and muscle activity while individuals carried the same load of 15 kg at a walking speed of 5 km/h and running speed of 7 km/h on an instrumented treadmill. A group of six healthy participants participated in experiments in which two different backpacks were worn under three different conditions: suspended-load backpack working condition (SLB_ON), suspended-load backpack locking condition (SLB_OFF), and ordinary backpack condition (ORB). The results showed that carrying the backpack in the SLB_ON condition can reduce lower limb muscle activities and biological joint work while decreasing the metabolic cost by 15.25 ± 4.21% and 8.81 ± 2.46% during walking and 12.53 ± 2.39% and 6.99 ± 2.37% during running compared to carrying the backpack in the SLB_OFF and the ORB conditions, respectively. However, the SLB_ON condition may cause increased shoulder strain and dynamic stability and balance problems. These results suggest that the control of load movement in a suspended-load backpack should be considered when locomotion performance is optimized in future studies.

Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.