This study utilizes a psychophysical approach to examine the effects on
carrying capacity for bi-manual carrying tasks involving different handle positions and
carrying ranges. A total of 16 female subjects participated in the experiment in groups of
two people, and each group of subjects performed the tasks in a random order with 12
different combinations of carrying task. The independent variables are handle position
(upper, middle, lower) and carrying range (F–F: floor height carried to floor height, F–W:
floor height carried to waist height, W–W: waist height carried to waist height, W–F:
waist height carried to floor height), the dependent variable is the maximum acceptable
carried weight (MAWC), heart rate (HR), and the rating of perceived exertion (RPE). The
results show that the handle position has a significant effect on MAWC and overall RPE but
no significant effect on HR. Carrying range has a significant effect on the MAWC and HR,
but no significant effect on overall HR. The handle position and carrying range have a
significant interaction on the MAWC and HR. The RPE for different body parts shows
significant differences, and the hands feel the most tired. Overall, this study confirms
that the lower handle position with the W–W carrying range is the best combination for a
two-person carrying task. 相似文献
In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.
