standing and sitting

站立和坐着
  • 文章类型: Journal Article
    未经批准:脊柱骨盆运动,人体矢状平衡的基石,在患者特异性全髋关节置换术中至关重要。
    UNASSIGNED:这项研究旨在开发一种新颖的模型,该模型使用反向传播神经网络(BPNN)来预测坐下时的骨盆变化,基于站立的侧面脊柱骨盆X光片。
    未经评估:年轻的健康志愿者被纳入研究,取18个脊髓骨盆参数,如骨盆发病率(PI)等。首先,通过Pearson相关性确定与坐位骨盆倾斜(PT)和骶骨倾斜(SS)相关的站立参数。然后,用这些参数作为输入,用PT和SS作为输出,建立了BPNN预测网络。最后,用相对误差(RE)评估预测结果,预测精度(PA),和归一化均方根误差(NRMSE)。
    UNASSIGNED:该研究包括145名23.1±2.3岁的志愿者(M:F=51:94)。Pearson分析显示,坐姿PT与6次站立测量相关,坐姿SS与5次相关。最佳BPNN模型预测PT和SS的准确率分别为78.48%和77.54%,分别;至于PI,骨盆形态的常数,是95.99%。
    未经批准:在这项研究中,BPNN模型在预测坐姿脊柱骨盆参数方面产生了理想的准确性,它提供了新的见解和工具,用于表征整个运动周期中的脊柱骨盆变化。
    UNASSIGNED: Spinopelvic motion, the cornerstone of the sagittal balance of the human body, is pivotal in patient-specific total hip arthroplasty.
    UNASSIGNED: This study aims to develop a novel model using back propagation neural network (BPNN) to predict pelvic changes when one sits down, based on standing lateral spinopelvic radiographs.
    UNASSIGNED: Young healthy volunteers were included in the study, 18 spinopelvic parameters were taken, such as pelvic incidence (PI) and so on. First, standing parameters correlated with sitting pelvic tilt (PT) and sacral slope (SS) were identified via Pearson correlation. Then, with these parameters as inputs and sitting PT and SS as outputs, the BPNN prediction network was established. Finally, the prediction results were evaluated by relative error (RE), prediction accuracy (PA), and normalized root mean squared error (NRMSE).
    UNASSIGNED: The study included 145 volunteers of 23.1 ± 2.3 years old (M:F = 51:94). Pearson analysis revealed sitting PT was correlated with six standing measurements and sitting SS with five. The best BPNN model achieved 78.48% and 77.54% accuracy in predicting PT and SS, respectively; As for PI, a constant for pelvic morphology, it was 95.99%.
    UNASSIGNED: In this study, the BPNN model yielded desirable accuracy in predicting sitting spinopelvic parameters, which provides new insights and tools for characterizing spinopelvic changes throughout the motion cycle.
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  • 文章类型: Journal Article
    先前的调查主要是在实验室进行的,以检查智能手机使用对颈部和头部位置的影响,这些结果是否适用于实际情况仍是未知的。这项实地调查分析了颈部屈曲(NF),头部屈曲(HF),注视角度(GA),和智能手机用户在台北公共区域的观看距离(VD),台湾。600名智能手机用户(300名男性和300名女性)被拍到站立时的速腾照片,支持坐,或使用智能手机时不支持的坐姿。结果显示,女性的NF和HF明显少于男性使用者,而VD则较短。不管性别,站立时的NF高于坐着。女性在坐着支持和不支持时患有类似的NF和HF,但两者都明显低于站立时的水平。相比之下,男性使用者在无支撑坐位时的NF和HF高于有支撑坐位.NF(45°-50°)远大于推荐的最大安全NF15°。由于VD较短,女性可能有更高的视觉疲劳风险。
    Prior investigations have been primarily conducted in a laboratory to examine the effects of the smartphone use on the neck and head positions, whether these results are applicable to actual conditions is still unknown. This field survey thus analyzed the neck flexion (NF), head flexion (HF), gaze angle (GA), and viewing distance (VD) of smartphone users in public areas in Taipei, Taiwan. Six hundred smartphone users (300 men and 300 women) were photographed sagittally in standing, supported sitting, or unsupported sitting postures while using a smartphone. Results showed that women had significantly less NF and HF and shorter VDs than male users. Regardless of gender, higher NF was observed for standing than for sitting. Women had similar NF and HF while sitting supported and unsupported, but both were significantly lower than those while standing. By contrast, male users had higher NF and HF during unsupported sitting than during supported sitting. The NF (45°-50°) was much greater than the recommended maximum safe NF of 15°. Women may be at higher risk of visual strain because of shorter VD.
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