| 引用本文: | 张焕明, 胡成雨, 朱家明.基于神经网络及特征运算的老年人平衡能力分析(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2020，37（4）：1-8 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 针对老年人跌倒现象，提出一定的改善建议;使用多层感知器神经网络对影响老人平衡的身体因素进行权重分析，基于前向传播原理推测出老人的摔倒概率；以标准老人各项实验指标为基准，建立平衡比对模板，测算出度量空间中标准老人和样本老人身体特征点的位移-时刻函数关系，并用MATLAB软件将其绘制成图，通过比较找出影响老人身体平衡的部位所在，针对不同的部位提出改善建议；结果表明：76位样本老人中超过半数的老人身体平衡能力较弱，具有较高的摔倒风险，且异常观测点均涉及肩关节、后脑和脊柱等部位，所得结果对于预防老人摔倒具有一定的参考价值。 |
| 关键词: MLP神经网络 平衡比对模板 预测 影响因素 |
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| Analysis of the Balance Ability of the Elderly Based on Neural Network and characteristic Operation |
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ZHANG Huan-ming,HU Cheng-yu,ZHU Jia-ming
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School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Anhui Bengbu 233030, China
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| Abstract: |
| According to the fall phenomenon of the elderly, this paper makes suggestion for improvement. The multi layer perceptron neural network is used to conduct weight analysis of the physical factors affecting the balance of the elderly body, and the probability for the fall of the elderly is predicted based on the forward propagation principle. By taking all experimental indicators of standard old people as the basis, balance comparison template is set up to calculate displacement time function relation of body feature points of standard old people and sample old people in metric space, and MATLAB software is used to draw a graph of it. By comparison, the positions affecting the balance of the elderly are found, and the suggestions for their improvement according to different positions are put forward. Results show that the accurate rate of the prediction by neural network reaches 80 percent, that more than half of the elderly in 76 samples has weak balance ability and has high risk of the fall, and that the observation point for abnormal positions of the elderly involves in their shoulder joint, back brain, spine and so on. The results obtained are of certain reference value to preventing the fall of the elderly. |
| Key words: MLP neural network balance comparison template prediction influencing factor |
