针对老龄人口创建基于力肌图的智能家居控制器的潜力研究

M. L. Delva, Maram Sakr, Rana Sadeghi Chegani, Mahta Khoshnam, C. Menon
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引用次数: 10

摘要

力肌图(FMG)量化肢体肌肉收缩时的体积变化,可用于设计方便、低成本的界面,以辅助日常生活活动(ADL)。这项研究的目的是评估老年人是否可以有效地使用基于fmg的腕带与他们的环境互动。为此,设计了一种由力敏电阻阵列组成的FMG带。10名参与者被分为“高级”和“非高级”两组,并被要求在戴着设计的腕带的情况下执行控制手势和不受约束的ADL任务。为了评估手环的可用性,对手势的正确识别和反应时间进行了记录。结果表明,在在线测试中,老年人能够在显示指令的1.4秒内成功完成控制手势。在这种情况下,个体训练的手势识别算法的准确率达到了76.5%。非老年人的反应时间为0.9 s,总体分类准确率为91.2%。这项初步研究证明了利用fmg为基础的技术在日常生活活动中为老年人提供帮助的潜力和可行性。
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Investigation into the Potential to Create a Force Myography-based Smart-home Controller for Aging Populations
Force Myography (FMG) quantifies the volumetric changes in a limb occurring with muscle contraction and can potentially be used to design convenient, low-cost interfaces to assist in activities of daily living (ADL). The aim of this study is to evaluate whether elders can effectively use an FMG-based wrist band to interact with their environment. In this regard, an FMG band consisted of an array of force-sensing resistors (FSRs) was designed. Ten participants were grouped in two classes, namely “senior” and “non-senior”, and were instructed to perform control gestures and unconstrained ADL tasks while wearing the designed wrist band. To evaluate the usability of the band, correct identification of hand gestures and reaction times were noted. Results showed that seniors were capable of successfully performing a control gesture within 1.4 s of displaying the instruction during online testing. The individually-trained gesture identification algorithm achieved an accuracy of 76.5% in this case. Non-seniors had a reaction time of 0.9 s with an overall classification accuracy of 91.2%. This preliminary study demonstrates the potential and feasibility of utilizing FMG-based technology to provide elders with assistance during activities of daily living.
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