Pattern recognition and direct control home use of a multi-articulating hand prosthesis

A. M. Simon, Kristi L. Turner, L. Miller, L. Hargrove, T. Kuiken
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引用次数: 18

Abstract

Although more multi-articulating hand prostheses have become commercially available, replacing a missing hand remains challenging from a control perspective. This study investigated myoelectric direct control and pattern recognition home use of a multi-articulating hand prosthesis for individuals with a transradial amputation. Four participants were fitted with an i-limb Ultra Revolution hand and a Coapt COMPLETE CONTROL system. An occupational therapist provided training for each control style and how to use the various grips. The number of grips available to each individual was determined by clinician and user feedback to optimize both the number of grips available and the reliability of grip selection. Home trial data corresponding to individual usage were recorded. No significant differences were found between direct and pattern recognition control home trials in regards to trial length (p=0.96), days powered on (p=0.21), or total time powered on (p=0.91). There was a higher average number of configured grips for direct control at 4.8 [0.5] compared to 3.8 [0.5] for pattern recognition control, but this difference did not reach significance (p=0.092). Across all hand close movements, users spent a majority of time $(\gt80$%) in one grip when using direct control. For pattern recognition usage was spread across more grips $(\gt45$% time in one grip, 25% time in a 2nd grip, and 20% time in a 3rd grip). Pattern recognition control may provide users with a more intuitive way to select and use the various grips available to them.
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模式识别和直接控制家庭使用的多关节手假体
虽然更多的多关节假肢已经商业化,但从控制的角度来看,替换缺失的手仍然是一个挑战。本研究探讨了肌电直接控制和模式识别的家庭使用多关节手假体的个人经桡骨截肢。四名参与者配备了一个i-limb Ultra Revolution手和一个Coapt COMPLETE CONTROL系统。一名职业治疗师为每种控制方式以及如何使用各种握把提供了培训。每个个体可使用的握把数量由临床医生和使用者反馈决定,以优化可用握把数量和握把选择的可靠性。记录个人使用情况对应的家庭试用数据。直接和模式识别对照家庭试验在试验长度(p=0.96)、开机天数(p=0.21)或总开机时间(p=0.91)方面无显著差异。直接控制的平均配置握把数为4.8[0.5],高于模式识别控制的3.8[0.5],但这种差异没有达到显著性(p=0.092)。在所有的手部接近动作中,当使用直接控制时,用户花在一个握把上的时间最多。对于模式识别的使用分布在更多的握把上$(第一个握把占用$ 45$%的时间,第二个握把占用$ 25%的时间,第三个握把占用$ 20%的时间)。模式识别控制可以为用户提供一种更直观的方式来选择和使用各种握把。
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