首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury
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Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury

机译:验证使用智能手机内置加速度计以估算脊髓损伤的全日制手动轮椅用户的有效能源支出

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摘要

This study aimed to investigate the validity of using built-in smartphone accelerometers to estimate the active energy expenditures of full-time manual wheelchair users with spinal cord injury (SCI). Twenty participants with complete SCI completed 10 5-min daily activities that involved the upper limbs, during which their oxygen consumption and upper limb activity were registered using a portable gas analyzer and a smartphone (placed on the non-dominant arm), respectively. Time series of 1-min averaged oxygen consumption and 55 accelerometer variables (13 variables for each of the four axes and three additional variables for the correlations between axes) were used to estimate three multiple linear models, using a 10-fold cross-validation method. The results showed that models that included either all variables and models or that only included the linear variables showed comparable performance, with a correlation of 0.72. Slightly worse general performance was demonstrated by the model that only included non-linear variables, although it proved to be more accurate at estimating the energy expenditures (EE) during specific tasks. These results suggest that smartphones could be a promising low-cost alternative to laboratory-grade accelerometers to estimate the energy expenditure of wheelchair users with spinal cord injury during daily activities.
机译:本研究旨在调查使用内置智能手机加速度计的有效性,以估算具有脊髓损伤(SCI)的全日制手动轮椅使用者的活动能源支出。完整SCI的二十名参与者完成了10 5分钟的日常活动,其中涉及上肢,在此期间,使用便携式天然气分析仪和智能手机(放置在非显着臂上)注册其耗氧和上肢活动。使用10倍的交叉验证方法,使用10分钟平均氧气消耗和55个加速度计变量(用于四个轴中的每一个的额外变量以及三个额外变量的13个变量)来估计三个多线性模型,使用10倍的交叉验证方法来估算三个多线性模型。结果表明,包括所有变量和模型的模型或仅包括线性变量的模型显示出相当的性能,相关性为0.72。稍微糟糕的一般性能是由仅包括非线性变量的模型证明了,尽管在特定任务期间估计能量支出(EE)估计能量支出(EE)更准确。这些结果表明,智能手机可能是实验室级加速度计的有希望的低成本替代方案,以估算日常活动期间脊髓损伤的轮椅使用者的能源支出。

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