Phase-Plane Based Model-Free Estimation of Steady-State Metabolic Cost
Phase-Plane Based Model-Free Estimation of Steady-State Metabolic Cost
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DOI：
10.1109/access.2022.3205629
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发表时间：
2022-01-01
期刊：
影响因子：
3.9
通讯作者：
Kim, Myunghee
中科院分区：
文献类型：
--
作者：
Kantharaju, Prakyath;Kim, Myunghee
稳态代谢成本已被用于评估可穿戴机器人设备的性能。最近，实时评估的代谢成本已被采用的目标函数时，优化机器人参数为个人用户，从而个性化的援助，以尽量减少人力。然而，基于模型的代谢成本估计方法所需的长估计时间限制了仅对光强度活动的优化。在这里，我们假设无模型相平面估计（PPE）将减少稳态代谢成本的估计时间。首先，我们开发了一个相平面表示来分类稳态（其中代谢率的变化为零）和瞬态代谢动力学。其次，我们使用数据驱动的高斯混合模型和实时呼吸测量来近似瞬态代谢动力学。我们通过检查（1）膝下截肢（BKA）和非膝下截肢（BKA）个体的机器人假脚辅助行走性能和（2）非BKA个体的蹲下和跑步性能，比较了PPE与基于模型的方法的性能。PPE减少了稳态估计时间在步行过程中的个人和没有BKA的31%和40%，分别。它还减少了56%和24%的蹲下和运行条件下的估计时间。这些估计时间的显着减少表明，数据驱动的PPE方法可以用于在个性化可穿戴机器人的帮助时快速估计体力。这扩展了对从事体力密集型活动的受试者或体力降低的个体进行个体优化的能力。
The steady-state metabolic cost has been used to assess the performance of wearable robotic devices. Recently, real-time assessment of this metabolic cost has been employed in an objective function when optimizing robotic parameters for an individual user, thus personalizing the assistance to minimize human effort. However, the long estimation time needed for the model-based approach to metabolic cost estimation limits the optimization only to light-intensity activities. Here, we hypothesized that model-free, phase-plane estimation (PPE) would reduce the estimation time for the steady-state metabolic cost. First, we developed a phase-plane representation to classify the steady-state (where the change in metabolic rate is zero) and the transient metabolic dynamics. Second, we approximated the transient metabolic dynamics using a data-driven Gaussian mixture model and a real-time respiratory measure. We compared the performance of PPE with that of the model-based method by examining (1) walking performance assisted by a robotic prosthetic foot for individuals with and without Below Knee Amputation (BKA) and (2) squatting and running performance for individuals without BKA. PPE reduced the steady-state estimation time during walking for individuals with and without BKA by 31% and 40%, respectively. It also reduced estimation time by 56% and 24% for squatting and running conditions. These significant reductions in estimation time suggest that the data-driven PPE method can be used to rapidly estimate physical effort when personalizing the assistance from wearable robots. This expands the ability to conduct individual optimization for subjects engaged in physically intensive activities or for individuals with reduced physical strength.