Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance.
Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance.
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DOI：
10.3390/s23031670
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发表时间：
2023-02-03
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影响因子：
--
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中科院分区：
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在可穿戴机器人领域，辅助需要个性化，以使用户受益最大化。从亮度模式（b模式）超声自动记录的肌束信息已用于设计辅助轮廓，该轮廓与年轻人估计的肌肉力量成正比。人们也希望为那些可能有年龄变化的生理机能的老年人开发类似的策略。本研究介绍并验证了一种用于提取年轻人和老年人神经束长度的ResNet + 2x-LSTM模型。青年人（40,696帧）和老年人（34,262帧）以半手工方式生成标签，描绘内侧腓肠肌的b模式成像。首先，对模型进行训练，并对年轻人（R2 = 0.85, RMSE = 2.36±1.51 mm, MAPE = 3.6%, aaDF = 0.48±1.1 mm）和老年人（R2 = 0.53, RMSE = 4.7±2.51 mm, MAPE = 5.19%, aaDF = 1.9±1.39 mm）进行测试。然后，对不同年龄的狗进行性能训练（R2 = 0.79, RMSE = 3.95±2.51 mm, MAPE = 4.5%, aaDF = 0.67±1.8 mm）。虽然与年轻人群相比，年龄相关的肌肉损失影响了跟踪方法的误差，但单个肌束的绝对百分比误差导致3-5%的小变化，这表明在生成辅助力剖面时，误差是可以接受的。
In the field of wearable robotics, assistance needs to be individualized for the user to maximize benefit. Information from muscle fascicles automatically recorded from brightness mode (B-mode) ultrasound has been used to design assistance profiles that are proportional to the estimated muscle force of young individuals. There is also a desire to develop similar strategies for older adults who may have age-altered physiology. This study introduces and validates a ResNet + 2x-LSTM model for extracting fascicle lengths in young and older adults. The labeling was generated in a semimanual manner for young (40,696 frames) and older adults (34,262 frames) depicting B-mode imaging of the medial gastrocnemius. First, the model was trained on young and tested on both young (R2 = 0.85, RMSE = 2.36 ± 1.51 mm, MAPE = 3.6%, aaDF = 0.48 ± 1.1 mm) and older adults (R2 = 0.53, RMSE = 4.7 ± 2.51 mm, MAPE = 5.19%, aaDF = 1.9 ± 1.39 mm). Then, the performances were trained across all ages (R2 = 0.79, RMSE = 3.95 ± 2.51 mm, MAPE = 4.5%, aaDF = 0.67 ± 1.8 mm). Although age-related muscle loss affects the error of the tracking methodology compared to the young population, the absolute percentage error for individual fascicles leads to a small variation of 3–5%, suggesting that the error may be acceptable in the generation of assistive force profiles.
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影响因子：
3.9
作者：
Katakis, Sofoklis;Barotsis, Nikolaos;Kakotaritis, Alexandros;Economou, George;Panagiotopoulos, Elias;Panayiotakis, George
通讯作者：
Panayiotakis, George
影响因子：
25
作者：
通讯作者：
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影响因子：
2.2
作者：
Miyoshi, Tasuku;Kihara, Tomohiko;Komeda, Takashi
通讯作者：
Komeda, Takashi
影响因子：
2.9
作者：
Gionfrida, Letizia;Rusli, Wan M. R.;Bharath, Anil A.
通讯作者：
Bharath, Anil A.
影响因子：
3.9
作者：
Hagoort I;Hortobágyi T;Vuillerme N;Lamoth CJC;Murgia A
通讯作者：
Murgia A