Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai 519087, China.
CNR IPCF, Bari Division c/o Dipartimento di Chimica, Universita di Bari, Via Orabona 4, I-70126 Bari, Italy.
Sensors (Basel). 2022 Jul 22;22(15):5469. doi: 10.3390/s22155469.
This study aims to fabricate smart insoles using wireless Flexi force and bend sensing technology. Polyvinyl chloride (PVC) film was chosen as the substrate to hold all the sensors. The developed smart insole has a three-layer structure (insole-PVC layer-fabric layer) and is calibrated in an isolation laboratory to evaluate its measurement performance. One male volunteer subject exhibited four different body postures, namely tree pose, forward-leaning, squatting, and forward folding pose. Changes in pressure distribution were considered to be similar for the forward, squat, and forward-folded positions. When subjects performed a full squat, the flex sensor exhibited maximum flexion during the squat position, and the flex sensor response against the squat pose was found to be higher by about 18.18% than in the forward lean, respectively. The tree pose has the highest error rate at the first metatarsal, about 18.6%, of which the maximum absolute relative error of the sensor is less than 5%. Plantar pressure distribution and body posture measurements were successfully validated using Flexi force and flex sensors embedded in the smart insole. The smart insole proposed in this research work has broader prospects for clinical application.
本研究旨在利用无线 Flexi 力和弯曲感应技术制造智能鞋垫。聚氯乙烯 (PVC) 薄膜被选为承载所有传感器的基底。所开发的智能鞋垫具有三层结构（鞋垫-PVC 层-织物层），并在隔离实验室中进行校准，以评估其测量性能。一名男性志愿者展示了四种不同的身体姿势，分别是树式、前屈式、深蹲式和前折叠式。压力分布的变化被认为在向前、深蹲和前折叠位置是相似的。当受试者进行全深蹲时，在深蹲位置 Flex 传感器表现出最大的弯曲，并且 Flex 传感器对深蹲姿势的响应比前屈姿势高约 18.18%。树式在第一跖骨处的错误率最高，约为 18.6%，其中传感器的最大绝对相对误差小于 5%。通过嵌入智能鞋垫中的 Flexi 力和 Flex 传感器成功验证了足底压力分布和身体姿势测量。本研究工作中提出的智能鞋垫在临床应用方面具有更广阔的前景。
