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Abstract：
This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer\'s gait and diagnose balance issues. This approach can be utilized to improve a user\'s rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques.
摘要：
本文提出了一种使用低成本FSR传感器预测地面反作用力（GRF）和压力中心（CoP）的方案。GRF和CoP数据通常从智能鞋垫收集，以分析佩戴者的步态并诊断平衡问题。这种方法可用于改善用户的康复过程，并为特定疾病的患者提供定制的治疗计划，使其成为许多领域的有用技术。然而,用于直接监测GRF和CoP值的常规测量设备，例如F扫描，是昂贵的，对该行业的商业化构成挑战。为了解决这个问题,本文提出了一种技术来预测相关指标只使用低成本的力敏电阻（FSR）传感器，而不是昂贵的设备。在这项研究中,数据是从同时佩戴低成本FSR传感器和F扫描设备的受试者收集的，并使用监督学习技术分析收集的数据集之间的关系。使用所提出的技术，构建了一个人工神经网络，该神经网络可以仅使用来自FSR传感器的数据得出接近实际F扫描值的预测值。在这个过程中,使用六个虚拟力代替整个鞋底的压力值计算GRF和CoP。通过各种模拟验证，与传统预测技术相比，使用所提出的技术可以实现30％以上的改进预测精度。
