Egger Peter Werner, Srinivas Gidugu Lakshmi, Brandstötter Mathias
ADMiRE Research Center, Carinthia University of Applied Sciences, 9524 Villach, Austria.
Sensors (Basel). 2025 May 10;25(10):3011. doi: 10.3390/s25103011.
Soft and flexible capacitive tactile sensors are vital in prosthetics, wearable health monitoring, and soft robotics applications. However, achieving accurate real-time force detection and spatial localization remains a significant challenge, especially in dynamic, non-rigid environments like prosthetic liners. This study presents a real-time force point detection and tracking system using a custom-fabricated soft elastomeric capacitive sensor array in conjunction with image processing and machine learning techniques. The system integrates Otsu's thresholding, Connected Component Labeling, and a tailored cluster-tracking algorithm for anomaly detection, enabling real-time localization within 1 ms. A 6×6 Dragon Skin-based sensor array was fabricated, embedded with copper yarn electrodes, and evaluated using a UR3e robotic arm and a Schunk force-torque sensor to generate controlled stimuli. The fabricated tactile sensor measures the applied force from 1 to 3 N. Sensor output was captured via a MUCA breakout board and Arduino Nano 33 IoT, transmitting the Ratio of Mutual Capacitance data for further analysis. A Python-based processing pipeline filters and visualizes the data with real-time clustering and adaptive thresholding. Machine learning models such as linear regression, Support Vector Machine, decision tree, and Gaussian Process Regression were evaluated to correlate force with capacitance values. Decision Tree Regression achieved the highest performance (R2=0.9996, RMSE=0.0446), providing an effective correlation factor of 51.76 for force estimation. The system offers robust performance in complex interactions and a scalable solution for soft robotics and prosthetic force mapping, supporting health monitoring, safe automation, and medical diagnostics.
柔软灵活的电容式触觉传感器在假肢、可穿戴健康监测和软体机器人应用中至关重要。然而，实现精确的实时力检测和空间定位仍然是一项重大挑战，尤其是在假肢内衬等动态、非刚性环境中。本研究提出了一种实时力点检测和跟踪系统，该系统使用定制制造的柔软弹性体电容式传感器阵列，并结合图像处理和机器学习技术。该系统集成了大津阈值法、连通组件标记法和一种用于异常检测的定制聚类跟踪算法，能够在1毫秒内实现实时定位。制作了一个基于6×6龙皮的传感器阵列，嵌入铜丝电极，并使用UR3e机器人手臂和Schunk力扭矩传感器进行评估，以产生受控刺激。制作的触觉传感器可测量1至3 N的作用力。传感器输出通过MUCA扩展板和Arduino Nano 33 IoT捕获，传输互电容数据的比率以进行进一步分析。基于Python的处理管道通过实时聚类和自适应阈值对数据进行过滤和可视化。对线性回归、支持向量机、决策树和高斯过程回归等机器学习模型进行了评估，以关联力与电容值。决策树回归实现了最高性能（R2=0.9996，RMSE=0.0446），为力估计提供了51.76的有效相关因子。该系统在复杂交互中具有强大的性能，为软体机器人和假肢力映射提供了可扩展的解决方案，支持健康监测、安全自动化和医疗诊断。
