Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USA.
Sensors (Basel). 2024 Jun 28;24(13):4202. doi: 10.3390/s24134202.
This work presents a novel approach for elbow gesture recognition using an array of inductive sensors and a machine learning algorithm (MLA). This paper describes the design of the inductive sensor array integrated into a flexible and wearable sleeve. The sensor array consists of coils sewn onto the sleeve, which form an LC tank circuit along with the externally connected inductors and capacitors. Changes in the elbow position modulate the inductance of these coils, allowing the sensor array to capture a range of elbow movements. The signal processing and random forest MLA to recognize 10 different elbow gestures are described. Rigorous evaluation on 8 subjects and data augmentation, which leveraged the dataset to 1270 trials per gesture, enabled the system to achieve remarkable accuracy of 98.3% and 98.5% using 5-fold cross-validation and leave-one-subject-out cross-validation, respectively. The test performance was then assessed using data collected from five new subjects. The high classification accuracy of 94% demonstrates the generalizability of the designed system. The proposed solution addresses the limitations of existing elbow gesture recognition designs and offers a practical and effective approach for intuitive human-machine interaction.
本工作提出了一种新颖的方法，使用感应传感器阵列和机器学习算法 (MLA) 进行肘部手势识别。本文描述了集成到灵活可穿戴袖套中的感应传感器阵列的设计。该传感器阵列由缝制在袖套上的线圈组成，这些线圈与外部连接的感应器和电容器一起形成 LC 槽路。肘部位置的变化会调节这些线圈的电感，从而使传感器阵列能够捕捉到一系列肘部运动。描述了信号处理和随机森林 MLA 来识别 10 种不同的肘部手势。对 8 个对象进行严格评估，并利用数据集将每个手势的样本增加到 1270 次，使系统在使用 5 折交叉验证和留一受试者交叉验证时分别达到了 98.3%和 98.5%的惊人准确性。然后使用从五个新对象收集的数据评估测试性能。高达 94%的分类准确性证明了所设计系统的通用性。所提出的解决方案解决了现有肘部手势识别设计的局限性，并为直观的人机交互提供了一种实用有效的方法。
