Comparative Study of Machine Learning Algorithms to Classify Hand Gestures from Deployable and Breathable Kirigami-Based Electrical Impedance Bracelet

• Main Authors: Godwin Ponraj Joseph Vedhagiri, Xin Zhi Wang, Kirthika Senthil Kumar, Hongliang Ren
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Multimodal Technologies and Interaction
• Subjects: kirigami wearable device; gesture classification; machine learning; electrical impedance tomography
• Online Access: https://www.mdpi.com/2414-4088/4/3/47

Wearable devices are gaining recognition for their use as a biosensor platform. Electrical impedance tomography (EIT) is one of the sensing techniques that utilizes wearable sensors as its primary data acquisition system. It measures the impedance or resistance at the peripheral (skin) level and calculates the conductivity distribution throughout the body. Even though the technology has existed for several decades, modern-day EIT devices are still costly and bulky. The paper proposes a novel low-cost kirigami-based wearable device that has soft PEDOT: PSS electrodes for sensing skin impedances. Simulation results show that the proposed kirigami structure for the bracelet has a large deformation during actuation while experiencing relatively lower stress. The paper also presents a comparative study on a few machine learning algorithms to classify hand gestures, based on the measured skin impedance. The best classification accuracy (91.49%) was observed from the quadratic support vector machine (SVM) algorithm with 48 principal components.