We introduce MultiSoft, a multilayer soft sensor capable of sensing real-time contact localization, classification of deformation types, and estimation of deformation magnitudes. We propose a multimodal sensing pipeline that carries out both inverse problem solving and machine learning tasks. Specifically, we employ an electrical impedance tomography (EIT) for contact localization and a support vector machine (SVM) for classifying deformations and regressing their magnitudes. We propose a deformation-aware system which enables maintaining a persistent single-point contact localization throughout the deformation. By updating a time-varying distribution of conductivity change caused by deformations, a single-point contact localization can be maintained and restored to support interaction using both contact localization and deformations.We devise a multilayer structure to fabricate a highly stretchable and flexible soft sensor with a short sensor settlement after excitations. Through a series of experiments and evaluations, we validate both raw sensor and multimodal sensing performance with the proposed method. We further demonstrate applicability and feasibility of MultiSoft with example applications.
Sang Ho Yoon, Luis Paredes, Ke Huo, and Karthik Ramani. 2018. MultiSoft: Soft Sensor Enabling Real-Time Multimodal Sensing with Contact Localization and Deformation Classification. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 3, Article 145 (September 2018), 21 pages. DOI: https://doi.org/10.1145/3264955
Sang Ho Yoon, Personal website