A Multitasking Flexible Sensor via Reservoir Computing

• Published in: Advanced materials (Weinheim) Vol. 34; no. 26; pp. e2201663 - n/a
• Main Authors: Wakabayashi, Seiji, Arie, Takayuki, Akita, Seiji, Nakajima, Kohei, Takei, Kuniharu
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.07.2022
• Subjects: Data collection; Electronics; Flexible components; flexible sensors; Flooding; Graphene; Hydrophobic surfaces; Hydrophobicity; Materials science; Meteorological data; Multitasking; Natural disasters; Power consumption; Rain; reservoir computing; resistive sensors; Sensors; Signal processing; weather monitoring; Wind speed; reservoir computing; flexible sensors; resistive sensors; weather monitoring
• ISSN: 0935-9648; 1521-4095
• DOI: 10.1002/adma.202201663

Natural disasters are reported globally, and one source of severe damage to cities is flooding caused by locally heavy rain. Sharing of local weather information can save lives. However, it is difficult to collect local weather information in real‐time because such data collection requires bulky, expensive sensors. For local, real‐time monitoring of heavy rain and wind, a sensor system should be simple and low‐cost so that it can be attached to a variety of surfaces, including roofs, vehicles, and umbrellas. To develop simple, low‐cost multitasking sensors located on nonplanar surfaces, a flexible rain sensor to monitor waterdrop volume and wind velocity is devised. To monitor both simultaneously, a laser‐induced graphene‐based superhydrophobic conductive film is introduced. Using the superhydrophobic surface, water dynamics are measured when waterdrops collide with the sensor surface, and obtained time‐series data are processed using “reservoir computing” to extract the volume and velocity from a single sensor as multitasking electronics. As a proof‐of‐concept, it is shown that the sensor measures continuous, long‐term volume and wind‐change dynamics. The results demonstrate feasibility of multitasking electronics with reservoir computing to reduce sensor integration complexity with low power consumption for both sensor and signal processing. A multitasking, flexible sensor sheet for monitoring of weather information via a flexible resistive sensor and reservoir computing is reported. This sensor and reservoir computing combination enables the device to estimate water droplet volume and wind velocity with a small training dataset. An important advance is to realize multiple parameter detection simultaneously using only one flexible sensor.