A digital nervous system aiming toward personalized IoT healthcareShow others and affiliations
2021 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 7757
Article in journal (Refereed) Published
Abstract [en]
Body area networks (BANs), cloud computing, and machine learning are platforms that can potentially enable advanced healthcare outside the hospital. By applying distributed sensors and drug delivery devices on/in our body and connecting to such communication and decision-making technology, a system for remote diagnostics and therapy is achieved with additional autoregulation capabilities. Challenges with such autarchic on-body healthcare schemes relate to integrity and safety, and interfacing and transduction of electronic signals into biochemical signals, and vice versa. Here, we report a BAN, comprising flexible on-body organic bioelectronic sensors and actuators utilizing two parallel pathways for communication and decision-making. Data, recorded from strain sensors detecting body motion, are both securely transferred to the cloud for machine learning and improved decision-making, and sent through the body using a secure body-coupled communication protocol to auto-actuate delivery of neurotransmitters, all within seconds. We conclude that both highly stable and accurate sensing—from multiple sensors—are needed to enable robust decision making and limit the frequency of retraining. The holistic platform resembles the self-regulatory properties of the nervous system, i.e., the ability to sense, communicate, decide, and react accordingly, thus operating as a digital nervous system. © 2021, The Author(s).
Place, publisher, year, edition, pages
Nature Research , 2021. Vol. 11, no 1, article id 7757
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-52956DOI: 10.1038/s41598-021-87177-zScopus ID: 2-s2.0-85104084403OAI: oai:DiVA.org:ri-52956DiVA, id: diva2:1546974
Note
Funding details: Stiftelsen för Strategisk Forskning, SSF; Funding details: VINNOVA; Funding details: Japan Science and Technology Agency, JST; Funding details: Linköpings Universitet, LiU; Funding details: Knut och Alice Wallenbergs Stiftelse; Funding details: Centrum för Industriell Informationsteknologi, Linköpings Universitet, CENIIT, LiU; Funding text 1: Major funding for this work was provided by the Swedish Foundation for Strategic Research, Vinnova, and the Japanese Science and Technology Agency. Additional funding was provided by grants from the Knut and Alice Wallenberg Foundation and the Önnesjö Foundation. We wish to thank Andrey Maleev and Eric Claar (Linköping University) for electronic back end-design and implementation, Dr Tomoyuki Yokota and Hanbit Jin (University of Tokyo) for aid with sensor development and input, and Theofilos Kakantousis and Robin Anders-son (RISE SICS). The authors also thank Thor Balkhed (Linköping University) for filming, Jonas Askergren (NyTeknik) for inspiration and assistance with Fig. 1, Per Janson and Dr Robert Brooke (conceptualized.tech) for visualization input and movie editing, and Dr Jae Joon Kim for significant assistance in reviewing the manuscript.
2021-04-232021-04-232024-04-09Bibliographically approved