Arie Levin

Mr. Arie Levin

Chemical & Bio Engineering, Monash University, Melbourne, Victoria, Australia
orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 January 2024 in Chemical Reviews
Verified
Journal Article
Closed access
SGShu Gong
Shu Gong
YLYan Lu
Yan Lu
JYJialiang Yin
Jialiang Yin
Arie LevinArie Levin
Arie Levin
Wenlong ChengWenlong Cheng
Wenlong Cheng

6 April 2023 in Biosensors
Verified
Journal Article
Open access
PDF file
Arie LevinArie Levin
Arie Levin
Shu GongShu Gong
Shu Gong
Wenlong ChengWenlong Cheng
Wenlong Cheng
Bandage is a well-established industry, whereas wearable electronics is an emerging industry. This review presents the bandage as the base of wearable bioelectronics. It begins with introducing a detailed background to bandages and the development of bandage-based smart sensors, which is followed by a sequential discussion of the technical characteristics of the existing bandages, a more practical methodology for future applications, and manufacturing processes of bandage-based wearable biosensors. The review then elaborates on the advantages of basing the next generation of wearables, such as acceptance by the customers and system approvals, and disposal.

28 November 2025 in Science Advances
Verified
Journal Article
Open access
LYLim Wei Yap
Lim Wei Yap
Arie LevinArie Levin
Arie Levin
YJYiwen Jiang
Yiwen Jiang
DNDuong Nhu
Duong Nhu
Shu GongShu Gong
Shu Gong
Wenlong ChengWenlong Cheng
Wenlong Cheng
Continuous fetal movement monitoring in late pregnancy may improve fetal wellbeing and pregnancy outcomes. While fetal movements can be visualized with ultrasound, it is intermittent and limited to clinical settings. Inertial measurement units may enable at-home fetal monitoring but usually require a large-footprint, multisensor design. Here, we report smart, compact wearable pressure-strain combo sensors continuously monitoring fetal movements through maternal abdominal skin motions. In the 2D and 3D artificial abdomen systems, our octagonal-shaped gold nanowire–based strain sensor served as an isotropic sensor, enabling omnidirectional simulated “kicking load” detection within an area of ~77 (2D) and ~217 cm 2 (3D), while an interdigitated electrode–based pressure sensor showed highly sensitive localized load detection. Building upon these findings, we designed compact pressure- and strain-sensing integrated Band-Aids and tested on 59 pregnant women. We developed machine learning models to distinguish fetal from nonfetal movements with >90% accuracy in ultrasound-based validation studies. This AI-powered, Band-Aid–like sensing system offers potential as a compact, comfortable, and accurate continuous out-of-hospital fetal movement monitoring technology.