In modern technological systems, surface deformation sensors are crucial for detecting stress, bending, and structural alterations. They are widely used in wearable health monitoring devices, soft robotics, flexible electronics, and structural health monitoring. Advances in nanomaterials, flexible polymers, and novel fabrication techniques have enabled the development of sensors with improved sensitivity, stretchability, wear resistance, and operational stability. These sensors allows highly precise detection of minute deformations even in complex and dynamic environments. The paper presents an comprehensive overview of recent technological developments in surface deformation sensing technologies. The fundamental sensing mechanisms including resistive, capacitive, piezoelectric, optical, and triboelectric approaches are systematically analysed. The review evaluates key material systems such as graphene, carbon nanotubes, MXenes, conductive polymers, hydrogels, and hybrid composites, highlighting their mechanical flexibility and sensing performance. The study examines how design elements like serpentine interconnects, wrinkled surfaces, and origami/kirigami-inspired architectures enhances stretchability and sensitivity. The review examines fabrication approaches, such as additive manufacturing, printing, laser scribing, and lithography, with emphasis on scalability and production cost. The evaluation assesses major application areas including human-machine interaction, soft robotics, healthcare monitoring, and structural health monitoring. The research investigates key challenges such as fatigue resistance, environmental sensitivity, long-term stability, and large-scale integration. The paper presents an outline of future research directions for next-generation surface deformation sensors, highlighting emerging trends such as bio-integrated systems, wireless sensing platforms, AI-assisted data processing and self-powered sensors.
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Priyanka Jagtap
Meena Laad
Symbiosis International University
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Jagtap et al. (Mon,) studied this question.
www.synapsesocial.com/papers/69d892886c1944d70ce03e26 — DOI: https://doi.org/10.1007/s42452-026-08498-0
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