journal article·2025-07-08

3D Printed Flexible Piezoelectric Sensor with Enhanced Performance for Gait Recognition

Kailong Xu, Bingyu Huang, Xiaofei Zhang, Shuzhi Sam Ge, Xiangxia Wei

ACS Applied Electronic Materials· Volume 7· Issue 13
Publisher page

Abstract

While wearable piezoelectric sensors are highly desirable for their reliable service in human–machine interfaces, substantial challenges remain in improving their electrical performance and structural design. Herein, a flexible and sensitive piezoelectric sensor is developed through electro-assisted fused deposition modeling (E-FDM) 3D printing, incorporating carbon nanotubes (CNTs) into a polyvinylidene fluoride (PVDF) matrix. The addition of CNTs and the applied external electric field during 3D printing significantly increase the content of the β-phase in PVDF, thereby improving the electrical output. Moreover, the unique grid structures are of great importance in enhancing the piezoelectric performance of PVDF-based piezocomposites. As a result, the 3D printed piezoelectric sensors demonstrate a high sensitivity of 65.8 mV kPa –1, rapid response time <30 ms, and outstanding cyclic stability of 100,000 cycles. Finally, the 3D Printed piezoelectric sensor is integrated into an intelligent insole system for real-time gait recognition. An impressive recognition accuracy of up to 97.25% is achieved for classifying various motions of patients with Parkinson’s disease (PD), including walking, limping, staggering, and falling, with the assistance of machine learning. 3D printing is a powerful tool in manipulating a barrier-free piezoelectric sensor with enhanced electrical performance for gait recognition and offers broad application prospects in intelligent sensing.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Gait Recognition and Analysis · Advanced Sensor and Energy Harvesting Materials · Hand Gesture Recognition Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute for Future (IFF), School of Automation, Shandong Key Laboratory of Industrial Control Technology

Qingdao University

College of Materials Science and Engineering

Department of Electrical & Computer Engineering

National University of Singapore

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