期刊
NANO ENERGY
卷 116, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.nanoen.2023.108824
关键词
Triboelectric; Piezoelectric; Nanocomposite; Pressure sensor; Wearable; Gait analysis; Machine learning
This paper presents the development of a flexible hybrid sensor for gait monitoring and grip strength assessment, achieving high accuracy in feature extraction through machine learning algorithms. The sensor exhibits high sensitivity, low cross talk, and a large pressure range, with the advantages of fast and cost-effective design flow and ease of operation.
Human gait analysis strongly correlates with critical health metrics and provides significant information about physiological well-being. Therefore, accurate, fast, and cost-effective gait monitoring is required for intelligent healthcare systems. This paper reports the development of a flexible hybrid transduction Barium Titanate (BTO)/ SU-8 nanocomposite-based, individually addressable pressure sensor matrix. The proposed sensor is highly suitable for wearables compared to the conventional pressure sensors due to its speedy and cost-effective design flow and ease of operation. The hybrid (piezoelectric/triboelectric), photo-patternable active layer enables strain and contact electrification-based sensing that convolves into a highly sensitive, lower cross talk and large area pressure sensing. The reported sensor is incorporated with a solder-free modular data acquisition setup for a straightforward design integration. A pressure sensitivity of 34 mV kPa-1 for the deep linear region and 2.7 mV kPa-1 for the linear region over a pressure range of 0-170 kPa is reported. The sensor shows excellent reliability and negligible hysteresis with an average deviation of 2.7 %. Furthermore, the 36 pressure cells with hybrid transduction deliver rich feature extraction to machine learning algorithms compared to single transducer-based systems for an accurate gait and grip strength monitoring. The developed convolution neural network (CNN)-2D model gives a model accuracy of 98.5 % and 98.3 % for two different gait characterizations, while delivering a model accuracy of 93.75 % for grip strength assessment. The combination of hybrid sensor design, development, and use of machine learning offers a novel approach to tackle the issues associated with sensors that are incompatible with rapidly developing smart healthcare technology.
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