Machine Learning-Enhanced Flexible Mechanical Sensing

Abstract

To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human–machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

Engineering Smart Composite Hydrogels for Wearable Disease Monitoring

“…Furthermore, the multi-stimuli responsiveness of composite hydrogels often leads to unwanted noise or coupling between multiple stimuli. Data processing methods based on machine learning (ML) can efficiently process a large amount of data generated by sensors and discover intricate and hidden relationships in the data [ 215 , 216 ]. The ML model trained by feeding a large amount of data can effectively reduce noise or decouple multiple stimulus signals.…”

Mechanically-Guided 3D Assembly for Architected Flexible Electronics

“…Electronic devices in planar forms utilize only in-plane spaces to integrate functional circuits, usually with a limited areal density in the cases of stretchable designs. ,− Various 3D configurations enabled by mechanically-guided assembly methods stand as promising platforms to achieve higher areal densities in flexible electronics, owing to their capabilities of expanding the distributions of functional circuits/elements into out-of-plane spaces with programmable spatial resolutions. In terms of achieving higher areal densities, both the multilayer flexible PCB (FPCB) and mechanically-guided assembly methods can be exploited. ,, In comparison to mechanically-guided assembly methods, the fabrication of multilayer FPCB is more simple and low cost.…”

Recent development of piezoelectric biosensors for physiological signal detection and machine learning assisted cardiovascular disease diagnosis

“…With the aid of machine learning algorithms, identication of features of collected cardiovascular signals and implementing automatic classication of specic cardiac conditions have been progressed steadily in recent years. 147 The real-time data analysis and categorization enabled by machine learning assist to detect cardiovascular abnormalities in time, helping healthcare professionals diagnose CVDs and providing early medical interventions to potential patients (Fig. 8).…”