Intelligent Workplace Activity Monitoring and Detection using Self-powered Triboelectric/Piezoelectric Sensor Augmented Machine Learning

• Published in: IEEE sensors letters 2023-06, Vol.7 (6), p.1-4
• Main Authors: Beigh, Faizan Tariq, Naval, Sourav, Garg, Vanshita, Beigh, Nadeem Tariq, Mallick, Dhiman
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Convolutional Neural Network; Convolutional neural networks; Data acquisition; Employment; Fingers; Flexible components; Human Activity Recognition; Hybrid Transducers; Intelligent sensors; Keyboards; Machine learning; Monitoring; Mouse devices; Piezoelectric transducers; Piezoelectricity; Self-powered Sensing; Sensors; Tactile sensors (robotics); Training; Triboelectricity; Wearables
• ISSN: 2475-1472; 2475-1472
• DOI: 10.1109/LSENS.2023.3276814

In this letter, we propose a method for monitoring workplace activity using a self-powered, flexible triboelectric/piezoelectric tactile sensor. The flexible sensor is applied to the forefinger of the participants while they perform typical workplace activities like keyboard typing, mouse scrolling, scribbling, and finger stretching. The electrical output signal corresponding to triboelectric and piezoelectric sensors for each activity is recorded by a data acquisition unit (DAQ) and analyzed using an efficient convolution neural network (CNN) algorithm to precisely distinguish between the different activities. The classification results demonstrate that the proposed strategy of incorporating dual transduction mechanisms results in an accuracy higher than 98\%, and the precision and recall of 98.13\% and 98.14\%, respectively. Due to the simple and inexpensive design of the sensor and the remarkable accuracy of the proposed machine-learning architecture, the reported work has immense potential in a wide range of workplace monitoring applications.