This paper delves deeply into the innovative realm of integrating human emotions with wearable technology. The primary focus is on the conceptualization and development of a kiss transfer device that harnesses the power of wearable technology to bridge the physical gap in human-human interactions. By investigating the intricate nuances of the human-human kissing process, the research seeks to replicate this intimate gesture through a technological medium. The paper not only elaborates on the anatomy, evolution, and hormonal dynamics of kissing but also underscores the transformative potential of wearable technology in capturing and transmitting these intimate moments. This exploration opens up new horizons for long-distance relationships, offering a tangible touchpoint that goes beyond traditional communication methods. Through this pioneering work, the research positions wearable technology as not just a tool for communication but as an extension of our human emotions and expressions.
Deep learning-based discriminant model for wearable sensing gait pattern
Vol 1, Issue 1, 2020
VIEWS - 4133 (Abstract)
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Abstract
In order to effectively improve the accuracy of identifying the gait pattern of wearable sensing data, this paper proposes a new model for deep learning gait mode discrimination that integrates convolutional neural network and long short-term memory neural network, which makes full use of the convolutional neural network to obtain the most local spatial characteristics of data and the long short-term memory neural network to obtain the inherent characteristics of the data, and effectively excavates the hidden high-dimensional, nonlinear, time-space gait characteristics of random wearable sensing timing gait data that are closely related to gait pattern changes, to improve the classification performance of gait mode. The effectiveness of the proposed model in this paper is evaluated using the HAR dataset from University of California UCI database. The experiment results showed that the proposed model in this paper can effectively obtain the time-space gait characteristics embedded in the wearable sensor gait data, and the classification accuracy can reach 91.45%, the precision rate 91.54%, and the recall rate 91.53%, and the classification performance is significantly better than that of the traditional machine learning model, which provides a new solution for accurately identifying the gait mode of wearable sensor data.
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References
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Copyright (c) 2020 Jianning Wu, Qiaoling Tan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Prof. Zhen Cao
College of Information Science & Electronic Engineering, Zhejiang University
China, China
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