AI-enabled photonic smart garment for movement analysis

Leticia Avellar,Anselmo Frizera,Gabriel Delgado,Arnaldo Leal-Junior,Eduardo Rocon,Carlos Stefano Filho

doi:10.1038/s41598-022-08048-9

Abstract

Smart textiles are novel solutions for remote healthcare monitoring which involve non-invasive sensors-integrated clothing. Polymer optical fiber (POF) sensors have attractive features for smart textile technology, and combined with Artificial Intelligence (AI) algorithms increase the potential of intelligent decision-making. This paper presents the development of a fully portable photonic smart garment with 30 multiplexed POF sensors combined with AI algorithms to evaluate the system ability on the activity classification of multiple subjects. Six daily activities are evaluated: standing, sitting, squatting, up-and-down arms, walking and running. A k-nearest neighbors classifier is employed and results from 10 trials of all volunteers presented an accuracy of 94.00 (0.14)%. To achieve an optimal amount of sensors, the principal component analysis is used for one volunteer and results showed an accuracy of 98.14 (0.31)% using 10 sensors, 1.82% lower than using 30 sensors. Cadence and breathing rate were estimated and compared to the data from an inertial measurement unit located on the garment back and the highest error was 2.22%. Shoulder flexion/extension was also evaluated. The proposed approach presented feasibility for activity recognition and movement-related parameters extraction, leading to a system fully optimized, including the number of sensors and wireless communication, for Healthcare 4.0.

Highlights

Smart textiles are novel solutions for remote healthcare monitoring which involve non-invasive sensors-integrated clothing
Remote healthcare monitoring with high speed and intelligent execution can be achieved by increasing the number of devices and using Artificial Intelligence (AI) algorithms[6], since the combination of Internet of Things (IoT) and AI in the healthcare sector has a higher potential of making intelligent decisions in real-time for patient medical r ecords[5,7]
In order to obtain an uniform distribution of the sensors in the smart garment, adjacent sensors are positioned with 10-cm distance between them to evaluate the possibility of identifying and classify activities using this sensor arrays

Summary

Introduction

Smart textiles are novel solutions for remote healthcare monitoring which involve non-invasive sensors-integrated clothing. Combining the low-cost wearable approach based on multiplexed POF-based sensors with AI algorithms, this paper presents a promising remote healthcare monitoring solution based on a scalable photonic garment capable of accurately identifying activities, by using the kNN classification algorithm, and assess movement-related parameters, including physiological and spatio-temporal gait parameters, for application in Smart Healthcare.

Results

Conclusion