Applications of flexible materials in health management assisted by machine learning

Abstract

In recent years, the demand for improved health management has become increasingly higher; however, the existing medical resources have made it difficult to meet this demand. The field of health management is in urgent need for self-help monitoring equipment, intelligent identification technology and personalized medical services. This article reviews the application of flexible materials in health management, particularly the application of flexible wearable sensing devices combined with machine learning technology in various medical scenarios, and classifies them into several types of applications such as health monitoring and prevention, disease diagnosis and treatment, rehabilitation treatment and assistance. Flexible materials can be used to fabricate or integrate various types of high-sensitivity sensors with the characteristics of high flexibility and self-adhesion, resulting in a wealth of health monitoring equipment. These devices can self-monitor various physiological indicators in various parts of the human body. The integration of machine learning (ML) makes it possible to analyze and identify subtle, massive, multi-channel and multi-modal sensor data, accelerating the intelligent process of health management and personalized medicine. This paper not only elaborates on various flexible materials and ML algorithms commonly used in the field of health management, but also focuses on discussing the application of ML-assisted flexible materials in different stages of health management, and puts forward prospects for the future development direction, providing reference and inspiration for major changes in the field of health management.

Fig. 1. Sensing devices based on different flexible materials are applied in the field of health management combined with ML. (A) Gel-based gesture recognition system. This figure has been reproduced from ref. with permission from ACS publication, copyright 2023; (B) textile-based human monitoring and identification systems. This figure has been reproduced from ref. with permission from Elsevier publication, copyright 2024; (C) TPU-based sensor for monitoring the full range of human motion. This figure has been reproduced from ref. with permission from ACS publication, copyright 2021. (D) Metal-doped graphene sensors for lung cancer diagnosis. This figure has been reproduced from ref. with permission from Elsevier publication, copyright 2024.

Fig. 2. ML algorithm mechanism diagram. (A) Schematic of DT. (B) Schematic of RF. (C) SVM model diagram including linear and nonlinear models. (D) Schematic of KNN; (E) schematic of an ANN. (F) Schematic of RNN.

Fig. 3. Healthcare management of the flexible materials in different scenes. (A) Motion recognition. This figure has been reproduced from ref. with permission from ACS publication, copyright 2017; (B) sitting position recognition. This figure has been reproduced from ref. with permission from Elsevier publication, copyright 2021; (C) gait recognition. This figure has been reproduced from ref. with permission from Elsevier publication, copyright 2023; (D) fatigue identification and early warning. This figure has been reproduced from ref. with permission from Elsevier publication, copyright 2023.

Fig. 4. Intelligent diagnosis and treatment of diseases in different contexts. (A) Intelligent pulse diagnostic system. This figure has been reproduced from ref. with permission from ACS publication, copyright 2023. (B) Tremor sensor assessment of Parkinson's disease. This figure has been reproduced from ref. with permission from Elsevier publication copyright 2021. (C) Walking pattern recognition of patients with Parkinson's disease. This figure has been reproduced from ref. with permission from ACS publication, copyright 2019. (D) Individualized management of wounds. This figure has been reproduced from ref. with permission from Elsevier publication, copyright 2022. (E) Wound identification at different stages. This figure has been reproduced from ref. with permission from ACS publication, copyright 2022. (F) Predicted structure and optimized hydrogel properties. This figure has been reproduced from ref. with permission from ACS publication, copyright 2020.

Fig. 5. Application of flexible materials and ML in rehabilitation and assistance. (A) Schematic of patient identification using smart insoles and ML. This figure has been reproduced from ref. with permission from Wiley Publications copyright 2021. (B) Diagram of the HMI gesture recognition system. This figure has been reproduced from ref. with permission from ACS publication, copyright 2023. (C) Smart glove and its potential applications. This figure has been reproduced from ref. with permission from Elsevier publication, copyright 2022. (D) Silent speech recognition schematic. This figure has been reproduced from ref. with permission from ACS publication, copyright 2023.