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Article Introduction
Wearable health monitoring devices can reflect an individual's health by measuring physiological data without restricting an individual's daily activities. This sensing system can be embedded in the insole to measure and analyze physiological data on the sole of the foot to understand the health of the individual.
M. from Canada Dr. Jamal Deen's team published a related article in Sensors, where they conducted a comprehensive review of insoles-based intelligent sensing systems that can measure a variety of health parameters, and raised and explored some of the problems that need to be solved, as shown in Figure 1.
Figure 1. Smart insole review.
Research process and results
Based on the sensing mechanism and application, the monitoring system can be roughly divided into two categories: foot pressure monitoring system and gait monitoring system. Foot pressure monitoring systems use pressure sensors to measure gait characteristics or detailed information about the distribution of plantar pressure; gait monitoring systems rely primarily on inertial sensing units to measure gait dynamics and monitor gait patterns.
This article reviews ten different plantar pressure distribution sensors. For example, Professor Bernhard Rumpe et al. of RWTH Aachen University in Germany placed three sensors on each of the eight measuring points of the insoles, which are lightweight, small and capable of recording more than 24 hours of data. Dr. Josie Hughes' team has developed a soft strain transducer synthesized from carbon black and carbon fiber conductive particles on a non-conductive silicon cone matrix, and its flexible properties provide favorable conditions for long-term monitoring. Each system has its own advantages and disadvantages, but while enhancing one advantage, you have to sacrifice some other performance. For example, increasing the number of sensors to obtain more detailed measurements increases the consumption of electricity.
The sensor types of these smart insoles are very similar, the main difference is the fusion of sensors and the algorithms that analyze data. For example, Gu-Min Jeong's team used a support vector machine algorithm to distinguish between three walking types, and Professor Hyunil Kim et al. used deep convolutional neural networks (DCNNs) to classify similar gait types with more than 90% accuracy.
Based on the analysis review of the above technologies, this paper gives four issues and considerations that need to be paid attention to in the development of smart insoles: the choice of sensors; the optimal frequency or rate of the acquired signal; in terms of data analysis, information from multiple sensors can be combined or fused with each other to perform more complex analysis; and more reasonable data feedback.
In summary, the authors summarize ten current challenges for smart insole research:
(1) Placement of the sensor, as shown in Figure 2;
(2) System integration;
(3) Signal error and calibration;
(4) Synchronization of signals;
(5) System compatibility;
(6) User comfort;
(7) Compatibility of the system with shoes;
(8) Applicability in different contexts;
(9) Consistency of equipment;
(10) Data and Privacy Security.
Figure 2. Position recommendation of sensors measuring various parameters in an insole-based monitoring system.
An ideal insole system should contain all the desired features, including comfortable wearing, complete reception signals, accurate data, and high energy efficiency and long battery life. In addition, the current challenge must also consider adding to the design of a high-performance and efficient insole-based sensor system. Based on the above discussion, the authors describe four future prospects for smart insoles: a combination of multi-sensor detection, intelligent feedback systems, the use of smart textiles, and interdisciplinary collaboration.
Summary of the study
This paper presents the latest research progress of insole-based lower limb health monitoring system. The main purpose of such a system is to conduct continuous and unobtrusive monitoring of lower limb health without affecting people's daily activities.
By developing a device that measures plantar pressure distribution, inertial movements, foot temperature, and heart rate, parameters such as plantar pressure, body temperature, pulse frequency, and gait can be easily measured, thereby achieving the purpose of remotely monitoring lower limb health, so that users and healthcare providers can obtain information reflecting the daily health of individuals. This system, combined with computational and machine learning techniques, can detect early diseases or other abnormalities with far-reaching implications for an individual's health and lifestyle. At present, further research and technology development is needed to solve some of the existing challenges, such as determining the optimal number and location of sensors, ensuring user comfort and ease of use, power efficiency, data privacy and security, etc.