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Rukmini PG, Hegde RB, Basavarajappa BK, Bhat AK, Pujari AN, Gargiulo GD, Gunawardana U, Jan T, Naik GR. Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey. SENSORS (BASEL, SWITZERLAND) 2024; 24:4301. [PMID: 39001080 PMCID: PMC11243832 DOI: 10.3390/s24134301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/16/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.
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Affiliation(s)
- Pradyumna G. Rukmini
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Roopa B. Hegde
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Bommegowda K. Basavarajappa
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Anil Kumar Bhat
- Department of Electronics & Communication Engineering, NMAM Institute Technology, NITTE (Deemed to be University), Nitte 574110, India; (P.G.R.); (R.B.H.); (B.K.B.); (A.K.B.)
| | - Amit N. Pujari
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hertfordshire AL10 9AB, UK;
- School of Engineering, University of Aberdeen, Aberdeen AB24 3FX, UK
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia; (G.D.G.); (U.G.)
- The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Penrith, NSW 2751, Australia
- Translational Health Research Institute, Western Sydney University, Penrith, NSW 2751, Australia
- The Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
| | - Upul Gunawardana
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia; (G.D.G.); (U.G.)
| | - Tony Jan
- Centre for Artificial Intelligence Research and Optimization (AIRO), Design and Creative Technology Vertical, Torrens University, Ultimo, NSW 2007, Australia;
| | - Ganesh R. Naik
- Centre for Artificial Intelligence Research and Optimization (AIRO), Design and Creative Technology Vertical, Torrens University, Ultimo, NSW 2007, Australia;
- College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia
- Design and Creative Technology Vertical, Torrens University, Wakefield Street, Adelaide, SA 5000, Australia
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Huang S, Dai H, Yu X, Wu X, Wang K, Hu J, Yao H, Huang R, Niu W. A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network. iScience 2024; 27:109093. [PMID: 38375238 PMCID: PMC10875158 DOI: 10.1016/j.isci.2024.109093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 12/09/2023] [Accepted: 01/30/2024] [Indexed: 02/21/2024] Open
Abstract
The monitoring of treadmill walking energy expenditure (EE) plays an important role in health evaluations and management, particularly in older individuals and those with chronic diseases. However, universal and highly accurate prediction methods for walking EE are still lacking. In this paper, we propose an ensemble neural network (ENN) model that predicts the treadmill walking EE of younger and older adults and stroke survivors with high precision based on easy-to-obtain features. Compared with previous studies, the proposed model reduced the estimation error by 13.95% and 66.20% for stroke survivors and younger adults, respectively. Furthermore, a contactless monitoring system was developed based on Kinect, mm-wave radar, and ENN algorithms, and the treadmill walking EE was monitored in real time. This ENN model and monitoring system can be combined with smart devices and treadmill, making them suitable for evaluating, monitoring, and tracking changes in health during exercise and in rehabilitation environments.
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Affiliation(s)
- Shangjun Huang
- Translational Research Center, Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China
| | - Houde Dai
- Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang 362201, China
| | - Xiaoming Yu
- Rehabilitation Medical Center, Shanghai Seventh’s Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200137, China
| | - Xie Wu
- Key Laboratory of Exercise and Health Sciences, Ministry of Education, Shanghai University of Sport, Shanghai 200438, China
| | - Kuan Wang
- Translational Research Center, Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China
| | - Jiaxin Hu
- Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang 362201, China
| | - Hanchen Yao
- Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang 362201, China
| | - Rui Huang
- Key Laboratory of Exercise and Health Sciences, Ministry of Education, Shanghai University of Sport, Shanghai 200438, China
| | - Wenxin Niu
- Translational Research Center, Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China
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Ni Z, Wu T, Wang T, Sun F, Li Y. Deep Multi-Branch Two-Stage Regression Network for Accurate Energy Expenditure Estimation with ECG and IMU Data. IEEE Trans Biomed Eng 2022; 69:3224-3233. [PMID: 35353692 DOI: 10.1109/tbme.2022.3163429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Energy Expenditure (EE) estimation plays an important role in objectively evaluating physical activity and its impact on human health. EE during activity can be affected by many factors, including activity intensity, individual physical and physiological characteristics, environment, etc. However, current studies only use very limited information, such as heart rate and step count, to estimate EE, which leads to a low estimation accuracy. METHODS In this study, we proposed a deep multi-branch two-stage regression network (DMTRN) to effectively fuse a variety of related information including motion information, physiological characteristics, and human physical information, which significantly improved the EE estimation accuracy. The proposed DMTRN consists of two main modules: a multi-branch convolutional neural network module which is used to extract multi-scale context features from inertial measurement unit (IMU) data and electrocardiogram (ECG) data and a two-stage regression module which aggregated the extracted multi-scale context features containing the physiological and motion information and the anthropometric features to accurately estimate EE. RESULTS Experiments performed on 33 participants show that our proposed method is more accurate and the average root mean square error (RMSE) is reduced by 22.8% compared with previous works. CONCLUSION The EE estimation accuracy was improved by the proposed DMTRN model with a well-designed network structure and new input signal ECG. SIGNIFICANCE This study verified that ECG was much more effective than HR for EE estimation and cast light on EE estimation using the deep learning method.
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