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Korzeniewska E, Zawiślak R, Przybył S, Sarna P, Bilska A, Mączka M. Prototype of Data Collector from Textronic Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:9813. [PMID: 38139659 PMCID: PMC10871124 DOI: 10.3390/s23249813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/12/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
In the era of miniaturization of electronic equipment and the need to connect sensors with textile materials, including clothing, the processing of signals received from the implemented sensors becomes an important issue. Information obtained by measuring the electrical properties of the sensors must be sent, processed, and visualized. For this purpose, the authors of this article have developed a prototype of a data collector obtained from textronic sensors created on composite textile substrates. The device operates in a system consisting of an electronic module based on the nRF52 platform, which supports wireless communication with sensors using Bluetooth technology and transmits the obtained data to a database hosted on the Microsoft Azure platform. A mobile application based on React Native technology was created to control the data stream. The application enables automatic connection to the selected collector, data download and their presentation in the form of selected charts. Initial verification tests of the system showed the correctness and reliability of its operation, and the presented graphs created from the obtained data indicate the usefulness of the device in applications where measurements and recording of impedance, resistance, and temperature are necessary. The presented prototype of a data collector can be used for resistance, impedance, and temperature measurements in the case of textronic structures but also in other wearable electronic systems.
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Affiliation(s)
- Ewa Korzeniewska
- Institute of Electrical Engineering Systems, Lodz University of Technology, Stefanowskiego 18 Street, 90-537 Lodz, Poland
| | - Rafał Zawiślak
- Institute of Automatic Control, Lodz University of Technology, Stefanowskiego 18 Street, 90-537 Lodz, Poland;
| | - Szymon Przybył
- Faculty of Electrical Electronic Computer and Control Engineering, Lodz University of Technology, Stefanowskiego 18 Street, 90-537 Lodz, Poland; (S.P.); (P.S.); (A.B.)
| | - Piotr Sarna
- Faculty of Electrical Electronic Computer and Control Engineering, Lodz University of Technology, Stefanowskiego 18 Street, 90-537 Lodz, Poland; (S.P.); (P.S.); (A.B.)
| | - Anna Bilska
- Faculty of Electrical Electronic Computer and Control Engineering, Lodz University of Technology, Stefanowskiego 18 Street, 90-537 Lodz, Poland; (S.P.); (P.S.); (A.B.)
| | - Mariusz Mączka
- Department of Electronics Fundamentals, Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland;
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Shajari S, Kuruvinashetti K, Komeili A, Sundararaj U. The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:9498. [PMID: 38067871 PMCID: PMC10708748 DOI: 10.3390/s23239498] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023]
Abstract
Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.
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Affiliation(s)
- Shaghayegh Shajari
- Center for Applied Polymers and Nanotechnology (CAPNA), Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N1 N4, Canada;
- Center for Bio-Integrated Electronics (CBIE), Querrey Simpson Institute for Bioelectronics (QSIB), Northwestern University, Evanston, IL 60208, USA
| | - Kirankumar Kuruvinashetti
- Intelligent Human and Animal Assistive Devices, Department of Biomedical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.K.); (A.K.)
- Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Amin Komeili
- Intelligent Human and Animal Assistive Devices, Department of Biomedical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.K.); (A.K.)
- Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Uttandaraman Sundararaj
- Center for Applied Polymers and Nanotechnology (CAPNA), Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N1 N4, Canada;
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Yun SH, Kim HJ, Ryu JK, Kim SC. Fine-Grained Motion Recognition in At-Home Fitness Monitoring with Smartwatch: A Comparative Analysis of Explainable Deep Neural Networks. Healthcare (Basel) 2023; 11:healthcare11070940. [PMID: 37046868 PMCID: PMC10094383 DOI: 10.3390/healthcare11070940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/13/2023] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
The squat is a multi-joint exercise widely used for everyday at-home fitness. Focusing on the fine-grained classification of squat motions, we propose a smartwatch-based wearable system that can recognize subtle motion differences. For data collection, 52 participants were asked to perform one correct squat and five incorrect squats with three different arm postures (straight arm, crossed arm, and hands on waist). We utilized deep neural network-based models and adopted a conventional machine learning method (random forest) as a baseline. Experimental results revealed that the bidirectional GRU/LSTMs with an attention mechanism and the arm posture of hands on waist achieved the best test accuracy (F1-score) of 0.854 (0.856). High-dimensional embeddings in the latent space learned by attention-based models exhibit more clustered distributions than those by other DNN models, indicating that attention-based models learned features from the complex multivariate time-series motion signals more efficiently. To understand the underlying decision-making process of the machine-learning system, we analyzed the result of attention-based RNN models. The bidirectional GRU/LSTMs show a consistent pattern of attention for defined squat classes, but these models weigh the attention to the different kinematic events of the squat motion (e.g., descending and ascending). However, there was no significant difference found in classification performance.
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Affiliation(s)
- Seok-Ho Yun
- Department of Physical Education, Graduate School, Dongguk University, Seoul 04620, Republic of Korea
| | - Hyeon-Joo Kim
- Machine Learning Systems Lab., College of Sports Science, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jeh-Kwang Ryu
- Department of Physical Education, Graduate School, Dongguk University, Seoul 04620, Republic of Korea
| | - Seung-Chan Kim
- Machine Learning Systems Lab., College of Sports Science, Sungkyunkwan University, Suwon 16419, Republic of Korea
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D’Souza O, Mukhopadhyay SC, Sheng M. Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge. SENSORS (BASEL, SWITZERLAND) 2022; 22:8143. [PMID: 36365840 PMCID: PMC9659114 DOI: 10.3390/s22218143] [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: 09/09/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time information. Our research presents a solution for the health, security, safety, and fire domains to obtain temporally synchronous, credible and high-resolution data from sensors to maintain the temporal hierarchy of reported events. We developed a multisensor fusion framework with energy conservation via domain-specific "wake up" triggers that turn on low-power model-driven microcontrollers using machine learning (TinyML) models. We investigated optimisation techniques using anomaly detection modes to deliver real-time insights in demanding life-saving situations. Using energy-efficient methods to analyse sensor data at the point of creation, we facilitated a pathway to provide sensor customisation at the "edge", where and when it is most needed. We present the application and generalised results in a real-life health care scenario and explain its application and benefits in other named researched domains.
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Affiliation(s)
- Ollencio D’Souza
- School of Engineering, Faculty of Science and Engineering, North Ryde Campus, Macquarie University, Sydney, NSW 2109, Australia
| | - Subhas Chandra Mukhopadhyay
- School of Engineering, Faculty of Science and Engineering, North Ryde Campus, Macquarie University, Sydney, NSW 2109, Australia
| | - Michael Sheng
- Department of Computing, Macquarie University, Sydney, NSW 2109, Australia
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Teoh YX, Lai KW, Usman J, Goh SL, Mohafez H, Hasikin K, Qian P, Jiang Y, Zhang Y, Dhanalakshmi S. Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4138666. [PMID: 35222885 PMCID: PMC8881170 DOI: 10.1155/2022/4138666] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/24/2022] [Accepted: 01/26/2022] [Indexed: 12/30/2022]
Abstract
Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren-Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.
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Affiliation(s)
- Yun Xin Teoh
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Juliana Usman
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Siew Li Goh
- Faculty of Medicine, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Hamidreza Mohafez
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Pengjiang Qian
- School of Artificial Intelligence and Computer Sciences, Jiangnan University, Wuxi 214122, China
| | - Yizhang Jiang
- School of Artificial Intelligence and Computer Sciences, Jiangnan University, Wuxi 214122, China
| | - Yuanpeng Zhang
- Department of Medical Informatics of Medical (Nursing) School, Nantong University, Nantong 226001, China
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
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