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Chiu YJ. Automated medication verification system (AMVS): System based on edge detection and CNN classification drug on embedded systems. Heliyon 2024; 10:e30486. [PMID: 38742071 PMCID: PMC11089321 DOI: 10.1016/j.heliyon.2024.e30486] [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: 04/24/2024] [Accepted: 04/28/2024] [Indexed: 05/16/2024] Open
Abstract
A novel automated medication verification system (AMVS) aims to address the limitation of manual medication verification among healthcare professionals with a high workload, thereby reducing medication errors in hospitals. Specifically, the manual medication verification process is time-consuming and prone to errors, especially in healthcare settings with high workloads. The proposed system strategy is to streamline and automate this process, enhancing efficiency and reducing medication errors. The system employs deep learning models to swiftly and accurately classify multiple medications within a single image without requiring manual labeling during model construction. It comprises edge detection and classification to verify medication types. Unlike previous studies conducted in open spaces, our study takes place in a closed space to minimize the impact of optical changes on image capture. During the experimental process, the system individually identifies each drug within the image by edge detection method and utilizes a classification model to determine each drug type. Our research has successfully developed a fully automated drug recognition system, achieving an accuracy of over 95 % in identifying drug types and conducting segmentation analyses. Specifically, the system demonstrates an accuracy rate of approximately 96 % for drug sets containing fewer than ten types and 93 % for those with ten types. This verification system builds an image classification model quickly. It holds promising potential in assisting nursing staff during AMVS, thereby reducing the likelihood of medication errors and alleviating the burden on nursing staff.
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Affiliation(s)
- Yen-Jung Chiu
- Department of Biomedical Engineering, Ming Chuan University, Taoyuan, 333, Taiwan
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Ferrara M, Bertozzi G, Di Fazio N, Aquila I, Di Fazio A, Maiese A, Volonnino G, Frati P, La Russa R. Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review. Healthcare (Basel) 2024; 12:549. [PMID: 38470660 PMCID: PMC10931321 DOI: 10.3390/healthcare12050549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need for hospitals to expand reactive and proactive clinical risk management programs is easily understood, and artificial intelligence fits well in this context. This systematic review aims to investigate the state of the art regarding the impact of AI on clinical risk management processes. To simplify the analysis of the review outcomes and to motivate future standardized comparisons with any subsequent studies, the findings of the present review will be grouped according to the possibility of applying AI in the prevention of the different incident type groups as defined by the ICPS. MATERIALS AND METHODS On 3 November 2023, a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was carried out using the SCOPUS and Medline (via PubMed) databases. A total of 297 articles were identified. After the selection process, 36 articles were included in the present systematic review. RESULTS AND DISCUSSION The studies included in this review allowed for the identification of three main "incident type" domains: clinical process, healthcare-associated infection, and medication. Another relevant application of AI in clinical risk management concerns the topic of incident reporting. CONCLUSIONS This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification.
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Affiliation(s)
- Michela Ferrara
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, Italy; (M.F.); (N.D.F.); (P.F.)
| | - Giuseppe Bertozzi
- Complex Intercompany Structure of Forensic Medicine, 85100 Potenza, Italy;
| | - Nicola Di Fazio
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, Italy; (M.F.); (N.D.F.); (P.F.)
| | - Isabella Aquila
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy;
| | - Aldo Di Fazio
- Regional Hospital “San Carlo”, 85100 Potenza, Italy;
| | - Aniello Maiese
- Department of Surgical Pathology, Medical, Molecular and Critical Area, Institute of Legal Medicine, University of Pisa, 56126 Pisa, Italy;
| | - Gianpietro Volonnino
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, Italy; (M.F.); (N.D.F.); (P.F.)
| | - Paola Frati
- Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00161 Rome, Italy; (M.F.); (N.D.F.); (P.F.)
| | - Raffaele La Russa
- Department of Clinical Medicine, Public Health, Life and Environment Science, University of L’Aquila, 67100 L’Aquila, Italy;
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You YS, Lin YS. A Novel Two-Stage Induced Deep Learning System for Classifying Similar Drugs with Diverse Packaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:7275. [PMID: 37631811 PMCID: PMC10459418 DOI: 10.3390/s23167275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/01/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023]
Abstract
Dispensing errors play a crucial role in various medical errors, unfortunately emerging as the third leading cause of death in the United States. This alarming statistic has spurred the World Health Organization (WHO) into action, leading to the initiation of the Medication Without Harm Campaign. The primary objective of this campaign is to prevent dispensing errors from occurring and ensure patient safety. Due to the rapid development of deep learning technology, there has been a significant increase in the development of automatic dispensing systems based on deep learning classification to avoid dispensing errors. However, most previous studies have focused on developing deep learning classification systems for unpackaged pills or drugs with the same type of packaging. However, in the actual dispensing process, thousands of similar drugs with diverse packaging within a healthcare facility greatly increase the risk of dispensing errors. In this study, we proposed a novel two-stage induced deep learning (TSIDL)-based system to classify similar drugs with diverse packaging efficiently. The results demonstrate that the proposed TSIDL method outperforms state-of-the-art CNN models in all classification metrics. It achieved a state-of-the-art classification accuracy of 99.39%. Moreover, this study also demonstrated that the TSIDL method achieved an inference time of only 3.12 ms per image. These results highlight the potential of real-time classification for similar drugs with diverse packaging and their applications in future dispensing systems, which can prevent dispensing errors from occurring and ensure patient safety efficiently.
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Affiliation(s)
- Yu-Sin You
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan;
- Department of Pharmacy, Lotung Poh-Ai Hospital, Yilan 265, Taiwan
| | - Yu-Shiang Lin
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan;
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Ranchon F, Chanoine S, Lambert-Lacroix S, Bosson JL, Moreau-Gaudry A, Bedouch P. Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review. Int J Med Inform 2023; 172:104983. [PMID: 36724730 DOI: 10.1016/j.ijmedinf.2022.104983] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/15/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Artificial Intelligence (AI) offers potential opportunities to optimize clinical pharmacy services in community or hospital settings. The objective of this systematic literature review was to identify and analyse quantitative studies using or integrating AI for clinical pharmacy services. MATERIALS AND METHODS A systematic review was conducted using PubMed/Medline and Web of Science databases, including all articles published from 2000 to December 2021. Included studies had to involve pharmacists in the development or use of AI-powered apps and tools.. RESULTS 19 studies using AI for clinical pharmacy services were included in this review. 12 out of 19 articles (63.1%) were published in 2020 or 2021. Various methodologies of AI were used, mainly machine learning techniques and subsets (natural language processing and deep learning). The datasets used to train the models were mainly extracted from electronic medical records (6 studies, 32%). Among clinical pharmacy services, medication order review was the service most targeted by AI-powered apps and tools (9 studies), followed by health product dispensing (4 studies), pharmaceutical interviews and therapeutic education (2 studies). The development of these tools mainly involved hospital pharmacists (12/19 studies). DISCUSSION AND CONCLUSION The development of AI-powered apps and tools for clinical pharmacy services is just beginning. Pharmacists need to keep abreast of these developments in order to position themselves optimally while maintaining their human relationships with healthcare teams and patients. Significant efforts have to be made, in collaboration with data scientists, to better assess whether AI-powered apps and tools bring value to clinical pharmacy services in real practice.
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Affiliation(s)
- Florence Ranchon
- CNRS, TIMC UMR5525, MESP, Université Grenoble Alpes, F-38041 Grenoble, France; Hospices Civils de Lyon, Hôpital Lyon Sud, unité de pharmacie clinique oncologique, Pierre-Bénite, France; Université Lyon-1, EA 3738 CICLY, Oullins cedex F-69921, France.
| | - Sébastien Chanoine
- CNRS, TIMC UMR5525, MESP, Université Grenoble Alpes, F-38041 Grenoble, France; Pôle Pharmacie, CHU Grenoble Alpes, F-38043 Grenoble, France; Université Grenoble Alpes, Faculté de Pharmacie, F-38041 Grenoble, France
| | | | - Jean-Luc Bosson
- CNRS, TIMC UMR5525, MESP, Université Grenoble Alpes, F-38041 Grenoble, France
| | | | - Pierrick Bedouch
- CNRS, TIMC UMR5525, MESP, Université Grenoble Alpes, F-38041 Grenoble, France; Pôle Pharmacie, CHU Grenoble Alpes, F-38043 Grenoble, France; Université Grenoble Alpes, Faculté de Pharmacie, F-38041 Grenoble, France
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Pakuła A, Żołnowski W, Paśko S, Kursa O, Marć P, Jaroszewicz LR. Multispectral Portable Fibre-Optic Reflectometer for the Classification of the Origin of Chicken Eggshells in the Case of Mycoplasma synoviae Infections. SENSORS (BASEL, SWITZERLAND) 2022; 22:8690. [PMID: 36433286 PMCID: PMC9692302 DOI: 10.3390/s22228690] [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: 10/14/2022] [Revised: 11/04/2022] [Accepted: 11/09/2022] [Indexed: 06/16/2023]
Abstract
The proper classification of the origins of food products is a crucial issue all over the world nowadays. In this paper, the authors present a device-a multispectral portable fibre-optic reflectometer and signal processing patch-together with a machine-learning algorithm for the classification of the origins of chicken eggshells in the case of Mycoplasma synoviae infection. The sensor device was developed based on previous studies with a continuous spectrum in transmittance and selected spectral lines in reflectance. In the described case, the sensor is based on the integration of reflected spectral data from short spectral bands from the VIS and NIR region, which are produced by single-colour LEDs and introduced to the sample via a fibre bundle. The measurement is carried out in a sequence, and the reflected signal is pre-processed to be put in the machine learning algorithm. The support vector machine algorithm is used together with three different types of data normalization. The obtained results of the F-score factor for classification of the origins of samples show that the percentages of eggs coming from Mycoplasma synoviae infected hens are up to 87% for white and 96% for brown eggshells.
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Affiliation(s)
- Anna Pakuła
- Institute of Micromechanics and Photonics, Warsaw University of Technology, Św. A. Boboli 8, 02-525 Warsaw, Poland
- Faculty of New Technologies and Chemistry, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Wojciech Żołnowski
- Institute of Optoelectronics, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Sławomir Paśko
- Institute of Micromechanics and Photonics, Warsaw University of Technology, Św. A. Boboli 8, 02-525 Warsaw, Poland
| | - Olimpia Kursa
- Department of Poultry Diseases, National Veterinary Research Institute, Al. Partyzantów 57, 24-100 Puławy, Poland
| | - Paweł Marć
- Faculty of New Technologies and Chemistry, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Leszek R. Jaroszewicz
- Faculty of New Technologies and Chemistry, Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
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Chien TY, Ting HW, Chen CF, Yang CZ, Chen CY. A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center. Int J Med Sci 2022; 19:1049-1055. [PMID: 35813300 PMCID: PMC9254376 DOI: 10.7150/ijms.71341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 05/19/2022] [Indexed: 11/05/2022] Open
Abstract
Background: Diabetes mellitus (DM) is a major public health problem worldwide. It involves dysfunction of blood sugar regulation resulting from insulin resistance, inadequate insulin secretion, or excessive glucagon secretion. Methods: This study collated 971,401 drug usage records of 51,009 DM patients. These data include patient identification code, age, gender, outpatient visiting dates, visiting code, medication features (included items, doses, and frequencies of drugs), HbA1c results, and testing time. We apply a random forest (RF) model for feature selection and implement a regression model with the bidirectional long short-term memory (Bi-LSTM) deep learning architecture. Finally, we use the root mean square error (RMSE) as the evaluation index for the prediction model. Results: After data cleaning, the data included 8,729 male and 9,115 female cases. Metformin was the most important feature suggested by the RF model, followed by glimepiride, acarbose, pioglitazone, glibenclamide, gliclazide, repaglinide, nateglinide, sitagliptin, and vildagliptin. The model performed better with the past two seasons in the training data than with additional seasons. Further, the Bi-LSTM architecture model performed better than support vector machines (SVMs). Discussion & Conclusion: This study found that Bi-LSTM models is a well kernel in a CDSS which help physicians' decision-making, and the increasing the number of seasons will negative impact the performance. In addition, this study found that the most important drug is metformin, which is recommended as first-line treatment OHA in various situations for DM patients.
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Affiliation(s)
- Ting-Ying Chien
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan.,Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan City, Taiwan.,Graduated Program in Biomedical Informatics, Yuan Ze University, Taoyuan City, Taiwan
| | - Hsien-Wei Ting
- Graduated Program in Biomedical Informatics, Yuan Ze University, Taoyuan City, Taiwan.,Department of Neurosurgery, Taipei Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan
| | - Chih-Fang Chen
- Department of Pharmacy, MacKay Memorial Hospital, Taipei City, Taiwan
| | - Cheng-Zen Yang
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan.,Graduated Program in Biomedical Informatics, Yuan Ze University, Taoyuan City, Taiwan
| | - Chong-Yi Chen
- Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan
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Lee S, Shin J, Kim HS, Lee MJ, Yoon JM, Lee S, Kim Y, Kim JY, Lee S. Hybrid Method Incorporating a Rule-Based Approach and Deep Learning for Prescription Error Prediction. Drug Saf 2021; 45:27-35. [PMID: 34766251 DOI: 10.1007/s40264-021-01123-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Recently, automated detection has been a new approach to address the risks posed by prescribing errors. This study focused on prescription errors and utilized real medical data to supplement the Drug Utilization Review (DUR)-based rules, the current prescription error detection method. We developed a new hybrid method through artificial intelligence for prescription error prediction by utilizing actual detection accuracy improvement to reduce 'warning fatigue' for doctors and improve medical care quality. OBJECT This study was conducted in the Department of Pediatrics, targeting children sensitive to drugs to develop a prescription error detection system. Based on the DUR prescription history, 15,281 patient-level observations of children from Konyang University Hospital (KYUH)'s common data model (CDM) and DUR were collected and analyzed retrospectively. METHOD Among the CDM data, inspection information was interlocked with DUR and reflected as standard information for model development; this included outpatient prescriptions from January 1 to December 31, 2018. Through consultation with pediatric clinicians, rule definitions and model development were conducted for 35 drugs, with 137,802 normal and 1609 prescription errors. RESULTS We developed a novel hybrid method of error detection in the form of an advanced rule-based deep neural network (ARDNN), which showed the expected performance (precision: 72.86, recall: 81.01, F1 score: 76.72) and reduced alarm pop-up alert fatigue to below 10%. We also created an ARDNN-based comprehensive dashboard that allows doctors to monitor prescription errors with alarm pop-ups when prescribing medications. CONCLUSION These results can advance the existing rule-based model by developing a prescription error detection model using deep learning. This method can improve overall medical efficiency and service quality by reducing doctors' fatigue.
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Affiliation(s)
- Seunghee Lee
- Health Care Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea
| | | | | | | | - Jung Min Yoon
- Department of Pediatrics, Konyang University Hospital, Daejeon, Republic of Korea
| | - Sohee Lee
- Department of Biomedical Engineering, Konyang University, Daejeon, Republic of Korea
| | - Yongsuk Kim
- Department of Medical Artificial Intelligence, Konyang University, Daejeon, Republic of Korea
| | - Jong-Yeup Kim
- Health Care Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea.
- Departments of Biomedical Informatics, College of Medicine, Konyang University, 158 Gwanjeodong-ro, Seo-gu, Daejeon, 35365, Republic of Korea.
| | - Suehyun Lee
- Health Care Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea.
- Departments of Biomedical Informatics, College of Medicine, Konyang University, 158 Gwanjeodong-ro, Seo-gu, Daejeon, 35365, Republic of Korea.
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Li Y, Guo J, Yang P. Developing an Image-Based Deep Learning Framework for Automatic Scoring of The Pentagon Drawing Test. J Alzheimers Dis 2021; 85:129-139. [PMID: 34776440 DOI: 10.3233/jad-210714] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND The Pentagon Drawing Test (PDT) is a common assessment for visuospatial function. Evaluating the PDT by artificial intelligence can improve efficiency and reliability in the big data era. This study aimed to develop a deep learning (DL) framework for automatic scoring of the PDT based on image data. METHODS A total of 823 PDT photos were retrospectively collected and preprocessed into black-and-white, square-shape images. Stratified fivefold cross-validation was applied for training and testing. Two strategies based on convolutional neural networks were compared. The first strategy was to perform an image classification task using supervised transfer learning. The second strategy was designed with an object detection model for recognizing the geometric shapes in the figure, followed by a predetermined algorithm to score based on their classes and positions. RESULTS On average, the first framework demonstrated 62%accuracy, 62%recall, 65%precision, 63%specificity, and 0.72 area under the receiver operating characteristic curve. This performance was substantially outperformed by the second framework, with averages of 94%, 95%, 93%, 93%, and 0.95, respectively. CONCLUSION An image-based DL framework based on the object detection approach may be clinically applicable for automatic scoring of the PDT with high efficiency and reliability. With a limited sample size, transfer learning should be used with caution if the new images are distinct from the previous training data. Partitioning the problem-solving workflow into multiple simple tasks should facilitate model selection, improve performance, and allow comprehensible logic of the DL framework.
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Affiliation(s)
- Yike Li
- Department of Otolaryngology-Head and Neck Surgery, Bill Wilkerson Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jiajie Guo
- State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Peikai Yang
- Guangdong Yunjian Intelligent Technology Co. Ltd., Guangzhou, Guangdong, China
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Deep Learning for Classifying Physical Activities from Accelerometer Data. SENSORS 2021; 21:s21165564. [PMID: 34451005 PMCID: PMC8402311 DOI: 10.3390/s21165564] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/17/2022]
Abstract
Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment.
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