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Chen X, Cui J, Liu Z, Wang Y, Li M, Zhang J, Pan S, Wang M, Bao C, Wei Q. Polyacrylamide/sodium alginate/sodium chloride photochromic hydrogel with high conductivity, anti-freezing property and fast response for information storage and electronic skin. Int J Biol Macromol 2024; 268:131972. [PMID: 38697436 DOI: 10.1016/j.ijbiomac.2024.131972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/07/2024] [Accepted: 04/28/2024] [Indexed: 05/05/2024]
Abstract
Photochromic hydrogels have promising prospects in areas such as wearable device, information encryption technology, optoelectronic display technology, and electronic skin. However, there are strict requirements for the properties of photochromic hydrogels in practical engineering applications, especially in some extreme application environments. The preparation of photochromic hydrogels with high transparency, high toughness, fast response, colour reversibility, excellent electrical conductivity, and anti-freezing property remains a challenge. In this study, a novel photochromic hydrogel (PAAm/SA/NaCl-Mo7) was prepared by loading ammonium molybdate (Mo7) and sodium chloride (NaCl) into a dual-network hydrogel of polyacrylamide (PAAm) and sodium alginate (SA) using a simple one-pot method. PAAm/SA/NaCl-Mo7 hydrogel has excellent conductivity (175.9 S/cm), water retention capacity and anti-freezing properties, which can work normally at a low temperature of -28.4 °C. In addition, the prepared PAAm/SA/NaCl-Mo7 hydrogel exhibits fast response (<15 s), high transparency (>70 %), good toughness (maximum elongation up to 1500 %), good cyclic compression properties at high compressive strains (60 %), good biocompatibility (78.5 %), stable reversible discolouration and excellent sensing properties, which can be used for photoelectric display, information storage and motion monitoring. This work provides a new inspiration for the development of flexible electronic skin devices.
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Affiliation(s)
- Xiaohu Chen
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China
| | - Jiashu Cui
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China
| | - Zhisheng Liu
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China
| | - Yanen Wang
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China.
| | - Mingyang Li
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China
| | - Juan Zhang
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China
| | - Siyu Pan
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China
| | - Mengjie Wang
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China
| | - Chengwei Bao
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China
| | - Qinghua Wei
- Department of Indurstry and Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China; Bio-additive manufacturing university-enterprise joint research center of Shaanxi Province, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, PR China.
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El-Helaly M. Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks. LA MEDICINA DEL LAVORO 2024; 115:e2024014. [PMID: 38686574 PMCID: PMC11181216 DOI: 10.23749/mdl.v115i2.15835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024]
Abstract
This paper discusses the impact of artificial intelligence (AI) on occupational health and safety. Although the integration of AI into the field of occupational health and safety is still in its early stages, it has numerous applications in the workplace. Some of these applications offer numerous benefits for the health and safety of workers, such as continuous monitoring of workers' health and safety and the workplace environment through wearable devices and sensors. However, AI might have negative impacts in the workplace, such as ethical worries and data privacy concerns. To maximize the benefits and minimize the drawbacks of AI in the workplace, certain measures should be applied, such as training for both employers and employees and setting policies and guidelines regulating the integration of AI in the workplace.
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Affiliation(s)
- Mohamed El-Helaly
- Occupational and Environmental Medicine, Faculty of Medicine, Mansoura University, Mansoura City, Egypt
- Faculty of Medicine, New Mansoura University, New Mansoura City, Egypt
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Figueira V, Silva S, Costa I, Campos B, Salgado J, Pinho L, Freitas M, Carvalho P, Marques J, Pinho F. Wearables for Monitoring and Postural Feedback in the Work Context: A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:1341. [PMID: 38400498 PMCID: PMC10893004 DOI: 10.3390/s24041341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
Wearables offer a promising solution for simultaneous posture monitoring and/or corrective feedback. The main objective was to identify, synthesise, and characterise the wearables used in the workplace to monitor and postural feedback to workers. The PRISMA-ScR guidelines were followed. Studies were included between 1 January 2000 and 22 March 2023 in Spanish, French, English, and Portuguese without geographical restriction. The databases selected for the research were PubMed®, Web of Science®, Scopus®, and Google Scholar®. Qualitative studies, theses, reviews, and meta-analyses were excluded. Twelve studies were included, involving a total of 304 workers, mostly health professionals (n = 8). The remaining studies covered workers in the industry (n = 2), in the construction (n = 1), and welders (n = 1). For assessment purposes, most studies used one (n = 5) or two sensors (n = 5) characterised as accelerometers (n = 7), sixaxial (n = 2) or nonaxialinertial measurement units (n = 3). The most common source of feedback was the sensor itself (n = 6) or smartphones (n = 4). Haptic feedback was the most prevalent (n = 6), followed by auditory (n = 5) and visual (n = 3). Most studies employed prototype wearables emphasising kinematic variables of human movement. Healthcare professionals were the primary focus of the study along with haptic feedback that proved to be the most common and effective method for correcting posture during work activities.
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Affiliation(s)
- Vânia Figueira
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (S.S.); (I.C.); (B.C.); (J.S.); (L.P.); (M.F.); (J.M.); (F.P.)
- H2M—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL 4760-409 Vila Nova de Famalicão, Portugal
- Research Centre in Physical Activity, Health and Leisure, Faculty of Sport, University of Porto, Rua Dr. Plácido da Costa, 91, 4200-450 Porto, Portugal
| | - Sandra Silva
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (S.S.); (I.C.); (B.C.); (J.S.); (L.P.); (M.F.); (J.M.); (F.P.)
- H2M—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL 4760-409 Vila Nova de Famalicão, Portugal
- School of Health Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
- Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Inês Costa
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (S.S.); (I.C.); (B.C.); (J.S.); (L.P.); (M.F.); (J.M.); (F.P.)
| | - Bruna Campos
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (S.S.); (I.C.); (B.C.); (J.S.); (L.P.); (M.F.); (J.M.); (F.P.)
| | - João Salgado
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (S.S.); (I.C.); (B.C.); (J.S.); (L.P.); (M.F.); (J.M.); (F.P.)
| | - Liliana Pinho
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (S.S.); (I.C.); (B.C.); (J.S.); (L.P.); (M.F.); (J.M.); (F.P.)
- H2M—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL 4760-409 Vila Nova de Famalicão, Portugal
- Research Centre in Physical Activity, Health and Leisure, Faculty of Sport, University of Porto, Rua Dr. Plácido da Costa, 91, 4200-450 Porto, Portugal
- Center for Rehabilitation Research (Cir), R. Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal
| | - Marta Freitas
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (S.S.); (I.C.); (B.C.); (J.S.); (L.P.); (M.F.); (J.M.); (F.P.)
- H2M—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL 4760-409 Vila Nova de Famalicão, Portugal
- Research Centre in Physical Activity, Health and Leisure, Faculty of Sport, University of Porto, Rua Dr. Plácido da Costa, 91, 4200-450 Porto, Portugal
- Center for Rehabilitation Research (Cir), R. Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal
| | - Paulo Carvalho
- Center for Translational Health and Medical Biotechnology Research, School of Health, Polytechnic Institute of Porto, 4200-072 Porto, Portugal;
| | - João Marques
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (S.S.); (I.C.); (B.C.); (J.S.); (L.P.); (M.F.); (J.M.); (F.P.)
- H2M—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL 4760-409 Vila Nova de Famalicão, Portugal
| | - Francisco Pinho
- Escola Superior de Saúde do Vale do Ave, Cooperativa de Ensino Superior Politécnico e Universitário, Rua José António Vidal, 81, 4760-409 Vila Nova de Famalicão, Portugal; (S.S.); (I.C.); (B.C.); (J.S.); (L.P.); (M.F.); (J.M.); (F.P.)
- H2M—Health and Human Movement Unit, Polytechnic University of Health, Cooperativa de Ensino Superior Politécnico e Universitário, CRL 4760-409 Vila Nova de Famalicão, Portugal
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García-Luna MA, Ruiz-Fernández D, Tortosa-Martínez J, Manchado C, García-Jaén M, Cortell-Tormo JM. Transparency as a Means to Analyse the Impact of Inertial Sensors on Users during the Occupational Ergonomic Assessment: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:298. [PMID: 38203160 PMCID: PMC10781389 DOI: 10.3390/s24010298] [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: 11/14/2023] [Revised: 12/19/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
The literature has yielded promising data over the past decade regarding the use of inertial sensors for the analysis of occupational ergonomics. However, despite their significant advantages (e.g., portability, lightness, low cost, etc.), their widespread implementation in the actual workplace has not yet been realized, possibly due to their discomfort or potential alteration of the worker's behaviour. This systematic review has two main objectives: (i) to synthesize and evaluate studies that have employed inertial sensors in ergonomic analysis based on the RULA method; and (ii) to propose an evaluation system for the transparency of this technology to the user as a potential factor that could influence the behaviour and/or movements of the worker. A search was conducted on the Web of Science and Scopus databases. The studies were summarized and categorized based on the type of industry, objective, type and number of sensors used, body parts analysed, combination (or not) with other technologies, real or controlled environment, and transparency. A total of 17 studies were included in this review. The Xsens MVN system was the most widely used in this review, and the majority of studies were classified with a moderate level of transparency. It is noteworthy, however, that there is a limited and worrisome number of studies conducted in uncontrolled real environments.
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Affiliation(s)
- Marco A. García-Luna
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
| | - Daniel Ruiz-Fernández
- Department of Computer Science and Technology, University of Alicante, 03690 Alicante, Spain;
| | - Juan Tortosa-Martínez
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
| | - Carmen Manchado
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
| | - Miguel García-Jaén
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
| | - Juan M. Cortell-Tormo
- Department of General and Specific Didactics, Faculty of Education, University of Alicante, 03690 Alicante, Spain; (J.T.-M.); (C.M.); (M.G.-J.); (J.M.C.-T.)
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Donisi L, Jacob D, Guerrini L, Prisco G, Esposito F, Cesarelli M, Amato F, Gargiulo P. sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings. Bioengineering (Basel) 2023; 10:1103. [PMID: 37760205 PMCID: PMC10525808 DOI: 10.3390/bioengineering10091103] [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: 07/24/2023] [Revised: 09/12/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology-based on wearable sensors and artificial intelligence-to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.
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Affiliation(s)
- Leandro Donisi
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 80138 Naples, Italy;
- The Institute of Biomedical and Neural Engineering, School of Science and Engineering, Reykjavik University, 102 Reykjavik, Iceland; (D.J.); (L.G.); (P.G.)
| | - Deborah Jacob
- The Institute of Biomedical and Neural Engineering, School of Science and Engineering, Reykjavik University, 102 Reykjavik, Iceland; (D.J.); (L.G.); (P.G.)
| | - Lorena Guerrini
- The Institute of Biomedical and Neural Engineering, School of Science and Engineering, Reykjavik University, 102 Reykjavik, Iceland; (D.J.); (L.G.); (P.G.)
- Department of Engineering, University of Campania Luigi Vanvitelli, 81031 Aversa, Italy
| | - Giuseppe Prisco
- Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy;
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Mario Cesarelli
- Department of Engineering, University of Sannio, 82100 Benevento, Italy;
| | - Francesco Amato
- Department of Information Technology and Electrical Engineering, University of Naples Federico II, 80125 Naples, Italy;
| | - Paolo Gargiulo
- The Institute of Biomedical and Neural Engineering, School of Science and Engineering, Reykjavik University, 102 Reykjavik, Iceland; (D.J.); (L.G.); (P.G.)
- Department of Science, Landspitali University Hospital, 102 Reykjavik, Iceland
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Peksa J, Mamchur D. State-of-the-Art on Brain-Computer Interface Technology. SENSORS (BASEL, SWITZERLAND) 2023; 23:6001. [PMID: 37447849 DOI: 10.3390/s23136001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
This paper provides a comprehensive overview of the state-of-the-art in brain-computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.
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Affiliation(s)
- Janis Peksa
- Department of Information Technologies, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia
- Institute of Information Technology, Riga Technical University, Kalku Street 1, LV-1658 Riga, Latvia
| | - Dmytro Mamchur
- Department of Information Technologies, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia
- Computer Engineering and Electronics Department, Kremenchuk Mykhailo Ostrohradskyi National University, Pershotravneva 20, 39600 Kremenchuk, Ukraine
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Michaud F, Pazos R, Lugrís U, Cuadrado J. The Use of Wearable Inertial Sensors and Workplace-Based Exercises to Reduce Lateral Epicondylitis in the Workstation of a Textile Logistics Center. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115116. [PMID: 37299843 DOI: 10.3390/s23115116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/19/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023]
Abstract
People whose jobs involve repetitive motions of the wrist and forearm can suffer from lateral epicondylitis, which is a significant burden on both the individual and the employer due to treatment costs, reduced productivity, and work absenteeism. This paper describes an ergonomic intervention to reduce lateral epicondylitis in the workstation of a textile logistics center. The intervention includes workplace-based exercise programs, evaluation of risk factors, and movement correction. An injury- and subject-specific score was calculated from the motion captured with wearable inertial sensors at the workplace to evaluate the risk factors of 93 workers. Then, a new working movement was adapted to the workplace, which limited the observed risk factors and took into account the subject-specific physical abilities. The movement was taught to the workers during personalized sessions. The risk factors of 27 workers were evaluated again after the intervention to validate the effectiveness of the movement correction. In addition, active warm-up and stretching programs were introduced as part of the workday to promote muscle endurance and improve resistance to repetitive stress. The present strategy offered good results at low cost, without any physical modification of the workplace and without any detriment to productivity.
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Affiliation(s)
- Florian Michaud
- Laboratory of Mechanical Engineering, Campus Industrial de Ferrol, Universidade da Coruña, 15403 Ferrol, Spain
| | | | - Urbano Lugrís
- Laboratory of Mechanical Engineering, Campus Industrial de Ferrol, Universidade da Coruña, 15403 Ferrol, Spain
| | - Javier Cuadrado
- Laboratory of Mechanical Engineering, Campus Industrial de Ferrol, Universidade da Coruña, 15403 Ferrol, Spain
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Leone A, Rescio G, Caroppo A, Siciliano P, Manni A. Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation. SENSORS (BASEL, SWITZERLAND) 2023; 23:1039. [PMID: 36679839 PMCID: PMC9865298 DOI: 10.3390/s23021039] [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: 12/21/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important to develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, to enhance the level of usability and promote their deployment. With this purpose an automatic tri-axial accelerometer-based system for postural recognition has been developed, useful in detecting potential inappropriate behavioral habits for the elderly. Systems in the literature and on the market for this type of analysis mostly use personal computers with high computing resources, which are not easily portable and have high power consumption. To overcome these limitations, a real-time posture recognition Machine Learning algorithm was developed and optimized that could perform highly on platforms with low computational capacity and power consumption. The software was integrated and tested on two low-cost embedded platform (Raspberry Pi 4 and Odroid N2+). The experimentation stage was performed on various Machine Learning pre-trained classifiers using data of seven elderly users. The preliminary results showed an activity classification accuracy of about 98% for the four analyzed postures (Standing, Sitting, Bending, and Lying down), with similar accuracy and a computational load as the state-of-the-art classifiers running on personal computers.
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