Unclean hand detection machine using vision sensor

Naim, Faradila; Jaafar, Rawaida; Arshad, Nurul Wahidah; Hamid, Rosyati; Razali, Mohd Najib

doi:10.1109/siecpc.2013.6550750

Cited by 7 publications

(10 citation statements)

References 1 publication

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“…No study was excluded if they met the inclusion criteria. Therefore, of the 213 studies, 89 (41.8%) fulfilled the eligibility criteria and were retained for review [ 25 , 29 - 116 ]. Figure 3 shows the process of searching for and selecting the studies included in the review.…”

Section: Resultsmentioning

confidence: 99%

“…All the 89 reviewed studies were published between 2009 and 2020, with 9 (10%) dated in or before 2010 [ 42 , 46 , 56 , 60 , 81 , 83 , 86 , 92 , 113 ], 38 (43%) dated between 2011 and 2015 [ 32 , 33 , 36 , 38 , 39 , 45 , 48 , 49 , 51 , 52 , 54 , 59 , 61 , 63 - 65 , 67 , 68 , 72 , 73 , 78 - 80 , 82 , 84 , 87 - 89 , 93 - 97 , 99 - 101 , 108 , 116 ], and 42 (47%) dated in or after 2016 [ 25 , 29 - 31 , 34 , 35 , 37 , 40 , 41 , 43 , 44 , 47 , 50 , 53 , 55 , 57 , 58 , 62 , 66 , 69 - 71 , 74 - 77 , 85 , 90 , 91 , 98 , 102 -…”

Section: Resultsmentioning

confidence: 99%

“…Of the 7 studies, 5 (71%) studies analyzed the collected images from both handwash and hand rub by measuring the surface coverage of fluorescent substances [ 112 - 116 ]. For hand rub, the hand rub quality was acceptable if all areas were bright without dark spots, therefore suggesting that all parts of the hand were covered homogeneously with disinfectant [ 22 ].…”

Section: Resultsmentioning

confidence: 99%

“…For handwash, as fluorescent substances contaminated hands in advance, the handwash quality was measured by the range of cleaned hand areas (dark areas). Approximately 60% (3/5) of studies applied specific threshold values or deep learning algorithms to measure handwash quality [ 114 - 116 ].…”

Section: Resultsmentioning

confidence: 99%

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Electronic Monitoring Systems for Hand Hygiene: Systematic Review of Technology

Wang¹,

Jiang²,

Yang³

et al. 2021

J Med Internet Res

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Background Hand hygiene is one of the most effective ways of preventing health care–associated infections and reducing their transmission. Owing to recent advances in sensing technologies, electronic hand hygiene monitoring systems have been integrated into the daily routines of health care workers to measure their hand hygiene compliance and quality. Objective This review aims to summarize the latest technologies adopted in electronic hand hygiene monitoring systems and discuss the capabilities and limitations of these systems. Methods A systematic search of PubMed, ACM Digital Library, and IEEE Xplore Digital Library was performed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were initially screened and assessed independently by the 2 authors, and disagreements between them were further summarized and resolved by discussion with the senior author. Results In total, 1035 publications were retrieved by the search queries; of the 1035 papers, 89 (8.60%) fulfilled the eligibility criteria and were retained for review. In summary, 73 studies used electronic monitoring systems to monitor hand hygiene compliance, including application-assisted direct observation (5/73, 7%), camera-assisted observation (10/73, 14%), sensor-assisted observation (29/73, 40%), and real-time locating system (32/73, 44%). A total of 21 studies evaluated hand hygiene quality, consisting of compliance with the World Health Organization 6-step hand hygiene techniques (14/21, 67%) and surface coverage or illumination reduction of fluorescent substances (7/21, 33%). Conclusions Electronic hand hygiene monitoring systems face issues of accuracy, data integration, privacy and confidentiality, usability, associated costs, and infrastructure improvements. Moreover, this review found that standardized measurement tools to evaluate system performance are lacking; thus, future research is needed to establish standardized metrics to measure system performance differences among electronic hand hygiene monitoring systems. Furthermore, with sensing technologies and algorithms continually advancing, more research is needed on their implementation to improve system performance and address other hand hygiene–related issues.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Electronic Monitoring Systems for Hand Hygiene: Systematic Review of Technology

Wang¹,

Jiang²,

Yang³

et al. 2021

J Med Internet Res

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“…The study of hand detection or hand gesture detection which has been reviewed in [1] can be implemented in some areas such as in education [2,3], robotics [4,5], healthcare application [6][7][8], interactive projector scree [9], human computer interaction [10,11] and banking [12]. In detecting hands most of researches used the image of hand with various different method.…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0: Hand Recognition on Assembly Supervision Process

Analia

Pratama

Susanto

2021

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In the assembly industry, the process of assembling components is very important in order to produce a quality product. Assembly of components should be carried out sequentially based on the standards set by the company. For companies that still operate the assembly process manually by employee, sometimes errors occur in the assembly process, which can affect the quality of production. In order to be carried out the assembly process according to the procedure, a system is needed that can detect employee hands when carrying out the assembly process automatically. This study proposes an artificial intelligence-based real-time employee hand detection system. This system will be the basis for the development of an automatic industrial product assembly process to welcome the Industry 4.0. To verify system performance, several experiments were carried out, such as; detecting the right and left hands of employees and detecting hands when using accessories or not. From the experimental results it can be concluded that the system is able to detect the right and left hands of employees well with the resulting FPS average of 15.4.

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