Virtual Keyboard:A Real-Time Hand Gesture Recognition-Based Character Input System Using LSTM and Mediapipe Holistic

In the digital age,non-touch communication technologies are reshaping human-device interactions and raising security concerns.A major challenge in current technology is the misinterpretation of gestures by sensors and cameras,often caused by environmental factors.This issue has spurred the need for advanced data processing methods to achieve more accurate gesture recognition and predictions.Our study presents a novel virtual keyboard allowing character input via distinct hand gestures,focusing on two key aspects:hand gesture recognition and character input mechanisms.We developed a novel model with LSTM and fully connected layers for enhanced sequential data processing and hand gesture recognition.We also integrated CNN,max-pooling,and dropout layers for improved spatial feature extraction.This model architecture processes both temporal and spatial aspects of hand gestures,using LSTM to extract complex patterns from frame sequences for a comprehensive understanding of input data.Our unique dataset,essential for training the model,includes 1,662 landmarks from dynamic hand gestures,33 postures,and 468 face landmarks,all captured in real-time using advanced pose estimation.The model demonstrated high accuracy,achieving 98.52%in hand gesture recognition and over 97%in character input across different scenarios.Its excellent performance in real-time testing underlines its practicality and effectiveness,marking a significant advancement in enhancing human-device interactions in the digital age.

Design and Implementation of Hand Gesture Detection System Using HM Model for Sign Language Recognition Development

Gesture detection is the primary and most significant step for sign language detection and sign language is the communication medium for people with speaking and hearing disabilities. This paper presents a novel method for dynamic hand gesture detection using Hidden Markov Models (HMMs) where we detect different English alphabet letters by tracing hand movements. The process involves skin color-based segmentation for hand isolation in video frames, followed by morphological operations to enhance image trajectories. Our system employs hand tracking and trajectory smoothing techniques, such as the Kalman filter, to monitor hand movements and refine gesture paths. Quantized sequences are then analyzed using the Baum-Welch Re-estimation Algorithm, an HMM-based approach. A maximum likelihood classifier is used to identify the most probable letter from the test sequences. Our method demonstrates significant improvements over traditional recognition techniques in real-time, automatic hand gesture recognition, particularly in its ability to distinguish complex gestures. The experimental results confirm the effectiveness of our approach in enhancing gesture-based sign language detection to alleviate the barrier between the deaf and hard-of-hearing community and general people.

An Adaptive Hand Exoskeleton for Teleoperation System

Teleoperation can assist people to complete various complex tasks in inaccessible or high-risk environments,in which a wearable hand exoskeleton is one of the key devices.Adequate adaptability would be available to enable the master hand exoskeleton to capture the motion of human fingers and reproduce the contact force between the slave hand and its object.This paper presents a novel finger exoskeleton based on the cascading four-link closed-loop kinematic chain.Each finger has an independent closed-loop kinematic chain,and the angle sensors are used to obtain the finger motion including the flexion/extension and the adduction/abduction.The cable tension is changed by the servo motor to transmit the contact force to the fingers in real time.Based on the finger exoskeleton,an adaptive hand exoskeleton is consequently developed.In addition,the hand exoskeleton is tested in a master-slave system.The experiment results show that the adaptive hand exoskeleton can be worn without any mechanical constraints,and the slave hand can follow the motions of each human finger.The accuracy and the real-time capability of the force reproduction are validated.The proposed adaptive hand exoskeleton can be employed as the master hand to remotely control the humanoid five-fingered dexterous slave hand,thus,enabling the teleoperation system to complete complex dexterous manipulation tasks.

An Efficient and Robust Hand Gesture Recognition System of Sign Language Employing Finetuned Inception-V3 and Efficientnet-B0 Network

Hand Gesture Recognition(HGR)is a promising research area with an extensive range of applications,such as surgery,video game techniques,and sign language translation,where sign language is a complicated structured form of hand gestures.The fundamental building blocks of structured expressions in sign language are the arrangement of the fingers,the orientation of the hand,and the hand’s position concerning the body.The importance of HGR has increased due to the increasing number of touchless applications and the rapid growth of the hearing-impaired population.Therefore,real-time HGR is one of the most effective interaction methods between computers and humans.Developing a user-free interface with good recognition performance should be the goal of real-time HGR systems.Nowadays,Convolutional Neural Network(CNN)shows great recognition rates for different image-level classification tasks.It is challenging to train deep CNN networks like VGG-16,VGG-19,Inception-v3,and Efficientnet-B0 from scratch because only some significant labeled image datasets are available for static hand gesture images.However,an efficient and robust hand gesture recognition system of sign language employing finetuned Inception-v3 and Efficientnet-Bo network is proposed to identify hand gestures using a comparative small HGR dataset.Experiments show that Inception-v3 achieved 90%accuracy and 0.93%precision,0.91%recall,and 0.90%f1-score,respectively,while EfficientNet-B0 achieved 99%accuracy and 0.98%,0.97%,0.98%,precision,recall,and f1-score respectively.

Intelligent Sign Multi-Language Real-Time Prediction System with Effective Data Preprocessing

A multidisciplinary approach for developing an intelligent sign multi-language recognition system to greatly enhance deaf-mute communication will be discussed and implemented. This involves designing a low-cost glove-based sensing system, collecting large and diverse datasets, preprocessing the data, and using efficient machine learning models. Furthermore, the glove is integrated with a user-friendly mobile application called “Life-sign” for this system. The main goal of this work is to minimize the processing time of machine learning classifiers while maintaining higher accuracy performance. This is achieved by using effective preprocessing algorithms to handle noisy and inconsistent data. Testing and iterating approaches have been applied to various classifiers to refine and improve their accuracy in the recognition process. Additionally, the Extra Trees (ET) classifier has been identified as the best algorithm, with results proving successful gesture prediction at an average accuracy of about 99.54%. A smart optimization feature has been implemented to control the size of data transferred via Bluetooth, allowing for fast recognition of consecutive gestures. Real-time performance has been measured through extensive experimental testing on various consecutive gestures, specifically referring to Arabic Sign Language (ArSL). The results have demonstrated that the system guarantees consecutive gesture recognition with a lower delay of 50 milliseconds.

Study on Bacteriostatic Properties of Piroctone Olamine in Commonly Used Surfactants Systems

Piroctone olamine(OCT) was used as the main bacteriostatic component, the inhibition of OCT in different kinds and mass concentrations of surfactants were studied. Six surfactants commonly used in personal care products, i.e. sodium laureth sulfate(AES), cocamidopropyl betaine(CAB 35), sodium lauroyl sarcosinate(LS-30), sodium lauroyl glutamate(ULS-30S), alkyl glycoside(APG), cocamide methyl MEA(CMMEA),were used. The results showed that the bacteriostatic of OCT decreased with the increase of AES, which was suggested ≤ 5%. OCT has good bacteriostatic performance in the systems of amino acid surfactants and high dosage of amphoteric surfactants, 5% LS 30 and ≥ 10% CAB 35 was recommended. High dosage of nonionic surfactant could interfere the bacteriostatic performance of OCT, the recommended dosage was ≤ 2%.In addition, OCT has good bacteriostatic performance against Staphylococcus aureus, Escherichia coli and Candida albicans when pH was controlled less than 5.5.

外源硒对蒲公英生长、生理特性及其硒含量的影响

为研究叶面喷施外源硒对蒲公英(Taraxacum mongolicum Hand.-Mazz.)生长和品质的影响,采用盆栽试验,分析叶面喷施不同浓度亚硒酸钠溶液(0、100、200、300、400μmol/L)对蒲公英生长、生理特性及其硒含量的影响。结果表明,随着喷施硒浓度的增大,蒲公英根长和鲜重等生长指标呈先升高后降低的趋势;与对照相比,施加200μmol/L亚硒酸钠可显著提高蒲公英光合作用速率和光化学反应效率,提高光合色素含量;施加200μmol/L亚硒酸钠时蒲公英叶片SOD和CAT活性最高,施加300μmol/L亚硒酸钠时POD活性最高(P<0.05)。施硒可有效提高蒲公英绿原酸、总黄酮和总多酚的含量,施加200μmol/L亚硒酸钠时总黄酮和总多酚的含量较对照分别提高49.73%、31.34%。施硒后蒲公英各部位硒含量显著提高,叶片和根系的硒含量随硒浓度的变化符合一元二次方程。蒲公英叶片对外源硒浓度的变化较敏感,是富硒的主要部位。研究认为,叶面喷施200μmol/L亚硒酸钠是培养富硒蒲公英的最佳浓度。

基于HAND的流域洪水淹没快速分析技术

快速分析计算流域洪水淹没,对于防洪具有重要作用,而基于水动力学模型的经典淹没算法在时效性方面存在不足,制约防洪“四预”功能实现和数字孪生流域建设。针对此,本文建立了基于HAND(Height Above Nearest Drainage)的形态淹没模型,用于快速分析流域洪水淹没情况,在乐安河流域上游进行了应用验证。结果表明,模型可在5秒内完成洪水淹没计算,对四场历史洪水和三场设计洪水淹没范围的总体模拟精度平均为0.95,达到水动力学模型的模拟精度;对淹没水深的模拟存在较大误差,平均误差为0.79 m。基于HAND的形态淹没模型可快速绘制全流域洪水淹没范围图,大幅提升影响预报和防洪预演的时效性水平。

子痫前期病人长链非编码RNA H19和长链非编码RNA HAND2-AS1表达水平与胰岛素抵抗的相关性研究

目的检测子痫前期病人胎盘组织、血清长链非编码RNA H19(LncRNA H19)与长链非编码RNAHAND2-AS1(Ln⁃cRNA HAND2-AS1)表达水平,并分析其与病人胰岛素抵抗(IR)的相关性。方法将2021年1—12月在唐山市妇幼保健院建卡的子痫前期孕妇105例作为子痫前期组,根据子痫前期孕妇严重程度分为轻度子痫前期组与重度子痫前期组,另选取同期孕周、年龄相符的健康孕妇97例作为对照组,收集所有孕妇身体质量指数(BMI)、孕周、收缩压、舒张压、空腹胰岛素(FINS)、空腹血糖(FBG)等一般资料,并计算稳态模型的IR指数(HOMA-IR);实时荧光定量PCR(qRT-PCR)法测定孕妇血清、胎盘组织中LncRNA H19,LncRNA HAND2-AS1水平;比较对照组与子痫前期组、轻度子痫前期组与重度子痫前期组间血清、胎盘组织LncRNA H19、LncRNA HAND2-AS1水平;Pearson法分析子痫前期孕妇血清、胎盘组织LncRNA H19、LncRNA HAND2-AS1与HOMA-IR的相关性。结果子痫前期组病人收缩压、舒张压高于对照组(P<0.05),且重度子痫前期病人收缩压、舒张压高于轻度子痫前期病人,孕周低于轻度子痫前期病人(P<0.05);与对照组相比,子痫前期组孕妇24 h尿蛋白、FBG[(5.84±0.97)mmol/L比(4.22±0.70)mmol/L]、FINS[(13.37±2.23)mU/L比(7.52±1.25)mU/L]、HOMI-IR水平(3.47±0.57比1.41±0.23)升高(P<0.05),且重度子痫前期孕妇24 h尿蛋白、FBG[(6.90±1.15)mmol/L比(5.24±0.85)mmol/L]、FINS[(13.97±2.32)mU/L比(13.05±2.17)mU/L]、HOMI-IR水平(4.27±0.71比3.03±0.50)高于轻度子痫前期孕妇(P<0.05);与对照组相比,子痫前期组孕妇血清、胎盘组织Ln⁃cRNA H19(2.52±1.42比1.01±0.15,3.75±0.62比1.02±0.17)与LncRNA HAND2-AS1水平(1.98±0.33比1.01±0.15,2.87±0.47比1.02±0.17)升高(P<0.05),且重度子痫前期孕妇血清、胎盘组织LncRNA H19(2.84±0.47比2.34±0.39,4.28±0.71比3.45±0.57)与LncRNA HAND2-AS1水平(2.59±0.43比1.63±0.27,3.56±0.59比2.49±0.41)高于轻度子痫前期孕妇(P<0.05);子痫前期病人血清与胎盘组织间LncRNA H19水平呈正相关(r=0.55,P<0.05),血清与胎盘组织间LncRNA HAND2-AS1水平呈正相关性(r=0.65,P<0.05)。子痫前期孕妇血清、胎盘组织LncRNA H19水平分别与HOMI-IR呈正相关(r=0.53、0.59,P<0.05);血清、胎盘组织LncRNA HAND2-AS1水平分别与HOMI-IR呈正相关(r=0.60、0.61,P<0.05)。结论子痫前期病人血清、胎盘组织LncRNA H19、LncRNA HAND2-AS1高表达,二者与IR密切相关,可能通过影响IR参与子痫前期发生发展。

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

基于隐喻与转喻的人体多义词hand的认知研究及其教学启示

人体是人类认知世界的起点,人体词的义项拓展严格遵循隐喻、转喻路径进行生成和演化。为帮助英语学习者更好地理解、记忆多义词,从隐喻映射和转喻映射角度出发,考察人体多义词hand的义项拓展路径及其类别。研究发现,hand的义项派生共有七种映射路径,分别为基本、抽象、人物、形貌、意动、计量、修饰,并探讨如何基于人体词分析其他多义词的义项拓展,尝试为二语词汇学习和教学提供思路。

NECAB family of neuronal calcium-binding proteins in health and disease

The N-terminal EF-hand calcium-binding proteins 1–3(NECAB1–3) constitute a family of predominantly neuronal proteins characterized by the presence of at least one EF-hand calcium-binding domain and a functionally less well characterized C-terminal antibiotic biosynthesis monooxygenase domain. All three family members were initially discovered due to their interactions with other proteins. NECAB1 associates with synaptotagmin-1, a critical neuronal protein involved in membrane trafficking and synaptic vesicle exocytosis. NECAB2 interacts with predominantly striatal G-protein-coupled receptors, while NECAB3 partners with amyloid-β A4 precursor protein-binding family A members 2 and 3, key regulators of amyloid-β production. This demonstrates the capacity of the family for interactions with various classes of proteins. NECAB proteins exhibit distinct subcellular localizations: NECAB1 is found in the nucleus and cytosol, NECAB2 resides in endosomes and the plasma membrane, and NECAB3 is present in the endoplasmic reticulum and Golgi apparatus. The antibiotic biosynthesis monooxygenase domain, an evolutionarily ancient component, is akin to atypical heme oxygenases in prokaryotes but is not wellcharacterized in vertebrates. Prokaryotic antibiotic biosynthesis monooxygenase domains typically form dimers, suggesting that calcium-mediated conformational changes in NECAB proteins may induce antibiotic biosynthesis monooxygenase domain dimerization, potentially activating some enzymatic properties. However, the substrate for this enzymatic activity remains uncertain. Alternatively, calcium-mediated conformational changes might influence protein interactions or the subcellular localization of NECAB proteins by controlling the availability of protein–protein interaction domains situated between the EF hands and the antibiotic biosynthesis monooxygenase domain. This review summarizes what is known about genomic organization, tissue expression, intracellular localization, interaction partners, and the physiological and pathophysiological role of the NECAB family.

Extraction of intersecting palm-vein and palmprint features for cancellable identity verification

A novel method based on the cross-modality intersecting features of the palm-vein and the palmprint is proposed for identity verification.Capitalising on the unique geometrical relationship between the two biometric modalities,the cross-modality intersecting points provides a stable set of features for identity verification.To facilitate flexibility in template changes,a template transformation is proposed.While maintaining non-invertibility,the template transformation allows transformation sizes beyond that offered by the con-ventional means.Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.

Tenosynovitis of hand: Causes and complications

Tenosynovitis represents a common clinical condition characterized by inflam-mation of the synovium that encases the tendon sheath.Although tenosynovities may be noted in any tendon in the body,extremities such as hand,and foot remain the sites of high predilection to acquire this condition.The predominant cause of this predilection rests in the intricate tendon arrangements in these extremities that permit fine motor actions.This editorial explores the common causes and the complications associated with this condition to improve the understanding of the readers of this common condition encountered in our everyday clinical practice.

Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

Surgical management of adult hand macrodactyly in a 49-year-old patient:A case report

BACKGROUND Macrodactyly is a rare congenital malformation characterized by an increase in the size of all structures of a digit,accounting for less than 1%of all congenital upper extremity conditions.CASE SUMMARY We report a case involving a 49-year-old woman who presented for the first time with untreated,radial-sided hand macrodactyly.We performed soft tissue debulking,amputation,median nerve neurotomy and coaptation,and carpal tunnel release.At the 6-year follow-up,no significant growth was observed in the bone or soft tissue of the affected area.CONCLUSION Tissue overgrowth in patients with progressive macrodactyly can continue and progress excessively with age.Median nerve neurotomy and coaptation play a crucial role in preventing recurrence of the deformity.

Porocarcinoma in a palm reconstructed with a full thickness skin graft: A case report

BACKGROUND Porocarcinoma is a rare type of skin cancer that originates from sweat gland tumors.It is an aggressive malignant skin cancer that is difficult to diagnose clinically owing to its rarity and similarity to squamous cell carcinoma(SCC).CASE SUMMARY This case involved a 92-year-old woman,a farmer by profession,presented with an exophytic and verrucous mass on her left palm that had formed 2 years prior and caused chronic pain and frequent bleeding.Initially,the patient was diagnosed with SCC using a punch biopsy;however,a repeat biopsy with addi-tional immunohistochemical tests was performed for porocarcinoma.Ultimately,the patient was diagnosed with porocarcinoma and reconstruction was planned using a full-thickness skin graft.After treatment,the range of motion of the palm was preserved,and the aesthetic outcome was favorable.At 6 mo of follow-up,the patient was satisfied with the outcome.CONCLUSION Porocarcinoma is commonly misdiagnosed as SCC;therefore,clinicians should consider porocarcinomas when evaluating mass-like lesions on the hands.

Effect analysis of applying high-quality service model to surgical nursing

BACKGROUND Surgical care of the hand plays a crucial role in the medical field,as problems with the hand can profoundly affect a patient's quality of life and function.In order to meet the needs of patients,improve patient satisfaction and improve treatment outcomes,high-quality service models have been introduced in the field of nursing.AIM To explore the effect analysis of applying high-quality service model to surgical nursing.METHODS We conducted a retrospective study of patients who underwent hand surgery at our hospital between 2019 and 2022,using a quality service model that included improved patient education,pain management,care team collaboration,and effective communication.Another group of patients received traditional care as a control group.We compared postoperative recovery,satisfaction,complication rate,and length of hospital stay between the two groups.Inferential statistics were used to compare the difference between the two groups by independent sample t test,Chi-square test and other methods to evaluate the effect of intervention measures.RESULTS Postoperative recovery time decreased from 17.8±2.3 d to 14.5±2.1 d,pain score decreased from 4.7±1.9 to 3.2±1.4,and hand function score increased from 78.4±7.1 to 88.5±6.2.In terms of patient satisfaction,the quality service model group scored 87.3±5.6 points,which was significantly higher than that of the traditional care group(74.6±6.3 points).At the same time,patients'understanding of medical information also improved from 6.9±1.4 to 8.6±1.2.In terms of postoperative complications,the application of the quality service model reduced the incidence of postoperative complications from 26%to 10%,the incidence of infection from 12%to 5%,and the incidence of bleeding from 10%to 3%.The reduction in these data indicates that the quality service model plays a positive role in reducing the risk of complications.In addition,the average hospital stay of patients in the quality service model group was shortened from 6.8±1.5 d to 5.2±1.3 d,and the hospitalization cost was also reduced from 2800±600 yuan to 2500±500 yuan.CONCLUSION Applying a quality service model to hand surgery care can significantly improve patient clinical outcomes,including faster recovery,less pain,greater satisfaction,and reduced complication rates.

Epidemiology of work-related hand and wrist injuries in a referral center: A descriptive study

BACKGROUND Occupational hand and wrist injuries(OHWIs)account for 25%of work-related accidents in low-and middle-income countries.In Colombia,more than 500000 occupational accidents occurred in 2021,and although the rate declined to less than 5%in 2020 and 2021,at least one in four accidents involved a hand or wrist injury.AIM To describe the OHWIs in workers seen at the emergency room at a second-level hospital in Colombia.METHODS An observational study was performed using data from workers who experienced OHWIs and attended a second-level hospital,between June,2020 and May,2021.The overall frequency of OHWIs,as well as their distribution by sociodemo-graphic,clinical,and occupational variables,are described.Furthermore,association patterns between sex,anatomical area(fingers,hand,wrist),and type of job were analyzed by correspondence analysis(CA).RESULTS There were 2.101 workers treated for occupational accidents,423(20.3%)were cases of OHWIs,which mainly affected men(93.9%)with a median age of 31 years and who worked mainly in mining(75.9%).OHWIs were more common in the right upper extremity(55.3%)and comprised different types of injuries,such as contusion(42.1%),laceration(27.9%),fracture(18.7%),and crush injury(15.6%).They primarily affected the phalanges(95.2%),especially those of the first finger(25.7%).The CAs showed associations between the injured anatomical area and the worker’s job that differed in men and women(explained variance>90%).CONCLUSION One out of five workers who suffered occupational accidents in Cundinamarca,Columbia had an OHWI,affecting mainly males employed in mining.This occupational profile is likely to lead to prolonged rehabilitation,and permanent functional limitations.Our results might be useful for adjusting preventive measures in cluster risk groups.