Landmark Recognition Research Articles

LiPro: displaying concurrent image viewing and imperceptible positioning using visible light

Visible light can be used to achieve effective human interaction with projectors. However, current visible light-based projectors for the target positioning either lose the image viewing function or cause abnormal luminance changes perceived by human eyes, which is not practical for real-world applications. Therefore, we propose a smart lighting-based projector system, LiPro, which can display imperceptible target positioning and any image viewing, simultaneously. In a nutshell, we propose what we believe to be a novel color space conversion modulation scheme to transmit the landmark information, which adds δ and −3δ color intensity value on the red (R) channel and blue (B) channel according to the YCbCr color space analysis, so that human eyes are imperceptible to light intensity changes. Then, we propose the dual-channel decision demodulation scheme, which demodulates the received light signals on both the R channel and B channel at the same time to decide and increase the correctness of the landmark recognition. In the end, we use off-the-shelf devices to implement LiPro to evaluate its performance, and results from extensive experiments show that our LiPro can achieve centimeter-level target positioning under any image viewing conditions.

Data Privacy and Security in Autonomous Connected Vehicles in Smart City Environment

A self-driving vehicle can navigate autonomously in smart cities without the need for human intervention. The emergence of Autonomous Connected Vehicles (ACVs) poses a substantial threat to public and passenger safety due to the possibility of cyber-attacks, which encompass remote hacking, manipulation of sensor data, and probable disablement or accidents. The sensors collect data to facilitate the network’s recognition of local landmarks, such as trees, curbs, pedestrians, signs, and traffic lights. ACVs gather vast amounts of data, encompassing the exact geographical coordinates of the vehicle, captured images, and signals received from various sensors. To create a fully autonomous system, it is imperative to intelligently integrate several technologies, such as sensors, communication, computation, machine learning (ML), data analytics, and other technologies. The primary issues in ACVs involve data privacy and security when instantaneously exchanging substantial volumes of data. This study investigates related data security and privacy research in ACVs using the Blockchain-enabled Federated Reinforcement Learning (BFRL) framework. This paper provides a literature review examining data security and privacy in ACVs and the BFRL framework that can be used to protect ACVs. This study presents the integration of FRL and Blockchain (BC) in the context of smart cities. Furthermore, the challenges and opportunities for future research on ACVs utilising BFRL frameworks are discussed.

Research and application discussion of cranial bone model preparation method based on three-dimensional reconstruction and 3D printing technology.

The aim of this study was to find an alternative method to meet traditional human anatomy teaching and clinical needs in order to solve the problem of cranial specimen attrition and specimen resource shortage due to long-term use. We performed a computed tomography (CT) scan of a well-preserved male cranial specimen and used Mimics 19.0 software for 3D reconstruction and cranial block separation. Subsequently, we compared the recognition ability of the processed cranial digital model with that of the 3D body digital model and used 3D printing to create the cranial model and compare it with the physical specimen. Twenty-two cranial bone block models were obtained, excluding the hyoid bone. Their 3D reconstructed digital models had better bony landmark recognition than the 3D body human digital models, and the differences between the 3D printed models and the physical specimens were minimal. In addition, only one stereolithography (STL) file was required to produce the cranial models, which facilitates repetitive printing at any time. By isolating cranial bone blocks through 3D reconstruction techniques and preparing high-quality cranial models in combination with 3D printing techniques, this study solves the problem of shortage of cranial teaching specimens for the sustainable development of clinical and medical schools.

Landmark-based guidance and cognitive saliency: Age-related benefits in spatial performance

Older adults have difficulties using a map, and the use of GPS systems seems beneficial to help them find their way in cities. The question arises however as to what extent the introduction of landmarks can be even more beneficial. While the visual saliency of landmarks has been well studied, the cognitive saliency of landmarks has received little attention. Our objective in this study was to determine if guiding young and older pedestrians with visually and cognitively salient landmarks inserted in a map or a GPS-like system improved spatial performances. Thirty-four young and 32 older adults navigated a virtual environment using a joystick. They were guided with a map or with GPS-like instructions, including either only visually or both visually and cognitively salient landmarks. After each itinerary, participants completed four tasks: landmark recognition and sequencing, direction recognition and sequencing. The results showed that the integration of visually and cognitively salient landmarks in a map or in GPS-like instructions improved the construction of landmark and route knowledge in both young and older participants. Results also suggested that age-related deficits in spatial memory could be reduced by using GPS-like instructions or by introducing cognitively salient landmarks, in a map or in a GPS-like system. This research opens several perspectives for research as well for the design of human-centred navigation aids.

Utilization of artificial intelligence in minimally invasive right adrenalectomy: recognition of anatomical landmarks with deep learning

Background The primary surgical approach for removing adrenal masses is minimally invasive adrenalectomy. Recognition of anatomical landmarks during surgery is critical for minimizing complications. Artificial intelligence-based tools can be utilized to create real-time navigation systems during laparoscopic and robotic right adrenalectomy. In this study, we aimed to develop deep learning models that can identify critical anatomical structures during minimally invasive right adrenalectomy. Methods In this experimental feasibility study, intraoperative videos of 20 patients who underwent minimally invasive right adrenalectomy in a tertiary care center between 2011 and 2023 were analyzed and used to develop an artificial intelligence-based anatomical landmark recognition system. Semantic segmentation of the liver, the inferior vena cava (IVC), and the right adrenal gland were performed. Fifty random images per patient during the dissection phase were extracted from videos. The experiments on the annotated images were performed on two state-of-the-art segmentation models named SwinUNETR and MedNeXt, which are transformer and convolutional neural network (CNN)-based segmentation architectures, respectively. Two loss function combinations, Dice-Cross Entropy and Dice-Focal Loss were experimented with for both of the models. The dataset was split into training and validation subsets with an 80:20 distribution on a patient basis in a 5-fold cross-validation approach. To introduce a sample variability to the dataset, strong-augmentation techniques were performed using intensity modifications and perspective transformations to represent different surgery environment scenarios. The models were evaluated by Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) which are widely used segmentation metrics. For pixelwise classification performance, accuracy, sensitivity and specificity metrics were calculated on the validation subset. Results Out of 20 videos, 1000 images were extracted, and the anatomical landmarks (liver, IVC, and right adrenal gland) were annotated. Randomly distributed 800 images and 200 images were selected for the training and validation subsets, respectively. Our benchmark results show that the utilization of Dice-Cross Entropy Loss with the transformer-based SwinUNETR model achieved 78.37%, whereas the CNN-based MedNeXt model reached a 77.09% mDSC score. Conversely, MedNeXt reaches a higher mIoU score of 63.71% than SwinUNETR by 62.10% on a three-region prediction task. Conclusion Artificial intelligence-based systems can predict anatomical landmarks with high performance in minimally invasive right adrenalectomy. Such tools can later be used to create real-time navigation systems during surgery in the near future.

Hand Glide: Gesture-Controlled Virtual Mouse with Voice Assistant

Abstract: Hand Glide presents a unified system that uses the MediaPipe library for accurate hand landmark recognition and categorization, enabling smooth integration of gesture-based control (GBC) and voice-controlled assistant (VCA) features. The system allows users to effortlessly translate hand movements into actions such as mouse manipulation, clicking, scrolling, and system parameter adjustments by providing distinct classes for hand gesture recognition (HandRecog) and execution (Controller), as well as a main class orchestrating camera input and gesture control. In addition, Hand Glide also has VCA functionality, which uses speech recognition and synthesis libraries and natural language processing (NLP) techniques. With these tools, Hand Glide can reply to greetings, perform tasks like Wikipedia searches and website openings, and converse with users through the GPT-2 language model. By merging these features, Hand Glide providesusers with an interactive and intuitive means of computer control, with broad applications in user interfaces, accessibility solutions, and immersive experiences

Data Protection and Privacy as a Fundamental Right - An In-depth Analysis of the European Union and India's Data Protection Legislation

In the digital age, the protection of personal data and the right to privacy have emerged as paramount concerns, necessitating robust legal frameworks to safeguard these fundamental human rights. This paper embarks on a comprehensive study of data protection laws in two distinct jurisdictions, the European Union (EU) and India. By examining the EU’s General Data Protection Regulation (GDPR), renowned for its stringent data protection standards, and juxtaposing it with India’s evolving legal framework, particularly in light of the Supreme Court’s landmark recognition of privacy as a fundamental right and the ongoing deliberations on the Digital Personal Data Protection Act (DPDPA), 2023, this research aims to unravel the complexities of legislating privacy in the digital era. Through a comparative analysis, the article highlights the similarities and divergences between the GDPR and the DPDP Act, evaluates the effectiveness of these frameworks in safeguarding privacy, and explores the challenges posed by technological advancements. Furthermore, it delves into the future of data protection laws. By integrating legal analysis with the theoretical underpinnings of privacy as a fundamental right, this research contributes to the global discourse on data protection, advocating for a dynamic legal landscape that aligns with the evolving nature of privacy threats in the digital world.

Improving Pedestrian Navigation in Urban Environment Using Augmented Reality and Landmark Recognition

Improving Pedestrian Navigation in Urban Environment Using Augmented Reality and Landmark Recognition

A rapid identification method for soft tissue markers of dentofacial deformities based on heatmap regression

ObjectiveThe purpose of this study was to construct a facial deformity dataset and a network model based on heatmap regression for the recognition of facial soft tissue landmarks to provide a basis for clinicians to perform cephalometric analysis of soft tissue.Materials and methodsA 34-point face marker detection model, the Back High-Resolution Network (BHR-Net), was constructed based on the heatmap regression algorithm, and a custom dataset of 1780 facial detection images for orthognathic surgery was collected. The mean normalized error (MNE) and 10% failure rate (FR10%) were used to evaluate the performance of BHR-Net, and a test set of 50 patients was used to verify the accuracy of the landmarks and their measurement indicators. The test results were subsequently validated in 30 patients.ResultsBoth the MNE and FR10% of BHR-Net were optimal compared with other models. In the test set (50 patients), the accuracy of the markers excluding the nose root was 86%, and the accuracy of the remaining markers reached 94%. In the model validation (30 patients), using the markers detected by BHR-Net, the diagnostic accuracy of doctors was 100% for Class II and III deformities, 100% for the oral angle plane, and 70% for maxillofacial asymmetric deformities.ConclusionsBHR-Net, a network model based on heatmap regression, can be used to effectively identify landmarks in maxillofacial multipose images, providing a reliable way for clinicians to perform cephalometric measurements of soft tissue objectively and quickly.

Development of deep learning framework for anatomical landmark detection and guided dissection line during laparoscopic cholecystectomy

BackgroundBile duct injuries during laparoscopic cholecystectomy can arise from misinterpretation of biliary anatomy, leading to dissection in improper areas. The integration of a deep learning framework into laparoscopic procedures offers the potential for real-time anatomical landmark recognition, ensuring accurate dissection. The objective of this study is to develop a deep learning framework that can precisely identify anatomical landmarks, including Rouviere's sulcus and the liver base of segment IV, and provide a guided dissection line during laparoscopic cholecystectomy. MethodsWe retrospectively collected 40 laparoscopic cholecystectomy videos and extracted 80 images form each video to establish the dataset. Three surgeons annotated the bounding boxes of anatomical landmarks on a total of 3200 images. The YOLOv7 model was trained to detect Rouviere's sulcus and the liver base of segment IV as anatomical landmarks. Additionally, the guided dissection line was generated between these two landmarks by the proposed algorithm. To evaluate the performance of the detection model, mean average precision (mAP), precision, and recall were calculated. Furthermore, the accuracy of the guided dissection line was evaluated by three surgeons. The performance of the detection model was compared to the scaled-YOLOv4 and YOLOv5 models. Finally, the proposed framework was deployed in the operating room for real-time detection and visualization. ResultsThe overall performance of the YOLOv7 model on validation set and testing set were 98.1 % and 91.3 %, respectively. Surgeons accepted the visualization of guide dissection line with a rate of 95.71 %. In the operating room, the well-trained model accurately identified the anatomical landmarks and generated the guided dissection line in real-time. ConclusionsThe proposed framework effectively identifies anatomical landmarks and generates a guided dissection line in real-time during laparoscopic cholecystectomy. This research underscores the potential of using deep learning models as computer-assisted tools in surgery, providing an assistant tool to accommodate with surgeons.

Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks.

Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to develop a fully automatic 2D registration system for augmented reality in ear surgery. CT-scans and corresponding oto-endoscopic videos were collected from 41 patients (58 ears) undergoing ear examination (vestibular schwannoma before surgery, profound hearing loss requiring cochlear implant, suspicion of perilymphatic fistula, contralateral ears in cases of unilateral chronic otitis media). Two to four images were selected from each case. For the training phase, data from patients (75% of the dataset) and 11 cadaveric specimens were used. Tympanic membranes and malleus handles were contoured on both video images and CT-scans by expert surgeons. The algorithm used a U-Net network for detecting the contours of the tympanic membrane and the malleus on both preoperative CT-scans and endoscopic video frames. Then, contours were processed and registered through an iterative closest point algorithm. Validation was performed on 4 cases and testing on 6 cases. Registration error was measured by overlaying both images and measuring the average and Hausdorff distances. The proposed registration method yielded a precision compatible with ear surgery with a 2D mean overlay error of mm for the incus and mm for the round window. The average Hausdorff distance for these 2 targets was mm and mm respectively. An outlier case with higher errors (2.3 mm and 1.5 mm average Hausdorff distance for incus and round window respectively) was observed in relation to a high discrepancy between the projection angle of the reconstructed CT-scan and the video image. The maximum duration for the overall process was 18s. A fully automatic 2D registration method based on a convolutional neural network and applied to ear surgery was developed. The method did not rely on any external fiducial markers nor human intervention for landmark recognition. The method was fast and its precision was compatible with ear surgery.

Artificial Intelligence Assisted Recognition of Anatomical Landmarks and Laparoscopic Instruments in Transabdominal Preperitoneal Inguinal Hernia Repair.

Laparoscopic TAPP (Trans-Abdominal PrePeritoneal) is a minimally invasive surgical procedure used to repair inguinal hernias. Arguably, one important aspect to TAPP hernia repair is the identification of anatomical landmarks and the correct use of various laparoscopic instruments. There are very few studies regarding the use of artificial intelligence in laparoscopic inguinal hernia repair and more specifically in TAPP. The aim of this study is to evaluate the feasibility and usefulness of AI in the recognition of anatomical landmarks and tools in laparoscopic TAPP videos. Imaging data have been exported from 20 Laparoscopic TAPP videos that have been performed by the authors and another 5 high quality TAPP videos from the internet (free access) performed by other surgeons. In total 1095 selected images have been exported for annotation. To accomplish the AI result of computer vision, the YOLOv8 model of deep learning was used. In total 2716 segmented areas of interest have been exported. The AI model was able to detect the various classes with a maximum F1 score of .82 when the confidence threshold was set to .406. The mAP50 was .873 for all classes. The Precision was above 50% when the confidence was over 10%. The Recall rate was above 50% when confidence was less than 70%. These results suggest that the model is effective at balancing precision and recall, capturing both true positives and minimizing false negatives. Artificial Intelligence recognition of anatomical landmarks and laparoscopic instruments in TAPP is feasible with acceptable success rates.

Comparative study of the spatial behavior of Chinese and foreign tourists based on landmark recognition in short tourism videos: a case study of Beijing

ABSTRACT Inefficient data collection methods hamper tourist behavior research. Focusing on Beijing, this paper employs computer-related video techniques to capture and segment tourism videos posted on platforms like YouTube and Bilibili by domestic and foreign tourists visiting Beijing. The keyframes are then subjected to landmark, common object, and scene recognition for a comparative analysis of spatial behavior of tourists. The results are as follows: first, tourists pay more attention to the folk culture in non-habitual environments; second, Chinese and foreign tourists have different tourism preferences and behaviors; and finally, cultural distance has an inhibitory effect on international tourism activities.

Limiting the reliance on navigation assistance with navigation instructions containing emotionally salient narratives for confident wayfinding

We live in a world that is increasingly dependent on technology, including for orientation in both familiar and unfamiliar space, which contributes to a long-term erosion of innate spatial navigation skills. In this study, we examined whether modified navigation instructions can make pedestrians less reliant on navigation aids to solve wayfinding tasks. In contrast to standard instructions, the modified instructions make decision-relevant landmarks at intersections emotionally salient and connected through narrative, and thus more memorable. The results of our online VR study with seventy adults revealed that, after navigating an unfamiliar route using modified navigation instructions, people made significantly fewer references to the navigation aid without compromising the accuracy of navigation compared to standard instructions. Narrative-based navigation instructions improved memory for the order in which relevant features in the environment were encountered along the traversed route, but not landmark recognition memory or memory for landmark-direction associations. Our findings highlight the benefits of using human-centred technologies that – as opposed to current navigation systems – promote the encoding and memorability of spatial information during navigation, and have the potential to train human spatial navigation abilities in the long term as a countermeasure toward GPS cognitive deskilling of population.

A-355 Eyescore, A Point of Care Smartphone App for Early Dry Eye Diagnosis and Management

Abstract Current diagnosis of dry eye disease (DED) has significant challenges with limited accuracy and poor correlation of clinical symptoms. The goal of this study is to develop a smartphone App, namely “EyeScore”, to provide a point of care (POC) and digital solution for early diagnosis of DED. The authors tested the hypothesis of measuring eye blink rate and partial blink count as early clinical biomarkers for the calculation of so called “eye healthiness score”, which allows a convenient, rapid eye exam at home with low resource settings. EyeScore uses an iPhone as an imaging and sensing device for in-App recordings of eyelid movements. The use of facial landmark recognition and eye aspect ratio (EAR) enabled comprehensive digital analysis of video frames for determination of the eye opening/closed states. The smartphone videos from 10 DED patients and 10 healthy controls were tested to optimize EAR derived thresholds for accurate measurements of blink rates and partial blink counts. The authors formulated a clinically relevant algorithm for the calculation of “eye healthiness score”. This 10-point scale score can be easily measured anytime with a non-invasive manner and remotely shared with the patient's eye doctor. As a result, both patients and doctors can monitor the eye conditions over time. Our results showed that EyeScore confirmed the diagnosis of all 10 DED patients. Importantly, Eyescore also identified three individuals with “pre-DED” conditions from 10 healthy controls, demonstrating its potential clinical values for early diagnosis and management of DED. Materials and Methods An Apple MacBook Pro installed with iOS software (iOS 16.1.2) and Xcode 14.0 was used for coding and debugging. Visionkit SDK from Apple Developer was adopted to extract coordinate data from six eye facial landmarks. EAR values were calculated based on the previously published formula, with a dynamic EAR developed as a threshold for the measurement of blink rate and half blink count. For eye healthiness score, a 10-point formula was formulated (0–3, normal; 4–5, Pre-DED; 6–10, DED) with several contributing factors including blink rate, half blink count, eye discomfort and demographic characteristics. Results A total of 20 iPhone videos from DED patients and healthy controls were analyzed and evaluated with EyeScore App. All DED patients obtained scores above 7, confirming their DED diagnosis. On the other hand, three controls obtained scores of 4 or 5, suggesting they were in the Pre-DED state with no noticeable clinical DED symptoms. All other controls obtain scores blow 3 with normal eye conditions. All tests were performed with the consents from the test participants. Conclusions Smart phone-based platform offers great potentials for point of care diagnostic methods. A POC enabled EyeScore App has been successfully developed for early diagnosis of DED. In the future, the authors plan to test EyeScore with a large-scale sample set of DED patients and controls to further validate the accuracy and predictability of eye healthiness score for its broader clinical applications.

Development of VGG-16 transfer learning framework for geographical landmark recognition

Computer vision mandates the development of landmark recognition paradigms for efficient Image Recognition. In this article, the concept of Visual Geometry Group Network (VGG-16) transfer learning is used to develop an AI model over a geographical landmarks’ dataset. The dataset is a small part of Google Landmarks dataset V2 which originally consists of over 4M images. A VGG-16 model trained on ImageNet dataset is used to transfer knowledge. A positive transfer of knowledge is seen and it was observed that the trained model was a highly generalized model for our dataset. Not only a training accuracy of more than 0.85 is observed but equivalent validation accuracy suggests that the developed model is highly robust with minimal overfitting. A comparison of our proposed approach was made with classical machine learning techniques like KNN (K Nearest Neighbor), Decision Trees, Random Forest, SVM (Support Vector Machines) and a 3 Layered CNN. The results clearly depict that the proposed approach outperforms all other machine learning classifiers in consideration.

PENGEMBANGAN APLIKASI PENERJEMAH BAHASA ISYARAT INDONESIA (BISINDO) MENGGUNAKAN METODE LONG-SHORT TERM MEMORY

The Indonesian Sign Language Translator (BISINDO) application is intended for individuals who are deaf and mute assist them in communicating with normal people. Due to the limitations of technology to assist individuals who are deaf and mute, the Indonesian Sign Language Translator application was developed. Additionally, it is hoped that this application will enable individuals who are deaf and mute to learn Indonesian Sign Language in real-time. LSTM generally consists of a cell, input, gate, and forget gate. LSTM is highly suitable for classifying, processing, and making predictions based on time series data. In this research, hand landmark recognition is used as a medium to store finger gestures from user inputs, which will then be saved in the form of coordinate points and stored in a file in csv format. Subsequently, this file will be trained and called back to determine the output.

The time course of spatial knowledge acquisition for different digital navigation aids

Digital maps on personal devices (e.g., phones) are common tools used to aid navigation. Different types of digital maps can influence spatial knowledge acquisition, and this effect might depend on whether the user interacts with an forward-up or north-up map. In spatial cognition theory, these differences can be used to support either sequential or continuous theories of spatial knowledge acquisition. To test these hypotheses, we compared spatial learning of participants (N = 67) after navigation in a virtual city with either a forward-up map, a north-up map, or a guiding arrow (i.e., a control) as a navigation aid. Critically, participants were tested on landmark recognition tasks and judgments of relative direction (JRDs) after each of four navigation blocks. We also examined mental workload during map usage using eye tracking in terms of the distributions of fixations on the maps. The results indicated that, regardless of navigation aid, participants improved on both landmark recognition and JRD tasks over blocks of trials. In addition, we found an interaction between block and navigation aid. This interaction suggests that participants in the forward-up map group initially produced more JRD errors than the north-up map group but that the two groups performed similarly in later blocks as they became more familiar with the environment. These findings are consistent with the eye-tracking data, which suggested a decrease in mental workload as evidenced by an increase in fixation distributions over blocks of trials. Together, these results suggest that participants with any of these navigation aids perform similarly on some tasks (supporting sequential theory), although the time course of learning may differ between map types (supporting continuous theory).

Automatic recognition of cephalometric landmarks via multi-scale sampling strategy

The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.

Environmental cognitive load and spatial anxiety: What matters in navigation?

Spatial navigation is essential for orienting oneself in familiar and novel environments. Several internal factors may affect spatial navigation, such as anxiety. This study explored the mediating role of anxiety (spatial, trait and state) in the relationship between cognitive load and spatial navigation (egocentric and allocentric) using parallel mediation models. A sample of 125 participants (60 females) completed self-reported measures of anxiety (spatial and state-trait) during free navigation in a virtual reality square with or without environmental cognitive load. After the virtual learning experience, three spatial tasks evaluating landmark recognition and the use of egocentric and allocentric coordinates were administered. Results showed that spatial anxiety partially mediated the relationship between environmental load and spatial skills, specifically for egocentric and allocentric tasks. In addition, trait anxiety mediated the relationship between environmental load and allocentric performance. Overall, these findings update the Environmental Knowledge Model (EKM) that explains human navigation in terms of individual and environmental factors. The study results suggest that navigation could be facilitated by reducing anxiety and the spatial complexity of the environment.