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In this paper we present a multimodal cross-system dataset for virtual reality (VR) biometrics. The dataset consists of 41 right-handed participants performing a ball-throwing task in a Unity-based VR environment. Data is collected from the participants using the Meta Quest, HTC Vive, and HTC Vive Cosmos VR systems, covering both lighthouse and camera-based tracking systems. The dataset is the only known multi-VR system dataset for VR biometrics, in addition to being the only known one with external video of the user performing their VR task. During each session participants provide 10 trials by lifting and throwing the ball from the virtual pedestal and throwing it at the target. Participants provide data across 6 distinct sessions separated by at least 1 day, where data is collected from each VR system for 2 sessions. Using the VR system, our dataset records headset and hand controller, both left and right, positions and orientations as well as the trigger position of the dominant hand controller. The VR system data is provided as NumPy files. In addition to the data gather using the VR systems, we collect data using an externally mounted GoPro Hero 7 camera. The GoPro Hero 7 camera is mounted perpendicular to the participant such that the whole interaction is visible in the image frame. The data from the GoPro is collected at 60 frames per second and manually cropped to include only the full view of the participant. The cropped videos are synchronized to the VR motions and provided as MPEG-4 (MP4) videos. Using the GoPro videos, we generate COCO body keypoints using the MMPose and OpenPose toolboxes and provide the data as JSON files. The VR system headset and hand controllers capture movement of the participant's head and hands. The external GoPro video and associated body keypoints enable capture of body parts on the participant's dominant side that are not tracked by the VR system. Our dataset also provides participant demographics in the form of Self-Identified Gender, Age, Height (in), Weight (lb), Writing Hand, Throwing Hand, Throwing Sport Experience, Type of Throwing Sport, and VR Experience. Finally, we provide capture time summary data that provides temporal differences in days between successive sessions for each participant. The provided dataset enables research in cross-system VR biometrics with and without external video and body keypoint. The dataset also enables researchers to extend beyond the headset and hand controllers by using 2D motion trajectories from the body keypoints. Using demographic information, researchers can use the dataset to understand the impact of various demographic characteristics on VR biometrics. Temporal data can enable an understanding of short-term movement evolution captured using virtual and real-world sensing techniques. The dataset enables development of cross domain motion trajectory prediction from 2D to 3D or vice versa.

To address the issues of insufficient restoration of texture details in deblurred images and inadequate learning of frequency domain features, an image deblurring algorithm based on frequency domain feature enhancement and convolutional neural networks is proposed. In this architecture, firstly, a Fourier residual module with a parallel structure is constructed to achieve collaborative learning and modeling of spatial and frequency domain features, aiming to improve frequency domain feature learning capability and the restoration effect of the texture details; secondly, a gated controlled feed-forward unit acts on the Fourier residual module to further enhance the nonlinear expression ability of the algorithm; thirdly, a supervised attention module is improved and added to the decoder to promote more effective capture of key features for image reconstruction; finally, the weighted sum of spatial domain Charbonnier loss function and frequency domain loss function is defined as a novel total loss function. In addition, to verify the performance of our proposed algorithm, we conducted experiments on the GOPRO and HIDE datasets. Through experiments on the GOPRO, we obtained an SSIM and an LPIPS of 0.961 and 0.0278, respectively. With regard to the experiments on the HIDE datasets, we obtained an SSIM and an LPIPS of 0.941 and 0.0286, respectively. As for parameter count and running time, their values were 1.197 and 9.15 × 106, respectively, obtained by the experiments on the GOPRO. In all algorithms, the values of our proposed algorithm are optimal. However, the PSNR of our proposed algorithm is very close to that of the latest comparison algorithm and is suboptimal. In a word, experimental results have demonstrated that our proposed algorithm effectively removes blur while better preserving the details and edges of the image. Therefore, it has more practical value and prospects in computer vision tasks.

Image deblurring and compression-artifact removal are both ill-posed inverse problems in low-level vision tasks. So far, although numerous image deblurring and compression-artifact removal methods have been proposed respectively, the research for explicit handling blur and compression-artifact coexisting degradation image (BCDI) is rare. In the BCDI, image contents will be damaged more seriously, especially for edges and texture details. Therefore, the restoration of the BCDI is a more severe ill-posed inverse problem, and deep mining of local and global feature information is critical for effective BCDI restoration. To this end, we propose a spatial-frequency hybrid restoration network (SFHRN) for explicit and effective joint-photographic-experts-group (JPEG) compressed BCDI restoration. Specifically, according to the nature of JPEG compression artifacts, we propose a spatial-frequency hybrid block (SFHB), which includes a dual-branch structure and an information screening strategy (ISS). First, for the dual-branch structure, we design a patch-level channel attention branch (PCAB) and a pixel-level global attention branch (PGAB) to fully exploit local context information in the spatial domain and mine the global feature information in the frequency domain respectively. Secondly, we design a simple and effective information screening strategy (ISS) to discriminatively determine which pixels and channels should be retained and enhanced in frequency and spatial domains respectively for latent clear image restoration. Finally, for the first time, we build the blur and compression-artifact coexisting degradation datasets by adding various degrees of JPEG compression-artifact into existing benchmark deblurring datasets, e.g. GoPro and HIDE, named as GoPro-Compressed and HIDE-Compressed respectively. Extensive experiments demonstrate the superiority of our proposed SFHRN in terms of both performance and computational cost.

The Indian medical curriculum recently included Early Clinical Exposure (ECE) to enhance the undergraduate medical training. A key challenge in in implementing it in surgical education is to ensure adequate operating room (OR) exposure, while maintaining sterility and minimizing the OR traffic. Traditional teaching methods such as surgical simulators and virtual dissecting tables provide anatomical insights but lack the immersive experience of an actual OR setting. Intraoperative video recordings, particularly using compact and surgeon-controlled devices like GoPro cameras, offer an effective alternative for augmenting the surgical training. This study intends to assess the scope of recording technologies in enhancing undergraduate medical education in the surgical subjects. This study was registered in PROSPERO database and the registration number is CRD420251049770.A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.A comprehensive search of PubMed, Embase, and Google Scholar was performed using MeSH and non-MeSH terms related to surgical recording technologies. Articles describing the use of head-mounted or point-of-view cameras like GoPro or Google Glass, for live teaching and surgical training were included, while those using instrument-mounted cameras (e.g., arthroscope, endoscope) or hypothetical discussions were excluded. Data extraction focused on the type and number of cameras used, their purpose, and the target audience. A total of 1472 articles on surgical video recording were identified. Following the elimination of duplicates, 964 records were evaluated, resulting in the exclusion of 818 based on title and abstract analysis. Following full-text evaluation of 146 articles, 25 studies met the inclusion criteria. The majority of studies were conducted in general surgery and orthopaedics (n = 7), followed by neurosurgery (n = 4). Overhead camera systems were used in 21 studies, while tripod-mounted cameras were used in three studies. Most studies employed GoPro Hero 4, 5, and 6 models. The studies primarily focused on undergraduate teaching (n = 19), with some evaluating postgraduate training (n = 3). Head-mounted recording devices, such as GoPro cameras, provide an effective, cost-efficient, and easily implementable tool for surgical instruction. They enhance undergraduate training by offering a OR exposure perspective while maintaining sterility and reducing OR congestion. The integration of sports camera-based intra operative recording should be considered a standard component of practical instruction in medical schools to improve surgical education and training outcomes.

Early-age shrinkage in 3D-printed concrete constitutes a critical applied challenge due to the rapid development of deformations and the absence of conventional reinforcement systems. From a scientific standpoint, a clear knowledge gap exists in materials science concerning the reliable quantification of very small, rapidly evolving strains in fresh and early-age cementitious materials produced by additive manufacturing. This study investigates practical and low-cost alternatives to commercial optical systems for monitoring early-age shrinkage in 3D-printed concrete, a key challenge given the rapid deformation of printed elements and their typical lack of reinforcement. The work focuses on identifying both the most precise method for capturing minor, fast-developing strains and affordable tools suitable for laboratories without access to advanced equipment. Three mixtures with different aggregate types were examined to broaden the applicability of the findings and to evaluate how aggregate selection affects fresh properties, hardened performance, and shrinkage behavior. Shrinkage measurements were carried out using a commercial digital image correlation system, which served as the reference method, along with simplified optical setups based on a smartphone camera and a GoPro device. Additional measurements were performed with laser displacement sensors and Linear Variable Differential Transformer LVDT transducers mounted in a dedicated fixture. Results were compared with the standardized linear shrinkage test to assess precision, stability, and the influence of curing conditions. The findings show that early-age shrinkage must be monitored immediately after printing and under controlled environmental conditions. When the results obtained after 12 h of measurement were compared with the values recorded using the commercial reference system, differences of 19%, 13%, 16%, and 14% were observed for the smartphone-based method, the GoPro system, the laser sensors, and the LVDT transducers, respectively.

Physical education (PE) teachers play a central role in shaping students' physical literacy, including motivation, confidence, physical competence, knowledge, and understanding that supports lifelong physical activity. Differentiation of task difficulty during lessons is widely considered essential in this process. Yet in game-based lessons, what challenges one student may overwhelm or bore another. To better understand how and why teachers differentiate task difficulty, this qualitative study explored how junior secondary school PE teachers adapt task difficulty during game-based lessons. Fourteen teachers participated in stimulated recall interviews based on 20-minute GoPro recordings of their own lessons. We analyzed the data using reflexive thematic analysis. Teachers described differentiation as an ongoing, situated process of monitoring and shaping students' engagement by verbal guidance, rather than primarily modifying task constraints (e.g., rules or equipment). Teachers emphasized fostering inclusive participation, cultivating a positive pedagogical climate, using praise to reinforce desired behaviors, and regulating gameplay by stepping in and out. Differentiation most often occurred through verbal feedback, with particular attention to supporting students perceived as less confident, while engaging higher-skilled students in role-model or leadership roles. In their reasoning, teachers placed students' confidence and motivation at the forefront, describing them as important building blocks from which physical competence may develop. We describe PE teachers as "motivational coaches", as their decision-making prioritizes students' emotional and relational experiences, with the expectation that skill development will follow. This contrasts with earlier research emphasizing skill-focused teaching. However, it can be questioned whether optimal motor learning occurs if teachers reduce task difficulty to support confidence. We suggest that acknowledging students' effort and progress while challenging a student may support both motivation and learning. Since challenge is a subjective concept, it is important to foreground students' experiences of challenge in game-based PE in future research.

The ability of triage nurses to quickly identify an urgent situation is crucial and requires good clinical reasoning, which is strongly influenced by the context and professional environment. To explore how triage nurses generate initial hypotheses at the very start of the triage encounter and which immediately available cues contribute to this early sense-making. This qualitative study was conducted in three regional hospitals and included 10 triage nurses. Nurses wore a forehead-mounted GoPro camera to record triage from their point of view. Semi-structured, video-cued recall interviews were conducted immediately after triage. Deductive and inductive coding was then carried out and analysed using thematic analysis methods. The average age of triage nurses was 36 years, with an average of 6.5 years of professional experience in the emergency department. Triage nurses generated hypotheses as soon as they encountered the patient, largely through pattern recognition (a core mechanism associated with intuition). These hypotheses were sometimes made as soon as the patient was registered at the emergency desk reception and even before talking to them. These hypotheses were based on the patient's main presenting complaint, their facial expression, and the time reported for the onset of symptoms. Triage nurses operate in a complex environment and use rapid clinical reasoning processes that draw on readily available cues and prior experience. These findings may inform triage education by highlighting the early, experience-based processes involved in hypothesis generation and the potential value of explicitly addressing intuitive reasoning in triage training.

Arboreal bipedalism is suggested as a precursor and adaptive locomotor mode for the immediate ancestor of hominin terrestrial bipedalism, yet detailed investigation of its locomotor biomechanics is hindered by its low frequency and observation difficulties in free-ranging hominoids. Further difficulties are faced in the creation and installation of a suitable experimental setup in natural settings. Captive studies may potentially reduce logistical issues, but data on arboreal bipedalism are scarce. We present an experimental design and protocol for collecting video data on arboreal bipedalism in captive primates, from which qualitative and quantitative gait data can be extracted. Our protocol increases the frequency of this rare behavior. Data were collected on six adult chimpanzees (three males, three females) at La Vallée des Singes, Romagne, France. The chimpanzees voluntarily engaged with a simulated arboreal foraging scenario consisting of two parallel PVC tubes and a high-value food reward. Five GoPro cameras recorded interactions with the experimental equipment. For validation of the effectiveness of our experimental design, protocol interactions were identified as successful (activity completed) or unsuccessful. All age and sex classes had successful interactions. Full strides were observed alongside the identification of two forms of arboreal bipedalism, forward-facing and sideways. This highlights the variation within the arboreal bipedalism locomotor category and the capacity for our experimental design to provide suitable data for gait parameter analysis and interspecies comparisons. Our protocol thus permits detailed investigation of arboreal bipedalism's role in the evolution of hominin bipedalism.

Indoor bouldering is a popular and rapidly growing sport in which climbers fall repeatedly from walls up to 4-5 m high, making lower-limb injuries common. It is therefore essential to understand fall kinematics and impact conditions, yet fall kinematics remain poorly documented because laboratory motion capture is impractical in gyms. This study aimed to validate a markerless multi-camera pipeline (Pose2Sim) against a 2D video annotation tool (Kinovea) for displacement and velocity measurement, and against IMUs for peak acceleration. Ten teenage athletes (3 males, 7 females; 14-17 years) performed 40 falls recorded with five cameras (GoPro HERO12, USA, 2.7 K, 240 fps) and three IMUs (Blue Trident, Vicon, UK; ±200 g, 1600 Hz). Cut-off frequencies were set using Yu's method (13 Hz for video, 39 Hz for IMUs). Pose2Sim's results closely matched those of Kinovea for fall height and peak velocity with non-significant differences but underestimated peak acceleration. At the forehead, no significant difference was found, likely due to smaller accelerations at the head. Markerless video analysis is appropriate for studying fall kinematics and typology in indoor bouldering. IMUs remain necessary to quantify impact intensity, and future work should explore the combination of both IMUs and video to overcome this limitation.

Image restoration is a vital research area in computer vision, focusing on reconstructing high-quality clear images from degraded observations. Common types of degradation include noise and blur, which may stem from imaging device limitations, environmental interference, and other factors. This paper centers on the design and optimization of multi-stage image restoration networks, conducting in-depth exploration of feature extraction, feature fusion, attention mechanisms, and their practical applications. A multi-stage hybrid attention mechanism-based image restoration network is proposed. Initially, each stage progressively extracts and restores image features. Then, an adaptive feature fusion block enables effective cross-stage information transfer. Finally, by calculating losses at each stage and assigning different weights, the network achieves stable convergence during training. The hybrid attention mechanism enhances the model's focus on critical features and improves its understanding of the overall image structure. Outstanding performance has been achieved in both image deblurring and denoising tasks. On the GoPro dataset, the restored results achieved a PSNR of 33.26 and an SSIM of 0.963. On the SIDD dataset, the restored results reached a PSNR of 40.23 and an SSIM of 0.963. Furthermore, ablation experiments demonstrated the effectiveness of the multi-stage model, hybrid attention mechanism, and adaptive feature fusion block.

Endoscopy is an important form of clinical gastroenterology education because it gives students the opportunity to learn about diagnosis procedures and even treatment. During the COVID-19 pandemic, medical students were observed from outside the endoscopy room due to the risk of airborne infection. In this study, we investigated the efficacy of combining endoscopy education with doctor's-eye-view videos of the procedure obtained using live-action cameras (GoPro®). From February to May 2021, endoscopists wore GoPro Hero8 cameras on their heads to display a doctor's-eye view video outside the room. The efficacy of the GoPro videos in combination with endoscopic monitoring was evaluated by 15 participating medical students. The participants rated the efficacy on a 5-point scale and commented on the positive and negative points. A total of 78.6% of participants evaluated the GoPro as good; 57.2% answered that it increased their understanding, with 71.4% stating that it increased their understanding of procedures in particular. A total of 85.7% of the students answered that their interest in endoscopy had increased, and 85.7% evaluated the benefit of the GoPro videos as good. In addition, 64.3% answered that the method was effective in preventing COVID-19 infection. Education using GoPro videos enabled students to feel as if they were conducting the endoscopy themselves and enabled them to concentrate on learning. Practical endoscopic education using a GoPro is an effective educational tool that not only increases understanding of endoscopic practice but also stimulates students' interest and awareness of their future as doctors.

Breast surgery, regardless of the type of procedure, requires a symmetrical result. However, this outcome is currently dependent on the surgeon's experience and subject to their subjectivity. Our study aims to investigate the correlation between the weight of breast gland resection and the volume variation measured by a portable 3D camera, adjusted for breast density measured electronically. Thirty patients who underwent bilateral breast reduction were included in the study. Each patient had her breast volume measured using a 3D camera (GOPRO by Creaform) during the preoperative consultation and again four months postoperatively. For each patient, breast density was measured using the MyotonPro device. Analysis of the correlation between the resection weight and pre- and postoperative volume variation revealed a coefficient of determination r2 of 0.822 (95% CI: 0.713-0.892). Adjustments based on the parameters measured by the MyotonPro did not appear to influence the correlation. Our model indicates that for a resection weight≥500g, there is a volume variation of 666mL, with a sensitivity of 85.3% and a specificity of 86.4%. There is a strong correlation between the resection weight and the volume variation measured by the 3D camera, with no influence from breast density. The routine use of a 3D camera would allow plastic surgeons to better plan each surgery and optimize our results.

To assess a rule-based decision tree algorithm's performance for classifying and counting specific hip flexion repetitions in able-bodied people and to validate the algorithm's efficacy for people with spinal cord injury (SCI). Alternative placement of the accelerometer was tested. A validation study. Specialized SCI center in Denmark. Ten able-bodied people and 10 people with SCI were recruited. All participants completed a 15-minute predefined protocol with the following movements: hip flexion in supine 90°, 45° and 20°, hip abduction, pelvic lift, transfer from supine to sitting, sit-to-stand, transfer to a wheelchair, pushed in a wheelchair, Motomed cycling, walking and steps in Nustep fitness trainer. All wore accelerometers on the thigh and a chest-mounted GoPro camera to establish ground truth. Confusion matrixes showed that able-bodied people's activities and specific hip movements can be classified and the number of repetitions counted with 0.86 accuracy. The algorithm's performance did not change substantially depending on the position of the accelerometer. For people with movement deficits caused by SCI, the accuracy lowered to 0.66 but could be improved to 0.79 for classifying and counting this population's activities/movements overall. The algorithm tested could classify specific hip movements and other activities in the SCI population. This method using a single accelerometer may be applied in clinical trials for people with SCI to objectively assess the change in the number of repetitions over time of hip flexion movements, walking and sit-to-stand activities and to some extent hip abduction and pelvic lift.Trial registration: ClinicalTrials.gov NCT05558254. Registered 28th September 2022.

In this study, we propose a lightweight causal Mamba network (LCM-QRIR) for blurred QR code image restoration. Its core architecture comprises a HOGMamba module that integrates HOG features with Mamba, which is employed to process long-range spatial dependencies in degraded QR code images with linear complexity. To further adapt to degraded image restoration, we introduce a dynamic interactive feed-forward (DIFF) module to promote channel-space interaction, a wavelet downsampling enhancement (WDE) module to mitigate information loss during encoder downsampling, and a sparse artifact similarity-weighted loss combined with a distortion invariant learning (DIL) strategy to guide the model toward learning more invariant features. By evaluating on the blurred QR code image dataset (BQRCI) and the publicly available GoPro dataset for natural image deblurring tasks, experimental results demonstrate that LCM-QRIR exhibits competitive advantages in both performance metrics and computational complexity. Furthermore, the model displays robust capabilities in interpretability and causal reasoning.

In image sensing, measurements such as an object's position or contour are typically obtained by analyzing digitized images. This method is widely used due to its simplicity. However, relative motion or inaccurate focus can cause motion and defocus blur, reducing measurement accuracy. Thus, video deblurring is essential. However, existing deep learning-based video deblurring methods struggle to balance high-quality deblurring, fast inference, and wide applicability. First, we propose a Current-Aware Temporal Fusion (CATF) framework, which focuses on the current frame in terms of both network architecture and modules. This reduces interference from unrelated features of neighboring frames and fully exploits current frame information, improving deblurring quality. Second, we introduce a Mixture-of-Experts module based on NAFBlocks (MoNAF), which adaptively selects expert structures according to the input features, reducing inference time. Third, we design a training strategy to support both sequential and temporally parallel inference. In sequential deblurring, we conduct experiments on the DVD, GoPro, and BSD datasets. Qualitative results show that our method effectively preserves image structures and fine details. Quantitative results further demonstrate that our method achieves clear advantages in terms of PSNR and SSIM. In particular, under the exposure setting of 3 ms-24 ms on the BSD dataset, our method achieves 33.09 dB PSNR and 0.9453 SSIM, indicating its effectiveness even in severely blurred scenarios. Meanwhile, our method achieves a good balance between deblurring quality and runtime efficiency. Moreover, the framework exhibits minimal error accumulation and performs effectively in temporal parallel computation. These results demonstrate that effective video deblurring serves as an important supporting technology for accurate image sensing.

This article analyzes the interactions and conversations of ten families visiting the exhibition "The science of love and forgiveness", at Maloka, in Bogotá, Colombia, which explores emotions from a neuroscientific and human sciences perspective, as well as psychological approaches to forgiveness in a post-conflict context, namely, since the peace agreement between the government and Colombia's largest guerrilla group. Family visits in December 2022 were recorded using GoPro cameras attached to the chest of a child from each group. The data were analyzed using Dedoose software. The mediation of adults and the exhibition design were found to foster physical and mental interaction. O artigo analisa interações e conversas de dez famílias em visita à exposição “Ciencia del amor y del perdón”, do museu Maloka – em Bogotá, Colômbia –, que apresenta emoções sob perspectivas neurocientíficas e das ciências humanas e explora ferramentas da psicologia para o perdão, em um contexto de “pós-conflito”, após assinatura de acordo de paz entre o governo e o maior grupo guerrilheiro colombiano. Os dados, coletados em dezembro de 2022, por meio de gravações das visitas em câmera GoPro acoplada ao tórax de uma criança de cada grupo, foram analisados com uso do software Dedoose. Os resultados indicaram que a mediação dos adultos e a proposta expográfica possibilitaram interatividade física e mental.

The rates of forceps-assisted vaginal deliveries in the management of the second stage of labor are decreasing. This decline is mirrored by fewer learning opportunities for resident trainees in obstetrics. Previous studies have demonstrated an increase in the skill and confidence of trainees in performing forceps-assisted vaginal delivery with simulation. The optimum timing and frequency of simulation sessions to improve skill retention are currently unknown. This study aimed to test the effects of clustered vs spaced training sessions to teach trainees forceps-assisted vaginal delivery to determine whether spaced training sessions would lead to superior retention of skill. A commercially available pelvic trainer (Lucy's Mum; MODEL-med International, Cheltenham, Australia) was used, and Objective Structured Assessment of Technical Skills scores were measured at >1 month after the intervention. This was a randomized controlled trial of clustered vs spaced forceps-assisted vaginal delivery simulation sessions. This study included 35 participants, giving 80% power to detect a difference in the Objective Structured Assessment of Technical Skills score of 6. Trainees and obstetrical providers who did not independently perform forceps-assisted vaginal delivery were randomized in blocks to a single learning session (clustered: 30 minutes of hands-on teaching with a model [n=17]) or 3 individual learning sessions (spaced: 10 minutes each [n=18]) spaced 1 week apart. Participants completed an online module introducing forceps-assisted vaginal delivery and completed skillset questionnaires before and after the simulation. A chest-mounted GoPro camera (GoPro, Inc, San Mateo, CA) was used to capture the first-person point-of-view technique to blind expert adjudicators to the participants' identities. Blinded footage was used to grade the average Objective Structured Assessment of Technical Skills scores. Both clustered and spaced simulation training led to improved forceps-assisted vaginal delivery Objective Structured Assessment of Technical Skills scores after the intervention (+4.8 vs +5.9, respectively, from baseline). However, the median change was not different between randomization groups (Wilcoxon rank-sum test, P=.78). Both simulation groups had higher confidence to apply forceps, perform safety checks, and independently/safely perform forceps-assisted vaginal delivery (t test, P<.05). This study described the novel use of a point-of-care video and simulator application to improve forceps-assisted vaginal delivery simulation training. Objective Structured Assessment of Technical Skills scores and provider self-assessment of training improved, regardless of clustered vs spaced simulation sessions. El resumen est&#xe1; disponible en Espa&#xf1;ol al final del art&#xed;culo.

Community engagement is an increasingly important component of ancient DNA (aDNA) research, especially when it involves archeological individuals connected to contemporary descendants or other invested communities. However, effectively explaining methods to non-specialist audiences can be challenging due to the intricacies of aDNA laboratory work. To overcome this challenge, the Anson Street African Burial Ground (ASABG) Project employed a GoPro camera to visually document the process of aDNA extraction for use in community engagement and education events. A GoPro Hero 6 camera enclosed in a decontaminated underwater case was used to film multiple rounds of aDNA extractions from first- and third-person perspectives. The raw footage was edited into long (13-minute) and short (5-minute) format videos to summarize the steps of aDNA extraction for different educational aims. The videos were used at community engagement events, as well as in classrooms and other educational venues for students of different age groups. General feedback from the community was solicited at the events. We found that the use of videographic methods increased the transparency and accessibility of the aDNA research conducted by the ASABG Project team. Providing a visual guide to the often destructive nature of aDNA testing served as an important step in the continuing practice of informed (dynamic) consent with the descendant community. Future initiatives could expand these visualization efforts by illustrating other steps in the aDNA testing process, such as library preparation or sequencing, or incorporating approaches such as live streaming to foster trust and expand public science literacy.