Three-dimensional Camera Research Articles

Non-contact measurement of conveyor belt speed based on fast point cloud registration of feature block

Abstract Non-contact, real-time measurement of conveyor belt speed is critical for energy-saving speed regulation and efficient development of coal mine conveyor systems. Existing speed measurement technologies for conveyor systems are often limited by the slippage and wear in contact measurement and complex environmental disturbance. This study introduces three-dimensional point cloud technology into coal flow information detection and innovatively presents a non-contact measurement method of conveyor belt speed based on fast point cloud registration of feature blocks. In the proposed method, a three-dimensional camera is used to capture point cloud data of the dynamically running conveyor belt, and the raw point cloud is preprocessed and segmented into blocks. Then, the C-MANV (curvature and mean angle of normal vectors) features of block point clouds are constructed based on the point cloud neighborhood curvature and the mean angle of neighborhood normal vectors. Finally, an improved block point cloud registration method based on C-MANV features is adopted to achieve the accurate measurement of the dynamic running speed of conveyor belt. Experimental results demonstrate that the proposed method achieves an average relative error of less than 1.8% in high-speed conveyor belt operation with an average processing time of less than 35 ms, which fulfils the accuracy and real-time requirements for conveyor belt speed detection in coal mines. This study provides an effective technical solution for the speed monitoring of coal mine conveyor systems.

465 Comparative analysis of semantic segmentation and deep regression models with supervised pre-training for accurate prediction of pig body weight from video data: Insights from industry-scale datasets

Abstract Accurate pig body weight (BW) measurement is essential for producers as it is related to pig growth, health, and marketing, yet conventional manual weighing methods are time-consuming and may cause potential stress to the animals. Although there is a growing trend towards the adoption of three-dimensional cameras coupled with computer vision techniques for pig BW estimation, their validation using industry-scale data are still limited. Among the prevailing methodologies, semantic segmentation and supervised pre-training regression are prominent models currently used. Therefore, the objectives of this study were: 1) to estimate pig BW from repeatedly measured video data obtained from a commercial setting, 2) to compare the performance of the two image analysis methods: thresholding segmentation and deep regression approaches, and 3) to evaluate the predictive ability of BW estimation models. An Intel RealSense D435 camera was installed in the commercial farm to collect top-view videos of 540 pigs biweekly at five different time points over three months. At the same time, manually measured BW records were collected using a digital weighing system. We used an automated video conversion pipeline and fine-tuned YOLOv8 to pre-process the raw depth videos. Subsequently, we acquired a total of 151,756 depth images and depth map files. Adaptive thresholding was applied to segment the pig body from the background. Four image-derived biometric features, including dorsal length, abdominal width, height, and volume, were estimated from the segmented images and fitted using ordinary least squares and random forest models. We applied transfer learning by initializing the weights of five deep learning models, including ResNet50, Xception, EfficientNetV2S, ConvNeXtBase, and Vision Transformer, with pre-trained weights from ImageNet. We then fine-tuned these models on the pig depth images. The last layer of each model was adapted to linear regression, enabling direct estimation of BW for each image without the need for additional image pre-processing steps. We employed random repeated subsampling cross-validation, dividing 80% of the pigs for training and 20% for testing, to evaluate prediction performance at each time point. The best prediction coefficients of determination and mean absolute percentage error for each time point were 0.76, 0.86, 0.90, 0.83, 0.90, and 4.57%, 3.76%, 3.01%, 3.41%, 4.84%, respectively. On average, the Xception model resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.90 and 3.01%. Our results suggest that deep learning-based supervised learning models improve the prediction performance of pig BW from industry-scale depth video data.

What Patient-related Factors Most Strongly Influence Autologous Fat Grafting Volume Retention in Breast Augmentation?

Background: Regarding autologous fat grafting for breast augmentation, the factors influencing volume retention are unclear. Few studies have assessed which factors, particularly patient-related variables, could affect volume retention and to what extent, without performing multivariate analysis. In this study, we performed three-dimensional breast volume measurements before and after autologous fat grafting, calculated the volume and volume retention, and investigated factors that may affect volume retention. Methods: A total of 204 breasts that underwent autologous fat grafting by the same surgeon at our hospital between May 25 and December 25, 2021 were included. Volumetric measurements were taken preoperatively and at 1, 3, and 6 months postoperatively using a three-dimensional camera; volume retention was calculated. Linear mixed model analysis was performed to examine the effects of age, body mass index, number of total autologous fat grafting procedures, pregnancy and lactation history, smoking status, fat processing techniques (sedimentation versus centrifugation), preoperative volume, and weight change at the 3- and 6-month postoperative volume retention analyses. Results: Using multivariate analysis, the preoperative volume was found to be a significant factor at 3 and 6 months. The preoperative volume affected fat volume retention rate by 0.004 (P = 0.001) and 0.008 (P = 0.011) per 10 mL at 3 and 6 months, respectively. Conclusions: The preoperative volume likely affected fat volume retention. Specifically, a 100-mL increase in the preoperative breast volume might increase the volume retention by 4%–8%. Preoperative volume is an important confounding factor for future studies.

Measurement of Nasal Contour Landmarks in Septorhinoplasties with Special Regard to the Course of Postoperative Swelling Using a Three-Dimensional Camera.

The integration of three-dimensional (3D) cameras into clinical practice for pre-operative planning and post-operative monitoring of rhinoplasties remains controversial. However, this technology offers the advantage of capturing the 3D surface without exposing patients to potentially harmful radiation. Continuous assessment allows the follow-up of swelling patterns, cartilage alignment, and bone remodeling. The primary objective of our study was to quantify changes in nasal structure before and after rhinoplasty by using 3D photography. Our study cohort consisted of 29 patients who underwent open structural rhinoplasty. We used the Artec Space Spider camera to acquire a total of 103 3D images. We collected pre-operative and at least two or three post-operative follow-up scans, which were taken one, three, and six months after surgery. We evaluated paired scans that included various time intervals to improve our understanding of swelling behavior and to ensure an objective analysis of changes. Eleven specific anatomical landmarks were identified for measurement. Two independent raters determined the distances between these landmarks over time. The calculation of intraclass correlation coefficients showed low inter-rater variability. Statistically significant changes over time (p < 0.05) were observed for various anatomical landmarks, including soft tissue nasion, soft tissue orbitale right, soft tissue maxillofrontale left, soft tissue maxillofrontale right, nasal bridge, and nasal break point. Conversely, no significant changes (p > 0.05) were observed in the measurements of soft tissue orbitale left, pronasale, subnasale, alare right, or alare left. A visual assessment was conducted using surface distance maps. The results indicate that the complete decrease in swelling takes at least 6 months or even longer. Additionally, 3D photography can provide an objectively comparable analysis of the face and external contours. Furthermore, it allows for a comparison of external contours and therefore pre- and post-operative differences.

NSVDNet: Normalized Spatial-Variant Diffusion Network for Robust Image-Guided Depth Completion

Depth images captured by low-cost three-dimensional (3D) cameras are subject to low spatial density, requiring depth completion to improve 3D imaging quality. Image-guided depth completion aims at predicting dense depth images from extremely sparse depth measurements captured by depth sensors with the guidance of aligned Red–Green–Blue (RGB) images. Recent approaches have achieved a remarkable improvement, but the performance will degrade severely due to the corruption in input sparse depth. To enhance robustness to input corruption, we propose a novel depth completion scheme based on a normalized spatial-variant diffusion network incorporating measurement uncertainty, which introduces the following contributions. First, we design a normalized spatial-variant diffusion (NSVD) scheme to apply spatially varying filters iteratively on the sparse depth conditioned on its certainty measure for excluding depth corruption in the diffusion. In addition, we integrate the NSVD module into the network design to enable end-to-end training of filter kernels and depth reliability, which further improves the structural detail preservation via the guidance of RGB semantic features. Furthermore, we apply the NSVD module hierarchically at multiple scales, which ensures global smoothness while preserving visually salient details. The experimental results validate the advantages of the proposed network over existing approaches with enhanced performance and noise robustness for depth completion in real-use scenarios.

Categorizing knee hyperextension patterns in hemiparetic gait and examining associated impairments in patients with chronic stroke

BackgroundPost-stroke hemiparetic gait exhibits considerable variations in motion patterns and abnormal muscle activities, notably knee hyperextension during the stance phase. Existing studies have primarily concentrated on its joint angle or moment. However, the underlying causes remain unclear. Thus, the causes of knee hyperextension were explored from a new perspective based on temporal-durational factors. Research QuestionDoes the temporal-durational difference of knee hyperextension presence result from specific decreased motor functions? MethodsBarefoot gait at a comfortable speed was captured using a three-dimensional camera system. Scores of knee hyperextension used a metric with the temporal-durational factor of knee hyperextension presence in each of four stance phases (1st double support, DS1; early single-leg stance, ESS; late single-leg stance, LSS; 2nd double support, DS2). These scores were used in cluster analysis. The classification and regression tree analysis characterizing each knee hyperextension cluster used the clinical measures of the lower limb and trunk motor function, muscle strength, and spasticity as explanatory variables. ResultsThirty patients with hemiparetic chronic stroke who exhibited knee hyperextension during gait were included. Four knee hyperextension clusters were shown: Momentary (almost no hyperextension), Continuous (DS1–DS2), ESS–LSS, and ESS–DS2. Knee flexor strength was lower in the groups with long hyperextension durations (Continuous and ESS–DS2) compared with short durations (ESS–LSS and Momentary). ESS–DS2 exhibited higher trunk motor function than Continuous, whereas more severe spasticity was observed in ESS–LSS than in Momentary. SignificanceThis study successfully classified four hemiparetic gait patterns with knee hyperextension based on the temporal-durational factor, providing valuable perspectives for understanding and addressing specific functional physical impairments. These findings offer guidance for focusing on related physical functions when striving for gait improvement with knee hyperextension and are expected to serve as a reference for treatment decision-making.

Feed intake in housed dairy cows: validation of a three-dimensional camera−based feed intake measurement system

Measuring feed intake accurately is crucial to determine feed efficiency and for genetic selection. A system using three-dimensional (3D) cameras and deep learning algorithms can measure the volume of feed intake in dairy cows, but for now, the system has not been validated for feed intake expressed as weight of feed. The aim of this study was to validate the weight of feed intake predicted from the 3D cameras with the actual measured weight. It was hypothesised that diet−specific coefficients are necessary for predicting changes in weight, that the relationship between weight and volume is curvilinear throughout the day, and that manually pushing the feed affects this relationship. Twenty-four lactating Danish Holstein cows were used in a cross-over design with four dietary treatments, 2 × 2 factorial arranged with either grass-clover silage or maize silage as silage factor, and barley or dried beet pulp as concentrate factor. Cows were adapted to the diets for 11 d, and for 3 d to tie-stall housing before camera measurements. Six cameras were used for recording, each mounted over an individual feeding platform equipped with a weight scale. When building the predictive models, four cameras were used for training, and the remaining two for testing the prediction of the models. The most accurate predictions were found for the average feed intake over a period when using the starting density of the feed pile, which resulted in the lowest errors, 6% when expressed as RMSE and 5% expressed as mean absolute error. A model including curvilinear effects of feed volume and the impact of manual feed pushing was used on a dataset including daily time points. When cross-validating, the inclusion of a curvilinear effect and a feed push effect did not improve the accuracy of the model for neither the feed pile nor the feed removed by the cow between consecutive time points. In conclusion, measuring daily feed intake from this 3D camera system in the present experimental setup could be accomplished with an acceptable error (below 8%), but the system should be improved for individual meal intake measurements if these measures were to be implemented.

Бесконтактная оценка промеров головы молочных коров

A system for non-contact assessment of animals using three-dimensional cameras will facilitate the grading process. To develop a system for non-contact exterior assessment of animals using automatic video cameras, it is necessary to justify the rational design and operating conditions of the system. Based on digital vision, the authors determined the necessary parameters of the system using three measurements of the head: head length, forehead length and maximum forehead width. 15 adult animals of the zebu-type black-motley breed with a height of 1300 to 1500 mm were studied. To ensure coverage of the cow’s head in any position, three cameras were used: two located above the animal at a height of 2 m from the floor (the minimum distance from the camera to the head was 500 to 800 mm) and one in front of the animal at a distance of at least 2 m from the head and at a height 1.3 to 1.5 m from the floor. The signal from the identification antenna of the signal from the RFID tag of the animal initiated the acquisition of a three-dimensional image at a speed of 5 to 10 frames/s. Based on the measurements, the system automatically determines the broad-headed and big-headed indices. As a result of the study, the authors determined rational parameters for the location of cameras. The efficiency of shooting and accurate measurement of head measurements is ensured when the cow’s head is tilted relative to the camera at an angle of 45° and when the upper camera is located at a level of 2 m from the floor. When the head is tilted 65° and above, shooting is provided with the front camera located at a distance of at least 2 m from the head and at a height of 1.3 to 1.5 m from the floor. In their subsequent research, the authors plan to justify the rational design and operational-technological parameters of the model.

Feasibility and tolerability of breath-hold in liver stereotactic body radiotherapy with surface guided radiotherapy

PurposeIntrafractional motion constitutes a significant challenge in SBRT (Stereotactic Body Radiotherapy).The breath-hold (BH) technique is employed to mitigate tumor motion; however, ensuring reproducibility and consistency remains critically important. Surface tracking systems, integrated into the treatment process, facilitate motion tracking through three-dimensional camera technology. Surface guidance has been incorporated with Varian EDGE (Varian Medical Systems, Palo Alto, CA, USA) and has been utilized at multiple treatment sites within our department since 2018. Drawing on four years of experience, this study aims to publish patient experience, assess the feasibility, and evaluate the tolerability of breath-hold during SBRT with surface guided radiotherapy (SGRT), particularly focusing on a specific subgroup: patients with liver metastases. MethodsProspective evaluation was conducted on patients with liver metastases undergoing breath-hold SBRT with SGRT. A two-step survey consisting of seven questions was administered after CT simulation and treatment. Treatment duration and the number of breath-holds were recorded. Additionally, factors potentially influencing SGRT and treatment time were assessed. ResultsBetween April 2021 and May 2022, a total of 41 patients underwent 171 fractions of treatment. According to the questionnaire, prior training was found to be beneficial, and breath-holding during the procedure was tolerable. Patients reported experiencing slight stress due to their active participation in the treatment. Factors such as Karnofsky Performance Status (KPS), age, lung volume, conditions affecting lung capacity, previous breath-hold history, and being a native speaker showed no correlation with treatment time. Moreover, these factors did not correlate with the tolerability of breath-hold during SGRT. However, female patients showed better breath-holding performance in SGRT treatments compared to male patients (p: 0.02). ConclusionsThe application of breath-hold with SGRT procedures is tolerable and feasible in liver SBRT treatments. There exists no specific subgroup that cannot tolerate this method.

306 Shinyanimalcv: Interactive Web Application for Object Detection and Three-Dimensional Visualization of Animals Using Computer Vision

Abstract Precision livestock farming applies integrated sensors and information technology to provide individualized animal care in a timely manner. A computer vision system, a non-intrusive and cost-efficient sensor technology, may be useful for monitoring animals to support farm management decisions. While the advancement in camera sensors provides a great opportunity for producers to improve animal health and welfare sustainably, the limited availability of user-friendly image data processing software tools substantially hinders the implementation of computer vision in livestock production systems. The objective of this study was to develop ShinyAnimalCV, which is a Shiny-based interactive animal computer vision web application. This software tool offers a user-friendly graphical user interface for object detection and three-dimensional visualization. The object detection module employs the Mask-RCNN to precisely segment the masks (regions) of the focal animals from two-dimensional images. A Python-based computer vision library OpenCV was used to draw minimum bounding boxes covering the segmented masks for object detection. The three-dimensional visualization module takes the depth map file captured from a top-view three-dimensional camera as an input, which contains the numerical distances between the camera and the objects (animals and background). The depth map file was first converted to a heatmap image, followed by identifying and segmenting the animal from the background using the Mask-RCNN model. The object detection module returns detection results, including the detected animal’s location, class/type, and morphological traits (e.g., body length and width). The visualization module generates a segmented mask to extract the height of the animal from the depth map file, which can be used to interactively visualize the three-dimensional surface of the animal and estimate its morphological traits, including length, width, height, and volume. By integrating these two modules into R Shiny, we deployed ShinyAnimalCV on a cloud server with pre-trained Mask-RCNN models using pig and cattle data to allow users to upload custom data and perform object detection and three-dimensional surface visualization. The features extracted by ShinyAnimalCV are expected to be useful for performing animal identification, feed intake monitoring, body weight predictions, and body condition score estimations. We conclude that the newly developed ShinyAnimalCV could facilitate the application of computer vision in the animal science community.

Analyzing Facial Asymmetry in Alzheimer's Dementia Using Image-Based Technology.

Several studies have demonstrated accelerated brain aging in Alzheimer's dementia (AD). Previous studies have also reported that facial asymmetry increases with age. Because obtaining facial images is much easier than obtaining brain images, the aim of this work was to investigate whether AD exhibits accelerated aging patterns in facial asymmetry. We developed new facial asymmetry measures to compare Alzheimer's patients with healthy controls. A three-dimensional camera was used to capture facial images, and 68 facial landmarks were identified using an open-source machine-learning algorithm called OpenFace. A standard image registration method was used to align the three-dimensional original and mirrored facial images. This study used the registration error, representing landmark superimposition asymmetry distances, to examine 29 pairs of landmarks to characterize facial asymmetry. After comparing the facial images of 150 patients with AD with those of 150 age- and sex-matched non-demented controls, we found that the asymmetry of 20 landmarks was significantly different in AD than in the controls (p < 0.05). The AD-linked asymmetry was concentrated in the face edge, eyebrows, eyes, nostrils, and mouth. Facial asymmetry evaluation may thus serve as a tool for the detection of AD.

Three-Dimensional Kinematic Analysis and Research on the Strong Volleyball Spike Technique of Excellent Female Volleyball Spikers from China and Abroad

This paper uses three-dimensional camera and technical statistics to analyze and study the spiking technique of Chinese and foreign excellent female volleyball attackers. The results show that the speed of Chinese and foreign women�s volleyball spikers is close to 4m/s, the maximum ball speed is close to 30m/s and the hitting height is more than 2.9m; the air hitting action is with obvious forward movement. There is a gap between Chinese women�s volleyball spikers and excellent foreign attackers in terms of spiking success rate, hitting height and hitting speed.

Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios.

Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis.

Use of Motion Capture Technology to Study Extrinsic Laryngeal Muscle Tension and Hyperfunction.

Patients with primary muscle tension dysphonia (pMTD) commonly report paralaryngeal pain and discomfort, and extrinsic laryngeal muscle (ELM) tension and hyperfunction are commonly implicated. However, quantitative physiological metrics to study ELM movement patterns for the characterization of pMTD diagnosis and monitoring of treatment progress are lacking. The objectives of this study were to validate motion capture (MoCap) technology to study ELM kinematics, determine whether MoCap could distinguish ELM tension and hyperfunction between individuals with and without pMTD, and investigate relationships between common clinical voice metrics and ELM kinematics. Thirty subjects (15 with pMTD and 15 controls) were recruited for the study. Sixteen markers were placed on different anatomical landmarks on the chin and anterior neck. Movements across these regions were tracked during four voice and speech tasks using two three-dimensional cameras. Movement displacement and variability were determined based on 16 key-points and 53 edges. Intraclass correlation coefficients demonstrated high intra- and inter-rater reliability (p's < 0.001). Other than greater movement displacements around the thyrohyoid space during longer phrasing (reading passage, 30-s diadochokinetics) and more movement variability in patients with pMTD, kinematic patterns between groups were similar across the 53 edges for the four voice and speech tasks. There were also no significant correlations between ELM kinematics and standard voice metrics. Results demonstrate the feasibility and reliability of MoCap for the study of ELM kinematics. 3 Laryngoscope, 133:3472-3481, 2023.

Robotic-assisted approach for complex cholecystectomies

Robotic approaches have facilitated the minimally invasive completion of increasingly complex surgical procedures. In the management of the difficult gallbladder, we have found that the wristed instruments, three-dimensional camera, the ability to use indocyanine green (ICG) with integrated fluorescent imaging, and ease of intracorporeal suturing to be useful in tackling the challenges associated with complex benign gallbladder disease. We describe the rationale and technical lessons learned during four cases of complex cholecystectomies that highlight the management principles and technical advantages afforded by the use of the robotic-assisted laparoscopic (RAL) approach. The cases include a subtotal fundus-first reconstituting cholecystectomy, subtotal fenestrating cholecystectomy, a cholecystocolonic fistula managed by a RAL subtotal fenestrating cholecystectomy, and an iatrogenic cholecystoduodenal fistula managed by RAL cholecystectomy. In each case, the operation was performed safely without intraoperative injury or conversion to open, and three of the four patients were discharged from the recovery room. In our view, these favorable outcomes were greatly facilitated by the robotic platform. It is our intent to share adaptations and innovations that we found helpful to encourage other surgeons with sufficient robotic experience to tackle complex gallbladder cases minimally invasively.

Outcomes of single-port robotic ureteral reconstruction using the da Vinci SP® system.

The da Vinci SP® robotic system enables three double-jointed wristed instruments and a fully wristed three-dimensional camera to be placed through a single port. This study presents our experience with robot-assisted ureteral reconstruction using the SP system and reports its outcomes. Between December 2018 and April 2022, a single surgeon performed robotic ureteral reconstruction using the SP system in 39 patients: 18 underwent pyeloplasty and 21 received ureteral reimplantation. Demographic and perioperative patient data were collected and analyzed. Radiographic and symptomatic improvements were assessed 3 months after surgery. In pyeloplasty group, 12 patients (66.7%) were female and two patients (11.1%) had undergone previous surgery for ureteral obstruction. The median operative time was 152 minutes, the median blood loss was 8 mL, and the median length of stay in hospital was 3 days. There was one case of a complication involving postoperative percutaneous nephrostomy (PCN). In ureteral reimplantation group, 19 patients (90.5%) were female and ten patients (47.6%) had undergone gynecological surgery that caused ureteral obstruction. The median operative time was 152 minutes, the median blood loss was 10 mL, and the median length of stay in hospital was 4 days. We observed one case of open conversion and two cases of complications (colonic serosal tearing and postoperative PCN after ileal ureter replacement). The radiographic results and symptoms successfully improved following both surgeries. Despite adhesion-related complications, the SP system appears to be safe and effective for use in robot-assisted ureteral reconstruction.

Three-dimensional image-guided topical photodynamic therapy system with light dosimetry dynamic planning and monitoring.

Photodynamic therapy (PDT) has shown significant potential for skin disease treatment. As a key element, light is critical to influencing its treatment outcome, and light dosimetry is an issue of much concern for researchers. However, because of three-dimensional irregularity in shape and patient's movement during the therapy, irradiance hardly keeps uniform on the lesion and flux measurement remains a challenge. In this work, we report the development of a three-dimensional image-guided PDT system, and the method of dynamic irradiance planning and flux monitoring for lesions in different poses. This system comprises a three-dimensional camera for monitoring patients' movement during therapy, a computer for data analysis and processing, and a homemade LED array for forming uniform irradiance on lesions. Simulations on lesions of the face and arm show that the proposed system significantly increases effective therapy area, enhances irradiance uniformity, is able to visualize flux on the lesion, and reduces risks of burns during PDT. The developed PDT system is promising for optimizing procedures of PDT and providing better treatment outcomes by delivering controllable irradiance and flux on lesions even when a patient is moving.

Near-infrared 3D imaging with upconversion detection

We demonstrate a photon-sensitive, three-dimensional (3D) camera by active near-infrared illumination and fast time-of-flight gating. It uses picosecond pump pulses to selectively upconvert the backscattered photons according to their spatiotemporal modes via sum-frequency generation in a χ 2 nonlinear crystal, which are then detected by an electron-multiplying CCD with photon sensitive detection. As such, it achieves sub-millimeter depth resolution, exceptional noise suppression, and high detection sensitivity. Our results show that it can accurately reconstruct the surface profiles of occluded targets placed behind highly scattering and lossy obscurants of 14 optical depth (round trip), using only milliwatt illumination power. This technique may find applications in biomedical imaging, environmental monitoring, and wide-field light detection and ranging.

Video-Based Facial Movement Analysis in the Assessment of Bulbar Amyotrophic Lateral Sclerosis: Clinical Validation.

Facial movement analysis during facial gestures and speech provides clinically useful information for assessing bulbar amyotrophic lateral sclerosis (ALS). However, current kinematic methods have limited clinical application due to the equipment costs. Recent advancements in consumer-grade hardware and machine/deep learning made it possible to estimate facial movements from videos. This study aimed to establish the clinical validity of a video-based facial analysis for disease staging classification and estimation of clinical scores. Fifteen individuals with ALS and 11 controls participated in this study. Participants with ALS were stratified into early and late bulbar ALS groups based on their speaking rate. Participants were recorded with a three-dimensional (3D) camera (color + depth) while repeating a simple sentence 10 times. The lips and jaw movements were estimated, and features related to sentence duration and facial movements were used to train a machine learning model for multiclass classification and to predict the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar subscore and speaking rate. The classification model successfully separated healthy controls, the early ALS group, and the late ALS group with an overall accuracy of 96.1%. Video-based features demonstrated a high ability to estimate the speaking rate (adjusted R 2 = .82) and a moderate ability to predict the ALSFRS-R bulbar subscore (adjusted R 2 = .55). The proposed approach based on a 3D camera and machine learning algorithms represents an easy-to-use and inexpensive system that can be included as part of a clinical assessment of bulbar ALS to integrate facial movement analysis with other clinical data seamlessly.

The Effect of Non-prescription Orthotics on Walking Gait in Participants with Excessive Pronation

Background: Insole orthotics are prescribed to correct human body position during both the standing position and the motion of gait. Also, they are being applied for people who have forefoot abduction and arch deformation. However, a little is known about the relationship between forefoot and arch orthotic correction and the alternation in foot biomechanics. Objectives: This study was aimed to determine the effect of non-prescription insole orthotics in walking gait parameters. Methods: It was a quasi- experimental with pre-test, post-test design study. Twelve volunteer participants (41 ± 17 y), 10 men and two women, participated in this study. Three-dimensional (3D) cameras were used to do motion capture through those markers placed on lower body segments. Seven segments such as: Hip, left and right thigh, left and right shank, left and right foot were used for labelling in this study, which was measured with Vicon T-Series cameras at 250 Hz. The participants did walk in three different phases: Barefoot, running shoe, running shoe with a non-prescription orthotic inside. t-test was used to analyse the data. Results: The paired t-test results indicated that there is significant difference in cadence, step length and stride length for running shoe, and non-prescription orthotics compare to those of barefoot (P ≤ 0.05). Additionally, it was found that, non-prescription orthotics resulted in higher step length and stride length, and less cadence (P ≤ 0.05). On the other hand, But, no significant changes were found for non-prescription orthotics (P ≥ 0.05). Conclusions: It was concluded that cadence, step length and stride length were improved in running shoe compared to non-prescription orthotics group. But there were not any significant differences between running shoe and non-prescription orthotics.