Abstract: High dynamic range (HDR) signals fundamentally differ from the traditional low dynamic range (LDR) ones in that pixels are related (proportional) to the physical luminance in the scene (i.e. scene-referred). For that reason, the existing LDR video quality measurement methods may not be directly used for assessing quality in HDR videos. To address that, we present an objective HDR video quality measure (HDR-VQM) based on signal pre-processing, transformation, and subsequent frequency based decomposition. Video quality is then computed based on a spatio-temporal analysis that relates to human eye fixation behavior during video viewing. Consequently, the proposed method does not involve expensive computations related to explicit motion analysis in the HDR video signal, and is therefore computationally tractable. We also verified its prediction performance on a comprehensive, in-house subjective HDR video database with 90 sequences, and it was found to be better than some of the existing methods in terms of correlation with subjective scores (for both across sequence and per sequence cases). A software implementation of the proposed scheme is also made publicly available for free download and use. HighlightsThe paper presents one of the first objective method for high dynamic range video quality estimation.It is based on analysis of short term video segments taking into account human viewing behavior.The method described in the paper would be useful in scenarios where HDR video quality needs to be determined in an HDR video chain study.

Abstract: Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments, and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in real training environment. We first present a system that automatically classifies a large range of training activities using the discrete wavelet transform (DWT) in conjunction with a random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the shank, thigh, and pelvis of a subject, from which the flexion-extension knee and hip angles are calculated. These angles, along with sacrum impact accelerations, are automatically extracted for each stride during jogging. Finally, normative data are generated and used to determine if a subject's movement technique differed to the normative data in order to identify potential injury-related factors. For the joint angle data, this is achieved using a curve-shift registration technique. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments for both injury management and performance enhancement.

Abstract: Moving object detection and tracking is an evolving research field due to its wide applications in traffic surveillance, 3D reconstruction, motion analysis (human and non-human), activity recognition, medical imaging etc. However real time object tracking is a challenging task due to dynamic tacking environment and different limiting parameters like view point, anthropometric variation, dimensions of an object, cluttered background, camera motions, occlusion etc. In this paper, we have developed new object detection and tracking algorithm which makes use of optical flow in conjunction with motion vector estimation for object detection and tracking in a sequence of frames. The optical flow gives valuable information about the object movement even if no quantitative parameters are computed. The motion vector estimation technique can provide an estimation of object position from consecutive frames which increases the accuracy of this algorithm and helps to provide robust result irrespective of image blur and cluttered background. The use of median filter with this algorithm makes it more robust in the presence of noise. The developed algorithm is applied to wide range of standard and real time datasets with different illumination (indoor and outdoor), object speed etc. The obtained results indicates that the developed algorithm over performs over conventional methods and state of art methods of object tracking.

Abstract: This work introduces an efficient method for fully automatic temporal segmentation of human motion sequences and similar time series. The method relies on a neighborhood graph to partition a given data sequence into distinct activities and motion primitives according to self-similar structures given in that input sequence. In particular, the fast detection of repetitions within the discovered activity segments is a crucial problem of any motion processing pipeline directed at motion analysis and synthesis. The same similarity information in the neighborhood graph is further exploited to cluster these primitives into larger entities of semantic significance. The elements subject to this classification are then used as prior for estimating the same target values for entirely unknown streams of data.The technique makes no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. Tests of our techniques are conducted on the CMU and HDM05 motion capture databases demonstrating the capability of our system handling motion segmentation, clustering, motion synthesis and transfer-of-label problems in practice - the latter being an optional step which relies on the preexistence of a small set of labeled data.

Abstract: A method that integrates sensor data and video analysis to analyze object motion. Motion capture elements generate motion sensor data for objects of interest, and cameras generate video of these objects. Sensor data and video data are synchronized in time and aligned in space on a common coordinate system. Sensor fusion is used to generate motion metrics from the combined and integrated sensor data and video data. Integration of sensor data and video data supports robust detection of events, generation of video highlight reels or epic fail reels augmented with metrics that show interesting activity, and calculation of metrics that exceed the individual capabilities of either sensors or video analysis alone.

Abstract: Although motion analysis is frequently employed in upper limb motor assessment (e.g. visually-guided reaching), they are resource-intensive and limited to laboratory settings. This study evaluated the reliability and accuracy of a new markerless motion capture device, the Leap Motion controller, to measure finger position. Testing conditions that influence reliability and agreement between the Leap and a research-grade motion capture system were examined. Nine healthy young adults pointed to 15 targets on a computer screen under two conditions: (1) touching the target (touch) and (2) 4 cm away from the target (no-touch). Leap data was compared to an Optotrak marker attached to the index finger. Across all trials, root mean square (RMS) error of the Leap system was 17.30 ± 9.56 mm (mean ± SD), sampled at 65.47 ± 21.53 Hz. The % viable trials and mean sampling rate were significantly lower in the touch condition (44% versus 64%, p < 0.001; 52.02 ± 2.93 versus 73.98 ± 4.48 Hz, p = 0.003). While linear correlations were high (horizontal: r2 = 0.995, vertical r2 = 0.945), the limits of agreement were large (horizontal: −22.02 to +26.80 mm, vertical: −29.41 to +30.14 mm). While not as precise as more sophisticated optical motion capture systems, the Leap Motion controller is sufficiently reliable for measuring motor performance in pointing tasks that do not require high positional accuracy (e.g. reaction time, Fitt’s, trails, bimanual coordination).

Abstract: Human gait has become of special interest to health professionals and researchers in recent years, not only due to its relation to a person's quality of life and personal autonomy, but also due to the involved cognitive process, since deviation from normal gait patterns can also be associated to neurological diseases. Vision-based abnormal gait detection can provide support to current human gait analysis procedures providing quantitative and objective metrics that can assist the evaluation of the geriatrician, while at the same time providing technical advantages, such as low intrusiveness and simplified setups. Furthermore, recent advances in RGB-D devices allow to provide low-cost solutions for 3D human body motion analysis. In this sense, this work presents a method for abnormal gait detection relying on skeletal pose representation based on depth data. A novel spatio-temporal feature is presented that provides a representation of a set of consecutive skeletons based on the 3D location of the skeletal joints and the motion's age. The corresponding feature sequences are learned using a machine learning method, namely BagOfKeyPoses. Experimentation with different datasets and evaluation methods shows that reliable detection of abnormal gait is obtained and, at the same time, an outstandingly high temporal performance is provided.

Abstract: Enables event analysis from sensors including environmental, physiological and motion capture sensors. Also enables displaying information based on events recognized using sensor data associated with a user, piece of equipment or based on previous motion analysis data from the user or other user(s) or other sensors. Enables intelligent analysis, synchronization, and transfer of generally concise event videos synchronized with motion data from motion capture sensor(s) coupled with a user or piece of equipment. Enables creating, transferring, obtaining, and storing concise event videos generally without non-event video. Events stored in the database identifies trends, correlations, models, and patterns in event data. Greatly saves storage and increases upload speed by uploading event videos and avoiding upload of non-pertinent portions of large videos. Creates highlight and fail reels filtered by metrics and can sort by metric. Compares motion trajectories of users and objects to optimally efficient trajectories, and to desired trajectories.

Abstract: Background/Purpose The squat is a fundamental movement of many athletic and daily activities. Methods to clinically assess the squat maneuver range from simple observation to the use of sophisticated equipment. The purpose of this study was to examine the reliability of Coach's Eye (TechSmith Corp), a 2‐dimensional (2D) motion analysis mobile device application (app), for assessing maximal sagittal plane hip, knee, and ankle motion during a functional movement screen deep squat, and to compare range of motion values generated by it to those from a Vicon (Vicon Motion Systems Ltd) 3‐dimensional (3D) motion analysis system.

Abstract: This paper proposes a novel concept for simultaneous cell shape and motion analysis in fast microchannel flows by implementing a multiobject feature extraction algorithm on a frame-straddling high-speed vision platform. The system can synchronize two camera inputs with the same view with only a tiny time delay on the sub-microsecond timescale. Real-time video processing is performed in hardware logic by extracting the moment features of multiple cells in 512 $\,\times\,$ 256 images at 4000 fps for the two camera inputs and their frame-straddling time can be adjusted from 0 to 0.25 ms in 9.9 ns steps. By setting the frame-straddling time in a certain range to avoid large image displacements between the two camera inputs, our frame-straddling high-speed vision platform can perform simultaneous shape and motion analysis of cells in fast microchannel flows of 1 m/s or greater. The results of real-time experiments conducted to analyze the deformabilities and velocities of sea urchin egg cells fast-flowing in microchannels verify the efficacy of our vision-based cell analysis system.

Abstract: A platform to move gait analysis, which is normally restricted to a clinical environment in a well-equipped gait laboratory, into an ambulatory system, potentially in non-clinical settings is introduced. This novel system can provide functional measurements to guide therapeutic interventions for people requiring rehabilitation with limited access to such gait laboratories. BioKin system consists of three layers: a low-cost wearable wireless motion capture sensor, data collection and storage engine, and the motion analysis and visualisation platform. Moreover, a novel limb orientation estimation algorithm is implemented in the motion analysis platform. The performance of the orientation estimation algorithm is validated against the orientation results from a commercial optical motion analysis system and an instrumented treadmill. The study results demonstrate a root-mean-square error less than 4° and a correlation coefficient more than 0.95 when compared with the industry standard system. These results indicate that the proposed motion analysis platform is a potential addition to existing gait laboratories in order to facilitate gait analysis in remote locations.

Abstract: Falling is a common problem in the growing elderly population, and fall-risk assessment systems are needed for community-based fall prevention programs. In particular, the timed up and go test (TUG) is the clinical test most often used to evaluate elderly individual ambulatory ability in many clinical institutions or local communities. This study presents an improved leg tracking method using a laser range sensor (LRS) for a gait measurement system to evaluate the motor function in walk tests, such as the TUG. The system tracks both legs and measures the trajectory of both legs. However, both legs might be close to each other, and one leg might be hidden from the sensor. This is especially the case during the turning motion in the TUG, where the time that a leg is hidden from the LRS is longer than that during straight walking and the moving direction rapidly changes. These situations are likely to lead to false tracking and deteriorate the measurement accuracy of the leg positions. To solve these problems, a novel data association considering gait phase and a Catmull–Rom spline-based interpolation during the occlusion are proposed. From the experimental results with young people, we confirm that the proposed methods can reduce the chances of false tracking. In addition, we verify the measurement accuracy of the leg trajectory compared to a three-dimensional motion analysis system (VICON).

Abstract: Objective To determine if a low-cost, automated motion analysis system using Microsoft Kinect could accurately measure shoulder motion and detect motion impairments in women following breast cancer surgery. Design Descriptive study of motion measured via 2 methods. Setting Academic cancer center oncology clinic. Participants 20 women (mean age = 60 yrs) were assessed for active and passive shoulder motions during a routine post-operative clinic visit (mean = 18 days after surgery) following mastectomy (n = 4) or lumpectomy (n = 16) for breast cancer. Interventions Participants performed 3 repetitions of active and passive shoulder motions on the side of the breast surgery. Arm motion was recorded using motion capture by Kinect for Windows sensor and on video. Goniometric values were determined from video recordings, while motion capture data were transformed to joint angles using 2 methods (body angle and projection angle). Main Outcome Measure Correlation of motion capture with goniometry and detection of motion limitation. Results Active shoulder motion measured with low-cost motion capture agreed well with goniometry (r = 0.70–0.80), while passive shoulder motion measurements did not correlate well. Using motion capture, it was possible to reliably identify participants whose range of shoulder motion was reduced by 40% or more. Conclusions Low-cost, automated motion analysis may be acceptable to screen for moderate to severe motion impairments in active shoulder motion. Automatic detection of motion limitation may allow quick screening to be performed in an oncologist's office and trigger timely referrals for rehabilitation.

Abstract: Motion estimation and motion analysis have an important role to play for detecting abnormal motion in surveillance videos. In this paper, we propose to use the non-extensive entropy to detect any unnaturalness in the motion over correlated video frames since it has already been proved to represent the correlated textures successfully. To achieve this end, we utilize the temporal correlation property of motion vectors over three consecutive frames to detect any motion disturbance using a weighted average of the non-extensive entropies. It is proved by the experimental results on the state-of-the-art database that the non-extensive entropy is most apt for detecting any disturbance in the continuance of motion vectors in between frames. The advantage of our approach is that no training period or normalcy reference is required since a relative disturbance in the magnitudes of motion vectors over a three-frame window gives an alarm.

Abstract: We propose a novel approach to segment hand regions in egocentric video that requires no manual labeling of training samples. The user wearing a head-mounted camera is prompted to perform a simple gesture during an initial calibration step. A combination of color and motion analysis that exploits knowledge of the expected gesture is applied on the calibration video frames to automatically label hand pixels in an unsupervised fashion. The hand pixels identified in this manner are used to train a statistical-model-based hand detector. Superpixel region growing is used to perform segmentation refinement and improve robustness to noise. Experiments show that our hand detection technique based on the proposed on-the-fly training approach significantly outperforms state-of-the-art techniques with respect to accuracy and robustness on a variety of challenging videos. This is due primarily to the fact that training samples are personalized to a specific user and environmental conditions. We also demonstrate the utility of our hand detection technique to inform an adaptive video sampling strategy that improves both computational speed and accuracy of egocentric action recognition algorithms. Finally, we offer an egocentric video dataset of an insulin self-injection procedure with action labels and hand masks that can serve towards future research on both hand detection and egocentric action recognition.

Abstract: Lagrangian theory provides a diverse set of tools for continuous motion analysis. Existing work shows the applicability of Lagrangian methods for video analysis in several aspects. In this paper we want to utilize the concept of Lagrangian measures to detect violent scenes. Therefore we propose a local feature based on the SIFT algorithm that incooperates appearance and Lagrangian based motion models. We will show that the temporal interval of the used motion information is a crucial aspect and study its influence on the classification performance. The proposed LaSIFT feature outperforms other state-of-the-art local features, in particular in uncontrolled realistic video data. We evaluate our algorithm with a bag-of-word approach. The experimental results show a significant improvement over the state-of-the-art on current violence detection datasets, i.e. Crowd Violence, Hockey Fight.

Abstract: The aim of this paper is to present a simple calibration method aimed at optimizing the kinematical invariants of a whole body motion capture model, meaning limb lengths and some of the marker placements. A case study and preliminary results are presented and give encouraging insights about the generalized use of such a method in motion analysis in sports.

Abstract: PURPOSE: accurate assessment of human joint parameters is a critical issue for the quantitative movement analysis, due to a direct influence on motion patterns. In this study three different known functional methods are experimentally compared to identify knee joint kinematics for further gait and motion analysis purposes. METHODS: taking into account the human knee physiology complexity, within its roto-translation, the study is conducted on a lower limb mechanical analogue with a polycentric hinge-based kinematic model. The device mimics a joint with a mobile axis of rotation whose position is definable. Sets of reflective markers are placed on the dummy and flexion-extension movements are imposed to the shank segment. Marker positions are acquired using an optoelectronic motion capture system (Vicon 512). RESULTS: acquired markers' positions are used as input data to the three functional methods considered. These ones approximate the polycentric knee joint with a fixed single axis model. Different ranges of motion and number of markers are considered for each functional method. RESULTS are presented through the evaluation of accuracy and precision concerning both misalignment and distance errors between the estimated axis of rotation and the instantaneous polycentric one, used as reference. CONCLUSION: the study shows the feasibility of the identification of joint parameters with functional approaches applied on a polycentric mechanism, differently from those usually conceived by the reviewed algorithms. Moreover, it quantifies and compares the approximation errors using different algorithms, by varying number and position of markers, as well ranges of motion. Language: en

Abstract: Video temporal segmentation and keyframe selection approaches for User Generated Video (UGV)Hierarchical Hidden Markov Models applied to camera motion analysis to detect motion patterns and temporally segment the videoEvaluation of the influence of camera motion over the performance of automatic UGV annotation systemsTwo datasets for User Generated Video have been developed and made publicly available In this paper we propose a temporal segmentation and a keyframe selection method for User-Generated Video (UGV) Since UGV is rarely structured in shots and usually user's interest are revealed through camera movements, a UGV temporal segmentation system has been proposed that generates a video partition based on a camera motion classification Motion-related mid-level features have been suggested to feed a Hierarchical Hidden Markov Model (HHMM) that produces a user-meaningful UGV temporal segmentation Moreover, a keyframe selection method has been proposed that picks a keyframe for fixed-content camera motion patterns such as zoom, still, or shake and a set of keyframes for varying-content translation patternsThe proposed video segmentation approach has been compared to a state-of-the-art algorithm, achieving 8% performance improvement in a segmentation-based evaluation Furthermore, a complete search-based UGV annotation system has been developed to assess the influence of the proposed algorithms on an end-user task To that purpose, two UGV datasets have been developed and made available online Specifically, the relevance of the considered camera motion types has been analyzed for these two datasets, and some guidelines are given to achieve the desired performance-complexity tradeoff The keyframe selection algorithm for varying-content translation patterns has also been assessed, revealing a notable contribution to the performance of the global UGV annotation system Finally, it has been shown that the UGV segmentation algorithm also produces improved annotation results with respect to a fixed-rate keyframe selection baseline or a traditional method relying on frame-level visual features

Abstract: Retrieval and comparative editing/modeling of motion data require temporal alignment. In other words, for such processes to perform accurately, critical features of motion sequences need to occur simultaneously. In this paper, we propose correlation-optimized time warping (CoTW) for aligning motion data. CoTW utilizes a correlation-based objective function for characterizing alignment. The method solves an optimization problem to determine the optimum warping degree for different segments of the sequence. Using segment-wise interpolated warping, smooth motion trajectories are achieved that can be readily used for animation. Our method allows for manual tuning of the parameters, resulting in high customizability with respect to the number of actions in a single sequence as well as spatial regions of interest within the character model. Moreover, measures are taken to reduce distortion caused by over-warping. The framework also allows for automatic selection of an optimum reference when multiple sequences are available. Experimental results demonstrate the very accurate performance of CoTW compared to other techniques such as dynamic time warping, derivative dynamic time warping and canonical time warping. The mentioned customization capabilities are also illustrated.

Abstract: This paper presents an approach to modeling the slippage of a robot’s rubber tracks moving on a non-deformable surface. A single horizontal deflection of a one-track lug is considered. The issue is modeled by FEM and verified by track lug deflection measurement and trajectory motion analysis. As a measurement device, a vision system is developed and configured. The vision-based measurement method can be considered as a new approach for estimating a robot’s track slippage.

Abstract: Activities of daily living of the elderly is often monitored using passive sensor networks. With the reduction of camera prices, there is a growing interest of video-based approaches to provide a smart, safe and independent living environment for the elderly. In this paper, activity level in context of tracking the movement pattern of an individual as a metric to monitor the daily living of the elderly is explored. Activity levels can be an effective indicator that would denote the amount of busyness of an individual by modelling motion features over time. The novel framework uses two different variants of the motion features captured from two camera angles and classifies them into different activity levels using neural networks. A new dataset for assisted living research called the Sheffield Activities of Daily Living (SADL) dataset is used where each activity is simulated by 6 subjects and is captured under two different illumination conditions within a simulated assisted living environment. The experiments show that the overall detection rate using a single camera setup and a dual camera setup is above 80%.

Abstract: This study aimed at assessing the applicability of a robust method to determine and correct lens distortion before using the direct linear transformation (DLT) algorithm in three-dimensional motion analysis. The known length of a rigid bar was reconstructed under different conditions of working volume (interpolation or extrapolation), number of cameras (2 or 4), position of the cameras (wide or narrow angle between optical axes), camera focal distance (4 or 8 mm) and number of control points (CPs; 8, 12, 18 or 162), through four different camera set-ups. The accuracy (percent root mean square error) of Set-up 2 (non-extrapolated working volume; two cameras; 4 mm focal distance; narrow optical axes angle) decreased with less CPs (162: 0.73%; 8: 2.78%). Set-up 1 (non-extrapolated working volume; two cameras; 8 mm focal distance; wide optical axes angle), Set-up 3 (Set-ups 1 and 2 used simultaneously) and Set-up 4 (extrapolated working volume; two cameras; 4 mm focal distance; wide optical axes angle) showed...

Abstract: Introduction: Motion analysis, particularly two-dimensional (2D) analysis, can be a useful tool for prosthetists and orthotists in their clinical practice as it provides a mechanism by which improvements in motion as a result of device use may be documented and quantified. However, usefulness of video-based 2D analysis is contingent on recording quality clinical videos using a standardized protocol. Materials and Methods: The purpose of this work was to provide practical tips for taking quality clinical videos for use in 2D motion analysis intended to enhance evidence-based practice. Conditions that affect the quality of the video include illumination, image size, camera position, steadiness of the camera, and marker placement on the patient. A bright image is needed to track the joints or segments. The camera needs to be held steady, level, and placed perpendicular to the image field. The camera should be as close as possible to the image field while maintaining sufficient space to view the activity. Correct marker placement on the joints of interest improves measurement of joint angles. Results and Conclusions: Good quality videos as part of 2D motion analysis can result in data that are comparable to that of instrumentedmotion analysis systems when used for 2D analysis, enhancing clinician’s evidence-based practice. Caremust be taken during the setup process and during data capture to ensure that the resulting video is recorded in a manner that is appropriate with respect to patient privacy concerns. (J Prosthet Orthot. 2015;27:27Y32.) KEY INDEXING TERMS: 2D motion analysis, clinical videos, gait analysis, kinematics Motion analysis, in particular gait analysis, is routinely performed by prosthetists and orthotists most commonly using visual observation alone despite moderate reliability.1 However, evidence-based practice demands that improvements in motion as a result of device use be documented. For example, the effect of an ankle-foot orthosis on sagittal plane knee angle during stance poststroke could be compared with a no-orthotic condition. Unfortunately, the criterion standard approach of computerized three-dimensional (3D) motion analysis is too expensive and time-consuming for routine use in clinical practice because it requires specialized equipment and trained personnel. It also provides a large amount of data that require complex postprocessing and are not straightforward to interpret.2Y5 With the availability of inexpensive yet good quality video cameras, video-based two-dimensional (2D) motion analysis is gaining greater attention as a practical tool for use in the clinical setting.6,7 All that is required is a camera, a tripod, a computer ormobile device, relatively inexpensive (and sometimes free) digitizing software, a recording area, a leveling device, and markers. Digital recording improves the reliability of observations about motion by allowing for repeated viewing, slow motion, and freezing of specific frames.5,8Y11 Digital recording also provides a permanent record and qualitative information about motion.11 Commercially available software (Table 1) allows for measurement of angles, distances, and timing from 2D video.12 Use of video analysis software has been shown to improve interrater agreement and angle and/or timing measurements.8 Calculation of kinematics from 2D video requires some digitization. Manual digitizing is typically used where markers are not present but is time-consuming and potentially error prone.13 Use of markers (e.g., white circular stickers 24 mm in diameter)13 may improve the quality of measurement5,10 by providing repeatable locations to digitize and facilitating automated tracking of movement by software if that feature is available. Automated digitization identifies markers based on their relative contrast with other areas of the image.5 Depending on the sagittal plane motion of interest, markers may be placed on the acromion, anterior superior iliac spine, greater trochanter, lateral femoral epicondyle, lateral malleolus, and fifth metatarsal head.5 Two-dimensional video-based motion analysis has both advantages and disadvantages compared with the criterion standard of computerized 3Dmotion analysis (summarized in Table 2). The utility of 2D video-based motion analysis is contingent upon recording goodquality clinical videos.5,13,14 Churchill et al.13 stated, ‘‘if the original video recordings were of reasonable quality, then identification of the leg markers was possible for each and every frame,’’ but guidelines as to how to capture a reasonable quality video are difficult to find. Our experience Volume 27 & Number 1 & 2015 27 STEFANIA FATONE, PhD, BPO(Hons), and REBECCA STINE, MS, are affiliated with the Northwestern University Prosthetics-Orthotics Center, Feinberg School of Medicine, Chicago, Illinois. REBECCA STINE, MS, is affiliated with the Jesse Brown VA Medical Center, Chicago, Illinois. Disclosure: The authors declare no conflict of interest. Copyright * 2014 American Academy of Orthotists and Prosthetists Correspondence to: Stefania Fatone, PhD, BPO(Hons), 680N Lake Shore Dr., Suite 1100, Chicago IL 60611; email: s-fatone@northwestern.edu This research was funded by the National Institute on Disability and Rehabilitation Research (NIDRR) of the US Department of Education under grant number H133E080009 (principal investigators, Steven Gard and Stefania Fatone). The opinions contained in this publication are those of the grantee and do not necessarily reflect those of the Department of Education. Copyright @ 2014 by the American Academy of Orthotists and Prosthetists. Unauthorized reproduction of this article is prohibited. conducting a smallmulticenter clinical trial collecting 2D videos in orthotic practices suggests that guidance is needed regarding this issue. Hence, this article provides practical tips for capturing quality clinical videos for use in 2D motion analysis intended to enhance evidence-based practice in prosthetics and orthotics. TECHNIQUE Baker et al.14 summarized the conditions for an optimal video as keep it bright, keep it big, keep it straight, and keep it steady. This referred to having good lighting, optimizing the image field to ensure that as much of the individual as needed is in view of the camera, ensuring that the camera is perpendicular to the relevant plane of movement (and thus minimizing optical errors), and using a tripod to ensure a steady image. Coutts5 and Muybridge15 also emphasized the need to standardize position of the video camera, walkway, and level of light/contrast. ILLUMINATION Having a bright image with good contrast is important,5 especially if markers are to be used to track joints or segments. Camera properties that affect the amount of light illuminating the sensor include lens size (the larger the lens, the more light will be admitted to the sensor), frame rate and/or shutter speed (increasing frame rate and/or shutter speed will reduce illumination), and aperture/gain (increasing the aperture and gain can compensate for low light levels but may increase noise on the image).11,14 Recording against a plain light-colored wall (background)15 and using floor and wall colorings that contrast with skin tones is helpful. Generally, lighter colors will reflect more light, keeping the image bright. IMAGE SIZE AND CAMERA POSITION Position of the camera with respect to the patient is important.5,15 Parallax error occurs when objects are viewed away from the optical axis of the camera, whereas perspective error is the apparent change in length of an object when it moves out Table 1. Examples of commercially available software for 2D motion analysis Software Platform Features Costs Dartfish Computer based Playback, slow motion, split screen, automatic tracking, graphic tools Low to medium Dartfish Express Mobile application Playback, slow motion, split screen, image sharing, graphic tools Free Innovision Systems, Inc. Computer based Analysis tools, automatic tracking, direct recording, Low to medium Kinovea Computer based Sports specific, open source Free SportsCad Computer based Playback, multiple video analysis, full screen analysis Low Coach’s Eye Mobile application Playback, slow motion, split screen, graphic tools, image sharing Low PnO Data Solutions Computer based Commercial postprocessing service Medium 2D, two-dimensional. Table 2. Advantages and disadvantages of using 2D video-based motion analysis Advantages Disadvantages & Permanent record/documentation of improvements in motion (especially as a result of intervention with a device) & Not as accurate as the criterion standard approach of computerized 3D motion analysis & Less expensive and time-consuming than 3D motion analysis & Measurement of angles, distances, and timing of motion events requires some digitization and takes extra time than observation alone & More practical tool for use in clinical setting as it needs only minimal equipment compared with 3D motion analysis & Difficult to interpret data when there is cross plane interaction & Improves reliability of observations about motion by allowing repeated viewing, slow motion, and freezing of specific frames & Finding space with required dimensions, lack of clutter, and good lighting can be challenging & Can allow for measurement of angles, distances, and timing of motion events & May improve interrater agreement of observations about motion & If good quality videos are used, data may be comparable to that of instrumented motion analysis systems for 2D comparisons 2D, two-dimensional; 3D, three-dimensional. Fatone et al. Journal of Prosthetics and Orthotics 28 Volume 27 & Number 1 & 2015 Copyright @ 2014 by the American Academy of Orthotists and Prosthetists. Unauthorized reproduction of this article is prohibited. of the photographic plane.16Y18 In practical terms, these optical errors make segments or angles appear larger or smaller than reality depending on the distance/angle of the