Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Rotation Correction Method Using Depth-Value Symmetry of Human Skeletal Joints for Single RGB-D Camera System

Most red-green-blue and depth (RGB-D) motion-recognition technologies employ both depth and RGB cameras to recognize a user\u27s body. However, motion-recognition solutions using a single RGB-D camera struggle with rotation recognition depending on the device-user distance and field-of-view. This paper proposes a near-real-time rotational-coordinate-correction method that rectifies a depth error unique Microsoft Kinect by using the symmetry of the depth coordinates of the human body. The proposed method is most effective within 2 m, a key range in which the unique depth error of Kinect occurs, and is anticipated to be utilized in applications requiring low cost and fast installation. It could also be useful in areas such as media art that involve unspecified users because it does not require a learning phase. Experimental results indicate that the proposed method has an accuracy of 85.38%, which is approximately 12% higher than that of the reference installation method

Quantifying Upper-Limb Bimanual Coordination Performance Using Machine Learning Techniques for Concussion Screening

Current concussion screening and diagnosis tools rely on symptom checklist scores, along with subjective assessments performed by a clinician. This introduces high variability and bias, and increases the chances of missed diagnoses, which could lead to inappropriate return to play decisions resulting in dire consequences on the athlete’s health, especially in the case of a second hit. While limited, objective measures for motor assessment exist, they are generally infeasible for use as sideline screening tools. Impaired bimanual motor coordination is one of the major motor deficits that individuals with a concussion can experience. Measuring the degree of impairment in bimanual coordination could be an effective metric for concussion screening. These metrics would provide objective means for sideline screening that are more effective than the currently employed subjective assessments. However, bimanual coordination metrics that are sensitive to concussion remain unknown. Furthermore, a feasible testing paradigm that permits obtaining such metrics on the sideline is also lacking. This thesis contributes the design and evaluation of a novel tool that can be implemented and used in practice, on the sideline of sporting events, to detect coordination impairment associated with concussion objectively. In the first component of this work, a novel testing paradigm for bimanual motor control assessment is proposed and analyzed. Comprising of a simple 1Hz in-phase vertical bimanual movement, the proposed bimanual coordination paradigm requires individuals to integrate multiple sources of sensory information simultaneously (i.e., visual, auditory) to produce a successful arm coordination pattern. The most informative metrics or features, including power-based features, and average peak-to-peak distance were extracted and analyzed to identify metrics that are sensitive to motor deficits, pointing to potential concussion. A machine learning model was developed to distinguish athletes with a concussion and on-going symptoms (CON-S) from healthy controls (HC) using the extracted features from their kinematic data. The proposed method was able to identify concussion with an average accuracy of 86% using a logistic regression model, and 88% using an Adaboost classifier. Issues arise with difficulties in acquiring the required kinematic data, wherein tools currently in use for such applications are expensive, limited to laboratory settings and time consuming. Current gold standard methods are dominated by motion capture, which significantly limits the feasibility of using the proposed paradigm on the sidelines. As such, a portable, cost-effective, and rapid method for data collection is essential. One promising alternative is the utility of computer vision techniques. Utilizing such a method would allow data collection to be performed using devices with a camera, such as a smart phone, in a wide range of settings or environments, without the need for extensive calibration or markers like a motion capture system. In the second component of this thesis, a collection tool utilizing computer vision is proposed and tested for kinematic assessment, and its accuracy was compared to a research-grade motion capture device. Using a video sampling at 120 fps, an average peak-to-peak error of 6.96 mm was obtained. The overall proposed system utilizes computer vision to measure arm motion kinematics and assess bimanual motor coordination, which is expected to deteriorate following a concussion. Proof of concept analyses indicate that the extracted features are able to identify concussion effectively. This tool would be of benefit for quick, portable, and objective sideline concussion screening which has the potential to reduce missed diagnoses and inappropriate return to play decisions to prevent further injury

An Objective Balance Error Scoring System for Sideline Concussion Evaluation Using Duplex Kinect Sensors

Sports-related concussion is a common sports injury that might induce potential long-term consequences without early diagnosis and intervention in the field. However, there are few options of such sensor systems available. The aim of the study is to propose and validate an automated concussion administration and scoring approach, which is objective, affordable and capable of detecting all balance errors required by the balance error scoring system (BESS) protocol in the field condition. Our approach is first to capture human body skeleton positions using two Microsoft Kinect sensors in the proposed configuration and merge the data by a custom-made algorithm to remove the self-occlusion of limbs. The standing balance errors according to BESS protocol were further measured and accessed automatically by the proposed algorithm. Simultaneously, the BESS test was filmed for scoring by an experienced rater. Two results were compared using Pearson coefficient r, obtaining an excellent consistency (r = 0.93, p < 0.05). In addition, BESS test–retest was performed after seven days and compared using intraclass correlation coefficients (ICC), showing a good test–retest reliability (ICC = 0.81, p < 0.01). The proposed approach could be an alternative of objective tools to assess postural stability for sideline sports concussion diagnosis

An Objective Balance Error Scoring System for Sideline Concussion Evaluation Using Duplex Kinect Sensors

Sports-related concussion is a common sports injury that might induce potential long-term consequences without early diagnosis and intervention in the field. However, there are few options of such sensor systems available. The aim of the study is to propose and validate an automated concussion administration and scoring approach, which is objective, affordable and capable of detecting all balance errors required by the balance error scoring system (BESS) protocol in the field condition. Our approach is first to capture human body skeleton positions using two Microsoft Kinect sensors in the proposed configuration and merge the data by a custom-made algorithm to remove the self-occlusion of limbs. The standing balance errors according to BESS protocol were further measured and accessed automatically by the proposed algorithm. Simultaneously, the BESS test was filmed for scoring by an experienced rater. Two results were compared using Pearson coefficient r, obtaining an excellent consistency (r = 0.93, p < 0.05). In addition, BESS test–retest was performed after seven days and compared using intraclass correlation coefficients (ICC), showing a good test–retest reliability (ICC = 0.81, p < 0.01). The proposed approach could be an alternative of objective tools to assess postural stability for sideline sports concussion diagnosis

A Systematic Review and Meta-Analysis of the Incidence of Injury in Professional Female Soccer

The epidemiology of injury in male professional football is well documented and has been used as a basis to monitor injury trends and implement injury prevention strategies. There are no systematic reviews that have investigated injury incidence in women’s professional football. Therefore, the extent of injury burden in women’s professional football remains unknown. PURPOSE: The primary aim of this study was to calculate an overall incidence rate of injury in senior female professional soccer. The secondary aims were to provide an incidence rate for training and match play. METHODS: PubMed, Discover, EBSCO, Embase and ScienceDirect electronic databases were searched from inception to September 2018. Two reviewers independently assessed study quality using the Strengthening the Reporting of Observational Studies in Epidemiology statement using a 22-item STROBE checklist. Seven prospective studies (n=1137 professional players) were combined in a pooled analysis of injury incidence using a mixed effects model. Heterogeneity was evaluated using the Cochrane Q statistic and I2. RESULTS: The epidemiological incidence proportion over one season was 0.62 (95% CI 0.59 - 0.64). Mean total incidence of injury was 3.15 (95% CI 1.54 - 4.75) injuries per 1000 hours. The mean incidence of injury during match play was 10.72 (95% CI 9.11 - 12.33) and during training was 2.21 (95% CI 0.96 - 3.45). Data analysis found a significant level of heterogeneity (total Incidence, X2 = 16.57 P < 0.05; I2 = 63.8%) and during subsequent sub group analyses in those studies reviewed (match incidence, X2 = 76.4 (d.f. = 7), P <0.05; I2 = 90.8%, training incidence, X2 = 16.97 (d.f. = 7), P < 0.05; I2 = 58.8%). Appraisal of the study methodologies revealed inconsistency in the use of injury terminology, data collection procedures and calculation of exposure by researchers. Such inconsistencies likely contribute to the large variance in the incidence and prevalence of injury reported. CONCLUSIONS: The estimated risk of sustaining at least one injury over one football season is 62%. Continued reporting of heterogeneous results in population samples limits meaningful comparison of studies. Standardising the criteria used to attribute injury and activity coupled with more accurate methods of calculating exposure will overcome such limitations