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Human Blastocyst's Zona Pellucida Segmentation via Boosting Ensemble of Complementary Learning

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-10-25
Abstract: 

Characteristics of Zona Pellucida (ZP), particularly its thickness, is a key indicator of human blastocyst (day-5embryo) quality. Therefore, ZP segmentation is an important step towards achieving automatic embryo qualityassessment. In this paper, a novel approach based on boosting ensemble of hybrid complementary learning isproposed to segment Zona Pellucida in human blastocyst images. First, an inner-ZP localization method isproposed to separate the ZP from the heavily textured area inside a blastocyst. Then, a deep Hierarchical NeuralNetwork (HiNN) is proposed to segment ZP area. The hierarchical nature of the proposed network enableslearning features with respect to their spatial location in the embryo. Finally, a Self-supervised Image-SpecificRefinement (SISR) strategy is proposed as a complementary step to boost the performance. The proposed systemis a hybrid approach in the sense that the HiNN learns the intra-correlation among images, while the SISR takesinto account the inter-correlation within the query image. Experimental results confirm that the proposed method is capable of identifying ZP area with average Precision, Recall, Accuracy and Jaccard Index of 85.2%, 92.0%, 95.6% and 78.1%, respectively. The proposed HiNN system outperforms state of the art by 4.9% in Precision, 11.2% in Recall, 3.6% in Accuracy and 10.7% in Jaccard Index.

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Article
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Regressing Grasping Using Force Myography: An Exploratory Study

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-10-23
Abstract: 

Background: Partial hand amputation forms more than 90% of all upper limb amputations. This amputation has a notable efect on the amputee’s life. To improve the quality of life for partial hand amputees diferent prosthesis options, including externallypowered prosthesis, have been investigated. The focus of this work is to explore force myography (FMG) as a technique for regressing grasping movement accompanied by wrist position variations. This study can lay the groundwork for a future investigation of FMG as a technique for controlling externally-powered prostheses continuously. Methods: Ten able-bodied participants performed three hand movements while their wrist was fxed in one of six predefned positions. The angle between Thumb and Index fnger (θTI), and Thumb and Middle fnger (θTM) were calculated as measures of grasping movements. Two approaches were examined for estimating each angle: (i) one regression model, trained on data from all wrist positions and hand movements; (ii) a classifer that identifed the wrist position followed by a separate regression model for each wrist position. The possibility of training the system using a limited number of wrist positions and testing it on all positions was also investigated. Results: The frst approach had a correlation of determination (R2) of 0.871 for θTI and R2 θTM = 0.941. Using the second approach R2 θTI = 0.874 and R2 θTM = 0.942 were obtained. The frst approach is over two times faster than the second approach while having similar performance; thus the frst approach was selected to investigate the efect of the wrist position variations. Training with 6 or 5 wrist positions yielded results which were not statistically signifcant. A statistically signifcant decrease in performance resulted when less than fve wrist positions were used for training. Conclusions: The results indicate the potential of FMG to regress grasping movement, accompanied by wrist position variations, with a regression model for each angle. Also, it is necessary to include more than one wrist position in the training phase.

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Article
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Simplifying Through-Forest Propagation Modelling

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2020-01-23
Abstract: 

Propagation analysis and modeling is critical for radio systems design, but remains a challenge for most through-vegetation situations, including forests. Transmission through such inhomogeneous mixed media is complicated by the many different propagation mechanisms and the complexity of the randomness. This means that accurate, purely physics-based analysis is unlikely to be practical (conveniently computed), and similarly, that practical, purely random modeling is unlikely to be accurate. Through-vegetation propagation models, including the standard radiative energy transfer (RET), are not very accurate in the sense that the uncertainty can be tens of dB, and this seems to be an accepted limitation for vegetation. A simpler propagation model, which maintains or improves accuracy, but keeps a reasonable association with the physics, would be insightful. This paper discusses such a model. It comprises two parallel transmission mechanisms: direct transmission through a succession of trees, which is modeled by a simple linear transmission line; and transmission across the forest top, which is modeled by simplified multiple-edge diffraction. The model is examined using recently-published experiments over a long path-length. It is demonstrated that this two-mechanism model can provide an accurate fit to the dual-slope profile of through-forest propagation over a long distance which is not possible with the RET model.

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The Data Gap in Sports Analytics and How to Close It

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2019-11-19
Abstract: 

As the importance and prevalence of sports analytics grows, so does the inequality in sports data. In this paper we examine two main sources of such disparity - the perceived hierarchy of sports and privatization of data. We argue that such inequality hurts the sports analytics community in the short and long terms, and suggest ways for the deep-learning, AI, and sports analytics communities to help mitigate the issue. Keywords: Sports Analytics; AI; Team Sports; Diversity

Document type: 
Article

Perceptions of Existing Wearable Robotic Devices for Upper Extremity and Suggestions for Their Development: Findings From Therapists and People With Stroke

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-05-15
Abstract: 

Background: Advances in wearable robotic technologies have increased the potential of these devices for rehabilitation and as assistive devices. However, the utilization of these devices is still limited and there are questions regarding how well these devices address users’ (therapists and patients) needs.

Objective: The aims of this study were to (1) describe users’ perceptions about existing wearable robotic devices for the upper extremity; (2) identify if there is a need to develop new devices for the upper extremity and the desired features; and (3) explore obstacles that would influence the utilization of these new devices.

Methods: Focus groups were held to collect data. Data were analyzed thematically.

Results: A total of 16 participants took part in the focus group discussions. Our analysis identified three main themes: (1) “They exist, but...” described participants’ perceptions about existing devices for upper extremity; (2) “Indeed, we need more, can we have it all?” reflected participants’ desire to have new devices for the upper extremity and revealed heterogeneity among different participants; and (3) “Bumps on the road” identified challenges that the participants felt needed to be taken into consideration during the development of these devices.

Conclusions: This study resonates with previous research that has highlighted the importance of involving end users in the design process. The study suggests that having a single solution for stroke rehabilitation or assistance could be challenging or even impossible, and thus, engineers should clearly identify the targeted stroke population needs before the design of any device for the upper extremity.

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A Wearable Gait Phase Detection System Based on Force Myography Techniques

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2018-04-21
Abstract: 

(1) Background: Quantitative evaluation of gait parameters can provide useful information for constructing individuals’ gait profile, diagnosing gait abnormalities, and better planning of rehabilitation schemes to restore normal gait pattern. Objective determination of gait phases in a gait cycle is a key requirement in gait analysis applications; (2) Methods: In this study, the feasibility of using a force myography-based technique for a wearable gait phase detection system is explored. In this regard, a force myography band is developed and tested with nine participants walking on a treadmill. The collected force myography data are first examined sample-by-sample and classified into four phases using Linear Discriminant Analysis. The gait phase events are then detected from these classified samples using a set of supervisory rules; (3) Results: The results show that the force myography band can correctly detect more than 99.9% of gait phases with zero insertions and only four deletions over 12,965 gait phase segments. The average temporal error of gait phase detection is 55.2 ms, which translates into 2.1% error with respect to the corresponding labelled stride duration; (4) Conclusions: This proof-of-concept study demonstrates the feasibility of force myography techniques as viable solutions in developing wearable gait phase detection systems.

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Article
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Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2019-11-22
Abstract: 

Hand force estimation is critical for applications that involve physical human-machine interactions for force monitoring and machine control. Force Myography (FMG) is a potential technique to be used for estimating hand force/torque. The FMG signals reflect the volumetric changes in the arm muscles due to muscle contraction or expansion. This paper investigates the feasibility of employing force-sensing resistors (FSRs) worn on the arm to measure the FMG signals for isometric force/torque estimation. Nine participants were recruited in this study and were asked to exert isometric force along three perpendicular axes, torque about the same three axes, and force and torque simultaneously. During the tests, the isometric force and torque were measured using a 6-degree-of-freedom (DoF) (i.e., force in three axes and torque around the same axes) load cell for ground truth labels whereas the FMG signals were recorded using a total number of 60 FSRs, which were embedded into four bands worn on the different locations of the arm. A two-stage regression strategy was employed to enhance the performance of the FMG bands, where three regression algorithms including general regression neural network (GRNN), support vector regression (SVR), and random forest regression (RF) models were employed, respectively, in the first stage and GRNN was used in the second stage. Two cases were considered to explore the performance of the FMG bands in estimating: (1) 3-DoF force and 3-DoF torque at once and (2) 6-DoF force and torque. In addition, the impact of sensor placement and the spatial coverage of FMG measurements were studied. This preliminary investigation demonstrates promising potential of FMG to estimate multi-DoF isometric force/torque. Specifically, R2 accuracies of 0.83 for the 3-DoF force, 0.84 for 3-DoF torque, and 0.77 for the combination of force and torque (6-DoF) regressions were obtained using the four bands on the arm in cross-trial evaluation.

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Article
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Hand Motion and Posture Recognition in a Network of Calibrated Cameras

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2017-10-31
Abstract: 

This paper presents a vision-based approach for hand gesture recognition which combines both trajectory and hand posture recognition. The hand area is segmented by fixed-range CbCr from cluttered and moving backgrounds and tracked by Kalman Filter. With the tracking results of two calibrated cameras, the 3D hand motion trajectory can be reconstructed. It is then modeled by dynamic movement primitives and a support vector machine is trained for trajectory recognition. Scale-invariant feature transform is employed to extract features on segmented hand postures, and a novel strategy for hand posture recognition is proposed. A gesture vector is introduced to recognize hand gesture as an entirety which combines the recognition results of motion trajectory and hand postures where a support vector machine is trained for gesture recognition based on gesture vectors.

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Article
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Multimodal Sensing Interface for Haptic Interaction

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2017-04-04
Abstract: 

This paper investigates the integration of a multimodal sensing system for exploring limits of vibrato tactile haptic feedback when interacting with 3D representation of real objects. In this study, the spatial locations of the objects are mapped to the work volume of the user using a Kinect sensor. The position of the user’s hand is obtained using the marker-based visual processing. The depth information is used to build a vibrotactile map on a haptic glove enhanced with vibration motors. The users can perceive the location and dimension of remote objects by moving their hand inside a scanning region. A marker detection camera provides the location and orientation of the user’s hand (glove) to map the corresponding tactile message. A preliminary study was conducted to explore how different users can perceive such haptic experiences. Factors such as total number of objects detected, object separation resolution, and dimension-based and shape-based discrimination were evaluated. The preliminary results showed that the localization and counting of objects can be attained with a high degree of success. The users were able to classify groups of objects of different dimensions based on the perceived haptic feedback.

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