scholarly journals Simultaneous Prediction of Wrist/Hand Motion via Wearable Ultrasound Sensing

2020 ◽  
Vol 28 (4) ◽  
pp. 970-977 ◽  
Author(s):  
Xingchen Yang ◽  
Jipeng Yan ◽  
Yinfeng Fang ◽  
Dalin Zhou ◽  
Honghai Liu
Keyword(s):  
Hand Motion ◽  
Simultaneous Prediction ◽  
Ultrasound Sensing

scholarly journals Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Da Un Jeong ◽  
Ki Moo Lim
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Measurement ◽  
Patient Specific ◽  
Peak Time ◽  
Simultaneous Prediction ◽  
Deep Cnn ◽  
Pressure Estimation ◽  
The Difference ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure

AbstractThe pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

scholarly journals A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3035
Author(s):  
Néstor J. Jarque-Bou ◽  
Joaquín L. Sancho-Bru ◽  
Margarita Vergara
Keyword(s):  
Activities Of Daily Living ◽  
Musculoskeletal System ◽  
Muscle Activity ◽  
Daily Living ◽  
Muscle Activation ◽  
Human Hand ◽  
Hand Motion ◽  
Biomechanical Models ◽  
Rehabilitation Protocols ◽  
Open Issues

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.

scholarly journals Visual outcome of endogenous endophthalmitis in Thailand

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Preeyachan Lourthai ◽  
Pitipol Choopong ◽  
Dhanach Dhirachaikulpanich ◽  
Kunravitch Soraprajum ◽  
Warinyupa Pinitpuwadol ◽  
...  
Keyword(s):  
Retrospective Chart Review ◽  
Visual Outcome ◽  
Post Treatment ◽  
Endogenous Endophthalmitis ◽  
Infectious Episode ◽  
Hand Motion ◽  
Phthisis Bulbi ◽  
Causative Agents ◽  
Corneal Decompensation ◽  
The Mean

AbstractTo evaluate a 10-year visual outcome of endogenous endophthalmitis (EE) patients. A 10-year retrospective chart review of EE patients. Thirty-eight patients (40 eyes) were diagnosed with EE at the mean age of 42. Among the identifiable pathogens (71.1% culture positive), the causative agents were predominantly gram-negative bacteria (48.1%). The most common specie was Klebsiella pneumoniae (25.9%). About a quarter of the patients required surgical eye removal, and the remaining 45.7% had visual acuity (VA) worse than hand motion at one month after the infectious episode. The most common complication was ocular hypertension (52.5%). Poor initial VA was significantly associated with a worse visual outcome in the early post-treatment period (p 0.12, adjusted OR 10.20, 95% CI 1.65–62.96). Five patients continued to visit the clinic for at least ten years. One patient had gained his vision from hand motion to 6/7.5. Two patients had visual deterioration, one from corneal decompensation, and the other from chronic retinal re-detachment. Two patients developed phthisis bulbi, with either some VA perception of light or no light perception. Poor initial VA is the only prognostic factor of a poor early post-treatment visual outcome of EE.

Retinal Vasculitis Associated With Dermatomyositis Treated With Intravenous Steroids

2021 ◽  
pp. 247412642110189
Author(s):  
Austen N. Knapp ◽  
Jawad I. Arshad ◽  
Daniel F. Martin ◽  
Rula Hajj-Ali ◽  
Kimberly Baynes ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Macular Edema ◽  
Retinal Vasculitis ◽  
Subretinal Fluid ◽  
Retrospective Case ◽  
Hand Motion ◽  
Frosted Branch Angiitis ◽  
Intravenous Steroids

Purpose: This work aims to present treatment and long-term follow-up of a 31-year-old woman with dermatomyositis who presented with hemorrhagic retinal vasculitis and macular edema. Methods: A retrospective case report is presented. Results: A 31-year-old woman with dermatomyositis treated with systemic immunosuppression was evaluated for acute, reduced vision. Best-corrected visual acuity was hand motion in the right eye and 20/200 in the left eye. Fundus examination revealed diffuse intraretinal hemorrhages, cotton-wool spots, and vascular sheathing with a frosted branch angiitis–like appearance. Optical coherence tomography revealed significant macular edema and subretinal fluid that quickly resolved after admission and treatment with intravenous steroids. Multimodal imaging at 7-year follow-up disclosed long-term sequelae including peripheral nonperfusion and retinal neovascularization. Conclusions: When vasculitis associated with dermatomyositis is treated aggressively with intravenous steroids at initial presentation, good visual acuity outcomes can be achieved, but long-term consequences of retinal nonperfusion and neovascularization persist.

scholarly journals An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hong-Min Zhu ◽  
Chi-Man Pun
Keyword(s):  
Template Matching ◽  
Tracking System ◽  
Recognition System ◽  
Appearance Model ◽  
Hand Gesture ◽  
Svm Classifier ◽  
Motion Trajectory ◽  
Hand Motion ◽  
Target Hand ◽  
Gesture Tracking

We propose an adaptive and robust superpixel based hand gesture tracking system, in which hand gestures drawn in free air are recognized from their motion trajectories. First we employed the motion detection of superpixels and unsupervised image segmentation to detect the moving target hand using the first few frames of the input video sequence. Then the hand appearance model is constructed from its surrounding superpixels. By incorporating the failure recovery and template matching in the tracking process, the target hand is tracked by an adaptive superpixel based tracking algorithm, where the problem of hand deformation, view-dependent appearance invariance, fast motion, and background confusion can be well handled to extract the correct hand motion trajectory. Finally, the hand gesture is recognized by the extracted motion trajectory with a trained SVM classifier. Experimental results show that our proposed system can achieve better performance compared to the existing state-of-the-art methods with the recognition accuracy 99.17% for easy set and 98.57 for hard set.

Assessing Learning Effort with Hand Motion Tracking Methods

2020 ◽  
Author(s):  
Hansol Rheem ◽  
David V. Becker ◽  
Scotty D. Craig
Keyword(s):  
Motion Tracking ◽  
Hand Motion
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