Showing 1–12 of 34 results
Changes in the Perception of Postoperative Delirium Before and After a Simulated Experience of Postoperative Delirium in Nursing Students
Abstract: The purpose of this study is to use Unity to recreate the hallucinations and auditory
hallucinations experienced by patients who develop delirium, and to use VR to reveal the changes in perception of delirium before and after the simulated experience of delirium to nursing college students. We used VR to recreate the ICU at night and created a video of a simulated postoperative delirium experience. The duration is 12 minutes. We set up scenes of cockroaches appearing on the ceiling, the ceiling closing in on them, people in protective clothing, and soldiers attacking at two-minute intervals. Oculus Quest was used as the head-mounted display (HMD) for viewing the VR images. The target participants were 17 students in the second to the fourth year of nursing college. The target students were asked to answer two questions before and after the viewing. The interview content was analyzed by comparing the differences in speaking time, amount of speech and content before and after the VR viewing for each student and by text mining. The results showed that using VR to simulate postoperative delirium can lead to a change in perception from understanding the inner life of patients with postoperative delirium.
Geometric Shape Statistical Analysis of Tibial Plafond with Ankle Osteoarthritis
Abstract: The etiology of ankle osteoarthritis is not enough elucidated, and similar to hip and knee
osteoarthritis, the ankle osteoarthritis caused cartilage loss and pain in daily life. However, the ankle osteoarthritis has a worse prognosis than the hip or the knee osteoarthritis. Considering the ankle osteoarthritis skeletal structure is important in selecting the appropriate surgery for the patient. Selecting the appropriate surgery will lead to an improved prognosis. X-ray images and Computed Tomography (CT) scan images are usually used to classify ankle osteoarthritis, but the evaluation of 3D bone structure is difficult and the classification based on two-dimensional measurements of X-ray and CT images may vary among medical doctors. The purpose of this study is to investigate the three-dimensional geometric deformation characteristics of tibial plafond by using statistical analysis. Deformation characteristics were found in high severity ankle osteoarthritis compared with mild group. In particular, there was a statistically significant difference in the varus deformity of the articular facet of medial malleolus and tibial plafond between stage 3B and mild group (p < 0.006), and the hyperostosis of the medial malleolus between stage 4 and mild group (p < 0.002). These results suggest that patients with severe ankle osteoarthritis have common deformity characteristics in the tibial plafond. On the contrary, there was no significant difference in the deformity of the edge of anterior and posterior tibial plafond. Such phenomena suggest that the edge of anterior and posterior tibial plafond deforms regardless of ankle osteoarthritis. This study contributes to the scientific advancement of ankle osteoarthritis surgery.
Audio-based Wearable Contexts Recognition System for Apnea Detection
Abstract: Apnea or Sleep Apnea Syndrome is a condition when a person unconsciously stops
breathing during a sleeping state for longer than a certain time. Long-term and multiple apnea events induce various impairments. However, apnea detection in hospitals is an intensive and complicated procedure and this causes highly undiagnosed and low awareness of the disease. Existing wearable devices for apnea detections mostly used heartbeat signal patterns and SpO2 levels to detect the disease, however since apnea is a respiratory impairment, it is believed that using a breathing pattern is the most straightforward approach in apnea detection. Several recent studies investigated that swallowing frequency during sleep can increase along with the apnea severity. However, the number of wearable devices using swallowing to detect apnea is very limited. Thus, this study proposes a wearable system to recognize human contexts such as breathing, heartbeat pattern, and swallowing using an audio sensor. Experiments were conducted to compare and obtain the most suitable parameters for the system such as window sizes, types of audio feature values, and classification algorithms. The prototype of the device was built and able to detect breathing, swallowing, heartbeat, oral sounds, and body movement. The result shows the best accuracy of 76.9% using 1s window size and Mel’s Frequency Cepstral Coefficient (MFCC) features in contact microphone data.
Evaluation of Markerless Gait Analysis Method Including Out of Camera Plane Rotate Motion During Gait
Abstract: A RGB camera gait analysis system that does not require markers, large space, and
preparation can provide valuable information for effective treatment decisions in clinical settings. In this paper, we propose a simple markerless gait analysis method that can measure even if the rotation angle of the foot changes. The proposed method combines OpenPose (OP) and IMU measurement data using a complementary filter as a sensor fusion method to improve the measurement accuracy of the ankle joint angle, which is predicted to be less accurate for gait with a large foot rotation angle. Nine healthy adult males walked at a self-selected comfortable speed in two different foot-progression angle gait conditions. Spatio-temporal parameters and lower limb joint angles in the two gait conditions were measured. The mean absolute error (MAE) and the coefficient of cross-correlation (CCC) were calculated to evaluate the accuracy. The spatio-temporal parameters measured by the proposed method had low MAE compared with a conventional markerless method. The similarity between the changes in the angles of the hip and knee joints and the changes in the angles measured by a three-dimensional motion capture system was found to be very strongly correlated (CCC > 0.7). The MAE of the hip and
knee joint angles measured by the proposed method was small compared with a conventional markerless method. In particular, the proposed method was able to improve the measurement accuracy of the ankle angle by using two IMUs. The experimental results suggest that the proposed method can be used for simple and accurate measurement even when the rotation angle of the foot changes. Although the proposed method has some limitations, it has great potential as a simple and reliable gait analysis system in the clinical field.
Effect of the random forest with recursive feature elimination for breast cancer classification using a WDBC dataset
Abstract: A breast cancer is the most dangerous disease of the death cause among aged 40-55 women. We need a computer aided diagnosis system for breast cancer classification. In the previous study, the random forest which is known as an ensemble learning method was reported to be one of promising classifiers for classifying breast cancers using a Wisconsin Diagnostic Breast Cancer(WDBC) dataset. This paper presents the effect of the random forest with a recursive feature elimination for breast cancer classification on the WDBC dataset, compared to the state of the art ensemble learning techniques, such as XGBoost and LightGBM.
The Comparison of Two-Classes Basic Emotion Classification Methods Using a Single Heart rate change Parameter
Abstract: Emotion is a multifaceted phenomenon that plays a critical role in enhancing one’s quality of life by influencing motivation, perception, cognition, creativity, empathy, education, and decision-making. Additionally, negative emotions such as anger, shame, and anxiety are frequently triggered by stress, and the term destructive and threatening is used to indicate a connection between them. As a result, research into emotion recognition remains a critical issue at the moment. This study enrolled fifteen male university students. The heart rate was determined using a fingertip photoplethysmograph (PPG). The International Affective Picture System (IAPS) was used in this study to facilitate emotion changes. We used the Self-Assessment Manikin (SAM) to evaluate the subject’s emotions during the psychological assessment. As a pre-processing method, the FIR Band Pass Filter was established, and a single parameter called Heart rate change (HRC) was extracted from a PPG recording. Rather than employing complex classification techniques, we used binary classifiers such as logistic regression, Naïve Bayes, and Support Vector Machine (SVM) to distinguish between negative and positive emotions. We discovered that Naïve Bayes could provide greater than 50% accuracy and Area Under Curve (AUC) compared to the others using data from 30%, 40%, and 50% test sizes, respectively, particularly happiness (positive emotion) and anger (negative emotion). We concluded that the HRC as a single parameter could be considered the fundamental emotion classifier, though further research is necessary.
Keywords: Emotions; Binary Classifier; SAM; Photoplethysmograph
A Study on Smart Home Voice Control TerminalOpen Access
Abstract—With the development of the smart home, people are not only satisfied to control the home appliances and lights remotely by pressing the button. If people can make full use of voice as the most effective way to communicate information, it will make the smart home more convenient in control.
This paper describes the ARM microprocessor, speech recognition chip, voice broadcast module, and NRF24L01 wireless transceiver module. The voice control system of smart home, which is composed of sensor detection and other main modules, is different from the mainstream smart home control products in the market, such as Xiaomi Intelligent Audio. Its input device is portable wearable. When it is used, what you do is only to touch the button to start the recognition mode. Most importantly, it includes the function of voice broadcast so that it can let users achieve simple interaction.
Keywords—Arm microcontroller; Speech recognition; Wireless transceiver; Voice broadcast
Decision Making Using Fuzzy Cognitive Maps in Post-Triage of Non-Critical Elderly PatientsOpen Access
Abstract: For patients arriving in the Emergency Departments (EDs) of hospitals a key aspect is to classify patients and identify high-risk patients since they have the potential for rapid deterioration during the waiting time. Triage is a widely applied and well-known process of evaluating and categorizing patients’ condition, in EDs. On the other hand, EDs are frequently overcrowded, which makes triage an extremely challenging and demanding process in order to ensure that patients stepping into the ED are given the appropriate medical attention in time. This paper discusses the introduction of a general decision making procedure based on Fuzzy Cognitive Maps so that to create a Medical Decision Support System for Post-Triage decisions. The case of non-emergent and non-urgent elderly patients is examined and the corresponding model is developed.
Keywords: Soft Computing; Medical Decision Support; Triage Assessment; Fuzzy Cognitive Maps
Comparing Two Feature Selection Methods for Influenza-A Antivral Resistance Determination.Open Access
Abstract: The paper thoroughly analyzes the use of Principal Component Analysis (PCA) in comparison to Information Gain (IG) as a feature selection method for improving the classification of Influenza-A antiviral resistance. Neural networks were used as the classification method of choice with PCA, while decision trees were the classification of choice with IG. The experiment was conducted on cDNA viral segments of Influenza-A belonging to the H1N1 strain. The 7 Infleunza-A segments generating the best results were used for comparison. Sequences from each segment were further divided into Adamantane-resistant, & non-Adamantane-resistant. Accuracy, sensitivity, specificity precision & time were used as performance measures. Using PCA for feature selection increased preprocessing speeds from an average processing time of 1.5 hours to 5 minutes, as opposed to IG. IG had higher accuracy. The best accuracy generated by PCA & NNs on the M1/M2 was 96.5%, while that of IG & DTs was 98.2% Using PCA features & DTs also generated a comparable accuracy to that of IG features & DT at 97.6% on the M1/M2 segment. There was a 88% increase in feature selection processing speed when using PCA compared to IG on the M1/M2 segment alone
Keywords: Influenza-A, Principle component analysis (PCA), Machine learning, Information gain, DNA classification, decision trees, neural networks.
Construction of a focused ultrasound neuromodulation system for the treatment of epileptic seizureOpen Access
Abstract: Thus far, Ultrasound has been proved to be useful for noninvasively stimulating brain activity and has hope to play a positive role in the treatment of neurological diseases. Epilepsy is a neurological disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior, and sometimes loss of awareness. In this paper, we established a focused ultrasound (FUS) neuromodulation system for the treatment of epilepsy. We used the chemically-induced rat epilepsy model to explore the effect of pulsed focused ultrasound on epilepsy, and obtained preliminary experimental results. We have proved the feasibility of this system through experiments, which can be used in the treatment of epilepsy by ultrasound neuromodulation.
Image Segmentation-Based Face Tracking on Thermal Images for Automatic Estimation of Psychophysiological States Using Facial Skin Temperature DistributionOpen Access
Abstract: In human-machine system, human and machine need to recognize each other’s state with continuously, quantitatively and real-time property. Facial skin temperature could be measured with these properties by infrared thermography. The non-contact property is a great advantage in bioinstrumentation. Previous studies have been reported the availability of facial skin temperature for evaluation of psychophysiological states of a human such as stress, drowsiness and emotion. On the other hand, the development of the face detection and tracking techniques on thermal images are necessary for the automatic evaluation of psychophysiological states of a human based on facial skin temperature, measured by infrared thermography. The objective of this study is to establish the technique for face detection and tracking on thermal images. In this study, the algorithm consisting of three phases: (A) human detection based on inter-frame difference, (B) face detection based on image segmentation, and (C) face tracking based on temporal analysis, is proposed. As a result, the face region on thermal images could be detected and tracked with high precision. However, a part with low temperature such as the back of nasal and cheek was classified as a region other than a face.
Keywords: Thermal image; face tracking; image segmentation
Effect of Shoes on Lower Extremity Pain and Low Back during ProlongedOpen Access
Abstract: Media with 16o slope is an effective solution to reduce low back pain risk caused by prolonged standing. In this study, we examine the effect of shoes on lower back pain caused by prolonged standing for 2 hours on sloping medium. However, prolonged standing has another major risk: lower extremity pain. Many studies have shown that this risk can be affected by shoes type or characteristic. Hence, lower extremity pain risk is the main concern in this research. Two types of shoes observed in this study are Safety Shoes and Slip-On Shoes, as these are the most widely used in the manufacturing industry. Using the Surface Electromyography (S-EMG) method, the difference in Medial Gastrocnemius muscle response was measured against both types of shoes. The study showed that both types of shoes have different muscle activation values and the Safety Shoes showed greater activation. This result proves that, type of shoes may affect the amount of lower extremity pain caused while standing for 2 hours on sloping medium and Safety Shoes poses a greater lower extremity risk. Both Visual Analog Scale (VAS) and Foot Pain Questionnaire methods supported the finding. While the results of VAS method found standing for 2 hours on sloping medium has a greater lower extremity pain than low back pain risk. Foot Pain Questionnaire method indicated that the activity of standing for 2 hours over sloping medium causes a high pain on thumb toe and the back of foot. Based on this study, it can be concluded that it is necessary to design a special shoe for prolonged standing occupation on a sloping medium that can reduce the lower extremity pain risk, besides low back pain risk
Keywords: Biomechanics, ergonomics, lower extremity pain, low back pain, prolonged standing, shoes, surface-electromyography.