Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling ...time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.
Bovine tuberculosis in Taiwan, 2008–2019 Chan, Tai‐Hua; Huang, Chun‐Sheng; Tu, Chien ...
Transboundary and emerging diseases,
July 2022, 2022-07-00, 20220701, Letnik:
69, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Bovine tuberculosis (bTB) is a zoonosis caused by Mycobacterium bovis. The impact of bTB on global TB control has been underestimated. We adopted the One Health approach to human bTB surveillance in ...Taiwan. Of 20,972 human TB cases, 202 (1.0%) were bTB, 78.2% were in males, 85.1% were new cases, 83.2% were pulmonary TB, and most were in Central (52.5%) and Southern (24.8%) Taiwan. Only 18.8% of bTB patients had known animal contact. Of the 202 human M. bovis strains, 100% were resistant to pyrazinamide (PZA), 30.2% were concurrently resistant to isoniazid (INH) and 2.0% were multidrug resistant, defined as being resistant to at least INH and rifampin. Whereas, of the 22 animal M. bovis strains, 100% and 22.7% were resistant to PZA and INH, respectively. Seven spoligotypes and 25 mycobacterial interspersed repetitive unit genotypes were identified. The predominant genotype, SB0265, was also prevalent in livestock. Notably, six animal‐specific M. bovis genotypes were identified. bTB differential diagnosis and drug resistance detection are crucial for TB control. Comprehensive surveillance and human–animal interface investigations are needed.
The needs for light-weight and soft smart clothing in homecare have been rising since the past decade. Many smart textile sensors have been developed and applied to automatic physiological and ...user-centered environmental status recognition. In the present study, we propose wearable multi-sensor smart clothing for homecare monitoring based on an economic fabric electrode with high elasticity and low resistance. The wearable smart clothing integrated with heterogeneous sensors is capable to measure multiple human biosignals (ECG and respiration), acceleration, and gyro information. Five independent respiratory signals (electric impedance plethysmography, respiratory induced frequency variation, respiratory induced amplitude variation, respiratory induced intensity variation, and respiratory induced movement variation) are obtained. The smart clothing can provide accurate respiratory rate estimation by using three different techniques (Naïve Bayes inference, static Kalman filter, and dynamic Kalman filter). During the static sitting experiments, respiratory induced frequency variation has the best performance; whereas during the running experiments, respiratory induced amplitude variation has the best performance. The Naïve Bayes inference and dynamic Kalman filter have shown good results. The novel smart clothing is soft, elastic, and washable and it is suitable for long-term monitoring in homecare medical service and healthcare industry.
People nowadays often ignore the importance of proper hydration. Water is indispensable to the human body’s function, including maintaining normal temperature, getting rid of wastes and preventing ...kidney damage. Once the fluid intake is lower than the consumption, it is difficult to metabolize waste. Furthermore, insufficient fluid intake can also cause headaches, dizziness and fatigue. Fluid intake monitoring plays an important role in preventing dehydration. In this study, we propose a multimodal approach to drinking activity identification to improve fluid intake monitoring. The movement signals of the wrist and container, as well as acoustic signals of swallowing, are acquired. After pre-processing and feature extraction, typical machine learning algorithms are used to determine whether each sliding window is a drinking activity. Next, the recognition performance of the single-modal and multimodal methods is compared through the event-based and sample-based evaluation. In sample-based evaluation, the proposed multi-sensor fusion approach performs better on support vector machine and extreme gradient boosting and achieves 83.7% and 83.9% F1-score, respectively. Similarly, the proposed method in the event-based evaluation achieves the best F1-score of 96.5% on the support vector machine. The results demonstrate that the multimodal approach performs better than the single-modal in drinking activity identification.
Clinical characteristics and outcomes of intracranial hemorrhage (ICH) among adult patients with various hematological malignancies are limited.
A total of 2,574 adult patients diagnosed with ...hematological malignancies admitted to a single university hospital were enrolled into this study between 2001 and 2010. The clinical characteristics, image reports and outcomes were retrospectively analyzed.
A total of 72 patients (48 men and 24 women) with a median age of 56 (range 18 to 86) had an ICH. The overall ICH incidence was 2.8% among adult patients with hematological malignancies. The incidence of ICH was higher in acute myeloid leukemia (AML) patients than in patients with other hematological malignancies (6.3% vs 1.1%, P = 0.001). ICH was more common among patients with central nervous system (CNS) involvement of lymphoma than among patients with CNS involved acute leukemia (P <0.001). Sites of ICH occurrence included the cerebral cortex (60 patients, 83%), basal ganglia (13 patients, 18%), cerebellum (10 patients, 14%), and brainstem (5 patients, 7%). A total of 33 patients (46%) had multifocal hemorrhages. In all, 56 patients (77%) had intraparenchymal hemorrhage, 22 patients (31%) had subdural hemorrhage, 15 patients (21%) had subarachnoid hemorrhage (SAH), and 3 patients (4%) had epidural hemorrhage. A total of 22 patients had 2 or more types of ICH. In all, 46 (64%) patients died of ICH within 30 days of diagnosis, irrespective of the type of hematological malignancy. Multivariate analysis revealed three independent prognostic factors: prolonged prothrombin time (P = 0.008), SAH (P = 0.021), and multifocal cerebral hemorrhage (P = 0.026).
The incidence of ICH in patients with AML is higher than patients with other hematological malignancies. But in those with intracranial malignant disease, patients with CNS involved lymphoma were more prone to ICH than patients with CNS involved acute leukemia. Mortality was similar regardless of the type of hematological malignancy. Neuroimaging studies of the location and type of ICH could assist with prognosis prediction for patients with hematological malignancies.
A positive bias temperature instability (PBTI) recovery transient technique is presented to investigate trap properties in HfSiON as high-k gate dielectric in nMOSFETs. Both large- and small-area ...nMOSFETs are characterized. In a large-area device, the post-PBTI drain current exhibits a recovery transient and follows logarithmic time dependence. In a small-area device, individual trapped electron emission from HfSiON gate dielectric, which is manifested by a staircase-like drain current evolution with time, is observed during recovery. By measuring the temperature and gate voltage dependence of trapped electron emission times, the physical mechanism for PBTI recovery is developed. An analytical model based on thermally assisted tunneling can successfully reproduce measured transient characteristics. In addition, HfSiON trap properties, such as trap density and activation energy, are characterized by this method.
Drain current degradation in HfSiON gate dielectric nMOSFETs by positive gate bias and temperature stress is investigated by using a fast transient measurement technique. The degradation exhibits two ...stages, featuring a different degradation rate and stress temperature dependence. The first-stage degradation is attributed to the charging of preexisting high-k dielectric traps and has a log(t) dependence on stress time, whereas the second-stage degradation is mainly caused by new high-k trap creation. The high-k trap growth rate is characterized by two techniques, namely 1) a recovery transient technique and 2) a charge-pumping technique. Finally, the effect of processing on high-k trap growth is evaluated.
In order to improve healthcare services and support clinical professionals, it is important to develop the unobstructive and automatic ADLs monitoring system for healthcare applications. Currently, ...various works have been developed for the monitoring of daily activities, such as ambulation, kitchen task, food and fluid intake, dressing, and medication intake while only few works paid attention to the housekeeping task. Housekeeping activity is a complex task, generally important for the several clinical assessment tools. In this work, we design and develop a wearable sensor-based activity recognition system recognize housekeeping tasks and classify the activity level. The proposed system achieves 90.67% accuracy for housekeeping tasks recognition, and 94.35% accuracy for activity level classification, respectively. The results of the experiment demonstrate that the system is reliable and fulfills the requirements of the unobstructive, objective, and long-term monitoring system.
Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even ...leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD.
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