Gesture-recognition is an important component for many intelligent human-computer interaction applications. For example, a realtime sign-language recognition system would detect and interpret hand gestures. Many vision-based sign-language recognition methods have been proposed over the years with mix results of usability. Some system are limited to recognize only a few gestures, while others require the use of 3D camera to provides depth information to improve recognition accuracy. In this paper, a Kinect-based Taiwanese sign-language recognition system is proposed. Three main features are extracted from the signing gestures, namely hand positions, hand signing direction, and hand shapes. The hand positions are readily available through the input sensor. The signing direction is determined using HMM on trajectory of the hand movement, and a SVM is trained and used to recognize the hand shapes. Experimental results show that the proposed system achieved an 85.14 % recognition rate.
Aims
To evaluate the effectiveness of a mobile health (mHealth) application, based on self‐regulation theory, on patients’ knowledge of wound care, skills in changing dressings and anxiety.
Design
A prospective randomized controlled trial.
Methods
Seventy patients (or family members) at a 1,500‐bed university hospital in Taiwan were randomized into an experimental (N = 35) or control group (N = 35) from March to December 2016. The experimental group used a mHealth application for wound care; the control group received verbal instructions and a booklet. Instruments to collect data were a wound care knowledge scale, wound care skills scale, State‐Trait Anxiety Inventory, and a digital heart variability device. Data were collected at baseline, after three additional demonstrations and before discharge. The generalized estimating equation was used for statistical analysis.
Results
The experimental group showed significantly higher levels of wound care knowledge, improved wound care skills, lower levels of state anxiety, and lower heart rate variability than the control group after baseline data collection.
Conclusions
Results support hat a mHealth application may be effective in health education. Clinicians can use the results to promote patients’ wound care knowledge, enhance their wound care skills, and reduce anxiety related to dressing changes.
Impact
Lack of wound care knowledge and skills can affect the willingness and ability to perform effective wound dressing changes, producing anxiety and having an impact on a patient's self‐care after hospital discharge. mHealth applications (apps) have the potential to deliver health information in targeted and tailored ways that strengthen the self‐management of diseases. mHealth app can increase wound care knowledge, improve care skills, and reduce anxiety related to wound care. mHealth app effectively supports self‐monitoring of the wound healing process, self‐judgement of the wound condition, and self‐reaction of wound care accuracy. mHealth app provides step‐by‐step visual tutorials on wound care that allow patients and family caregivers to take pictures of the wounds and monitor the wound healing process. mHealth app for wound care knowledge is an effective and individualized method for learning.
Clinical Trial: This study was registered by U.S. National Library of Medicine, ClinicalTrials.gov (ID: NCT03683303).
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