The novel coronavirus pandemic has dramatically affected how nursing students are educated. Distance learning has become the norm, and an evaluation of learning achievement is needed. This is a mixed-method study of teaching presence, self-regulated learning, and learning satisfaction in distance learning to evaluate the learning achievement of students in a nursing education program. Ninety-four students for quantitative and seven students for qualitative research were sampled. All the sampled students attend the nursing education program in Seoul and Gyeonggi Province and were enrolled during the first semester of 2020. Quantitative data were analyzed using SPSS/WIN 21.0, and qualitative data were analyzed via content analysis in NVivo 12. Teaching presence and self-regulated learning were identified as the factors affecting learning satisfaction. In a focus group interview, teaching presence increased when the students received feedback and saw the faces of their professors. Self-regulated learning occurred when they had opportunities to practice self-study and leadership and when they formed relationships between professors and colleagues. These methods have also been recognized to increase learning satisfaction. Considering the results of this study, it is necessary to develop teaching methods that enhance the learning satisfaction of students in distance learning nursing education programs.

• distance
• education
• nursing
• personal satisfaction
• students

• COVID-19/epidemiology
• Education, Distance
• Education, Nursing/methods
• Education, Nursing, Baccalaureate/methods
• Humans
• Personal Satisfaction
• Students, Nursing
• Teaching

Technological development of motion and posture analyses is rapidly progressing, especially in rehabilitation settings and sport biomechanics. Consequently, clear discrimination among different measurement systems is required to diversify their use as needed. This review aims to resume the currently used motion and posture analysis systems, clarify and suggest the appropriate approaches suitable for specific cases or contexts. The currently gold standard systems of motion analysis, widely used in clinical settings, present several limitations related to marker placement or long procedure time. Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies, especially outside laboratories. Similarly, new posture analysis techniques are emerging, often driven by the need for fast and non-invasive methods to obtain high-precision results. These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies. The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient. Herein, these devices and their uses are described, providing researchers, clinicians, orthopedics, physical therapists, and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis, therapy, and prevention.

• Motion capture
• Gait analysis
• Inertial measurement unit
• Wearable devices
• Rasterstereography
• Posture

• MEMS sensors
• body sensor network
• gait analysis
• information fusion
• rehabilitation assessment

Parkinson’s disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients’ quality of life, and even death. Existing FOG assessments by doctors are based on a patient’s diaries and experts’ manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom’s characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called “Kin-FOG”. The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient’s home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.

• angular displacement
• freezing of gait (FOG)
• kinect
• parkinson’s disease (PD)
• simple walking (SW)
• standing mode (ST)
• trajectory
• walking with turning (WWT)