The Journal of the Institute of Internet, Broadcasting and Communication
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v.22
no.5
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pp.37-42
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2022
In this paper, AI methods such as deep learning are applied to provide customized exercise therapy for patients with kidney disease. In order to apply deep learning, a dataset that can determine kidney disease is trained to determine whether it is a kidney disease, and 1RM, which is the user's physical information and muscle strength according to whether it is a disease, can also be calculated through deep learning. The calculated muscle strength of 1RM was converted into resistant exercise for each part through a calculation equation for each part of the body, and was configured to be provided with an aerobic exercise amount tailored to the user's body information. If continuous research is conducted in the manner proposed in this paper, customized exercise therapy can be provided for various diseases.
Background: Muscle undergoes change continuously with aging. Sarcopenia, in which muscle mass decrease with aging, is associated with various diseases, the risk of falling, and the deterioration of quality of life. Obesity and sarcopenia also have a synergy effect on the disease of the older adults. Objects: This study examined the risk factors for sarcopenia, sarcopenic obesity, and sarcopenia without obesity and developed prediction models. Methods: This machine-learning study used the 2008-2011 Korea National Health and Nutrition Examination Surveys in the analysis. After data curation, 5,563 older participants were selected, of whom 1,169 had sarcopenia, 538 had sarcopenic obesity, and 631 had sarcopenia without obesity; the remaining 4,394 were normal. Decision tree and random forest models were used to identify risk factors. Results: The risk factors for sarcopenia chosen by both methods were body mass index (BMI) and duration of moderate physical activity; those for sarcopenic obesity were sex, BMI, and duration of moderate physical activity; and those for sarcopenia without obesity were BMI and sex. The areas under the receiver operating characteristic curves of all prediction models exceeded 0.75. BMI could predict sarcopenia-related disease. Conclusion: Risk factors for sarcopenia-related diseases should be identified and programs for sarcopenia-related disease prevention should be developed. Data-mining research using population data should be conducted to enhance the effectiveness of early treatment for people with sarcopenia-related diseases through predictive models.
Purpose: New therapeutic approaches have emerged to improve gait ability in patients with brain damage, such as action observation learning (AOT), auditory cueing, motor imagery etc. We attempted to investigate the effects of AOT with auditory cueing (AOTAC) on gait function in patients with stroke. Methods: The eighteen stroke patients with a unilateral hemiparesis were randomly divided into three groups; the AOTAC, AOT, and control groups. The AOTAC group (n=8) received training via observing a video that showed normal gait with sound of footsteps as an auditory cue; the AOT group (n=6) receive action observation without auditory stimulation; the control group (n=5) observed the landscape video image. Intervention time of three groups was 30 minutes per day, five times a week, for four weeks. Gait parameters, such as cadence, velocity, stride length, stance phase, and swing phase were collected in all patients before and after each training session. Results: Significant differences were observed among the three groups with respect to the parameters, such as cadence, velocity, stride length, and stance/swing phase. Post-hoc analysis indicated that the AOTAC group had a greater significant change in all of parameters, compared with the AOT and control groups. Conclusion: Our findings suggest that AOTAC may be an effective therapeutic approach to improve gait symmetry and function in patients with stroke. We believe that this effect is attributable to the change of cortical excitability on motor related to cortical areas.
Purpose: In this study, based on the error augmentation, we performed walking training with increased rhythmic auditory stimulation speed on the affected side (IRAS) and walking training with decreased rhythmic auditory stimulation speed on the unaffected side (DRAS). The purpose of this study was to verify whether motor learning was effective in improving balance ability. Methods: Twenty-eight subjects with chronic stroke were recruited from a rehabilitation center. The subjects were divided into three groups: an IRAS group (10 subjects), a DRAS group (9 subjects), and control group (9 subjects). They received 30minutes of neuro-developmental therapy and walking training for 30minutes, five times a week for three weeks. Static and functional balance ability were measured before and after the training period. Static balance was measured by balancia software. Functional balance was measured by the timed up and go test (TUG) and the berg balance scale (BBS). Results: After the training periods, the IRAS group showed a significant improvement in TUG, BBS, area 95% COP, and weight distribution on the affected side when compared to both the DRAS group and control group (p<0.05). Conclusion: Based on the results of this study, it is possible to consider error augmentation methods of motor learning if rhythmic auditory stimulation is applied to stroke patients in clinical practice. If the affected side is shorter than the unaffected side, the affected side should be adjusted to the increased rhythmic auditory stimulation speed, which is considered to be an effective intervention to improve balance ability.
Purpose: The purpose of this study was to investigate the availability of software for rehabilitation with the Kinect sensor by presenting an efficient algorithm based on machine learning when classifying the motion data of the PNF pattern if the subjects were wearing a patient gown. Methods: The motion data of the PNF pattern for upper extremities were collected by Kinect sensor. The data were obtained from 8 normal university students without the limitation of upper extremities. The subjects, wearing a T-shirt, performed the PNF patterns, D1 and D2 flexion, extensions, 30 times; the same protocol was repeated while wearing a patient gown to compare the classification performance of algorithms. For comparison of performance, we chose four algorithms, Naive Bayes Classifier, C4.5, Multilayer Perceptron, and Hidden Markov Model. The motion data for wearing a T-shirt were used for the training set, and 10 fold cross-validation test was performed. The motion data for wearing a gown were used for the test set. Results: The results showed that all of the algorithms performed well with 10 fold cross-validation test. However, when classifying the data with a hospital gown, Hidden Markov model (HMM) was the best algorithm for classifying the motion of PNF. Conclusion: We showed that HMM is the most efficient algorithm that could handle the sequence data related to time. Thus, we suggested that the algorithm which considered the sequence of motion, such as HMM, would be selected when developing software for rehabilitation which required determining the correctness of the motion.
The purpose of this study was to apply treadmill training through motor learning to cerebral palsy children and examine its effects on their Gross Motor Functions. The subjects of this study were 13 spastic diplegia children who had difficulty in independent gait, and GMFCS level III, IV. We performed treadmill gait training using the principle of weight bearing, based on 4times a week for 30 minutes before and after each session physical therapy we gave weight bearing treadmill training 5 to 10 minutes, during 7 weeks(April 9, 2012~May 26, 2012) fittingly for the children's gait characteristics. In order to test how the weight bearing treadmill training affects spastic diplegia children's gross motor functions, we measured body mobility with Gross Motor Function Measure (GMFM). These data were collected before and after the experiment and analyzed through comparison. Data collected from the 13 spastic diplegia children the results were as follows. For evaluating with regard to change in body mobility, significant difference was observed between before and after the experiment in measured gross motor functions, which were crawling, kneeling, standing, walking, jumping and running(p<0.05). According to the results of this study, when gait training through motor learning was applied to spastic cerebral palsy children, it made significant changes in their body mobility. Accordingly, for the effective application of gait training through motor learning to cerebral palsy children, it is considered necessary to make research from different angle on how such training affects children's mobility, activity of muscles in the lower limbs, and gait characteristics.
Purpose: This study examined the influence of visuomotor congruency on learning a relative phase relationship between a single joint movement and an external signal. Methods: Participants (N=5) were required to rhythmically coordinate elbow flexion-extension movements with a continuous sinusoidal wave (0.375 Hz) at a $90^{\circ}$ relative phase relationship. The congruent group was provided online feedback in which the elbow angle decreased (corresponding to elbow flexion) as the angle trajectory was movingup, and vice versa. The incongruent group was provided online feedback in which the elbow angle decreased as the angle trajectory was moving down, and vice versa. There were two practice sessions (day 1 and 2) and each session consisted of 6 trials per block (5 blocks per session). Retention tests were performed 24 hours after session 2, and only the external sinusoidal wave was provided. Repeated ANOVAs were used for statistical analysis. Results: During practice, the congruent group was significantly less variable than the incongruent group. Phase variability in the incongruent group did not significantly change across blocks, while variability decreased significantly in the congruent group. In retention, the congruent group produced the required $90^{\circ}$ relative phase pattern with significantly less phase variability than the incongruent group. Conclusions: Congruent visual feedback facilitates learning. Moreover, the deprivation of online feedback does not affect the congruent group but does affect the incongruent group in retention.
Purpose: We investigated the changes in the stop-signal reaction time (SSRT) and the no-signal reaction time (NSRT) following motor sequential learning in the stop-signal task (SST). This study also determined which of the reduction0s of spatial processing time was better between blocked- and random-SST. Methods: Thirty right-handed healthy subjects without a history of neurological dysfunction were recruited. In all subjects, both the SSRT and the NSRT were measured for the SST. Tasks were classified into two categories based on the stop-signal patterns, the blocked-SST practice group and random-SST practice group. All subjects gave written informed consent. Results: In the blocked-SST group, both the SSRT and the NSRT was significantly decreased (p<0.05) but not significantly changed in the random-SST group. In the SSRT and the NSRT, the blocked-SST group was faster than the random-SST group (p<0.05). In the post-test SST after practice of each group, the SSRT was significantly decreased in the random-SST group (p<0.05), but the NSRT showed no significant changes in either group. Conclusion: These findings demonstrate that random-SST practice resulted in a decrease in internal processing times needed for a rapid stop to visual signals, indicating motor skill learning is acquired through improved response selection and inhibition.
Purpose: This study examined whether 1) the motor inhibition response as cognitive-behavioral component is learning though a stop signal task using stop-signal paradigm, and 2) whether there is a difference in the learning degree according to imagery training and actual practice training. Methods: Twenty young adults (males: 9, females: 11) volunteered to participate in this study, and were divided randomly into motor imagery training (IT, n=10) and practice training (PT, n=10) groups. The PT group performed an actual practice stop-signal task, while the IT group performed imagery training, which showed a stop-signal task on a monitor of a personal computer. The non-signal reaction time and stop-signal reaction time of both groups were assessed during the stop-signal task. Results: In the non-signal reaction time, there were no significant intra-group and inter-group differences between pre- and post-intervention in both groups (p>0.05). The stop-signal reaction time showed a significant difference in the PT group in the intra-group analysis (p<0.05). On the other hand, there was no significant intra-group difference in the IT group and inter-group difference between pre- and post-intervention (p>0.05). Conclusion: These results showed that the motor inhibition response could be learned through a stop-signal task. Moreover, these findings suggest that actual practice is a more effective method for learning the motor inhibition response.
Purpose: The purpose of this study was to investigate occupational therapy at M college in Changwon and the impact of smart phone use on the school life of college students and to help them find ways to further develop in the future. Methods; Data collection was conducted using questionnaires, and the questionnaires consisted of 152 total questions with 15 interpersonal questions, 23 problem solving skills, 43 self-efficacy, 16 class participation scale, and 55 self-directed learning scale. It was conducted to first and second graders of M college and conducted a survey through the corresponding academic year from March 26, 2019 to March 29, 2019 to retrieve 120 questionnaires and use them for analysis. The collected data were analyzed using SPSS. Statistic 20.0. Results: Studies show that "school life satisfaction" is usually the highest at 53 percent. The "smartphone user motivation" was the highest with 50.8 percent, while the "most frequently used feature on smartphones" was the highest with 57.5 percent on SNS. Satisfaction after using a smartphone was the highest with 49.2 percent, while 41.7 percent said it would be easier to acquire and utilize information in the areas of satisfaction. Conclusion: Smartphone addiction, interpersonal relationships, problem-solving skills, self-efficacy, participation in classes, and self-control learning items are not only affected by one part, but also by the other.
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