The increase in stroke incidence with the aging of the Korean population will rapidly impose an economic burden on society. Timely treatment can improve stroke prognosis. Awareness of stroke warning signs and appropriate actions in the event of a stroke improve outcomes. Medical service use and health behavior data are easier to collect than medical imaging data. Here, we used a deep neural network to detect stroke using medical service use and health behavior data; we identified 15,099 patients with stroke. Principal component analysis (PCA) featuring quantile scaling was used to extract relevant background features from medical records; we used these to predict stroke. We compared our method (a scaled PCA/deep neural network [DNN] approach) to five other machine-learning methods. The area under the curve (AUC) value of our method was 83.48%; hence; it can be used by both patients and doctors to prescreen for possible stroke.

A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients’ medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients’ medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient’s statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients’ simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve (AUC) and minimized the effort to generate features. Hence, this methos can be a promising tool for patients and doctors to prescreen for possible osteoarthritis to reduce health costs and patients’ time in hospitals.

Mental health-related disorders are common in elderly populations. Among the various mental health disorders, one most significant threat is dementia, and prediction of dementia has become an important issue related to well-being in old age, because the disease progression of dementia can be slowed by early diagnosis and disease control. In this paper, we propose an unobtrusive dementia-prediction system for monitoring physical activities of elderly persons either living alone or as a couple in different house structures, achieved through passive infrared (PIR) motion sensors combined with data processing. The proposed feature extraction algorithm extracts feature values related to physical activities from simple passive infrared sensors located in each room space. We then apply a variety of common popular classification models, including Deep Neural Networks (DNNs), to predict the risk of dementia in a sensor-enabled home. We implemented and validated algorithms on data collected for over a month from 18 participants who were engaged with a variety of living conditions. The proposed system was effective in predicting dementia risk, with up to an 0.99 area under the curve (AUC) using DNN with principal component analysis (PCA) and a quantile transformer scaler. In terms of the result based on leave-one-subject-out (LOSO) analysis, an accuracy of 63.38% was achieved using DNN with PCA and a standard scaler. The proposed methodology is non-invasive and cost-effective, and can be used for a variety of long-term monitoring and early symptom detection systems, helping caregivers provide optimal interventions to elderly individuals at risk for dementia.

[Purpose] The purpose of the present study was to investigate the effect of TENS applied to the skin overlying the bellies of the gastrocnemius muscles of the lower limbs on balance and plantar pressure of healthy adults. [Subjects and Methods] Twenty-eight healthy college students were the subjects of this study. Adhesive transcutaneous electrical nerve stimulation (TENS) electrodes were attached to the medial and lateral belly of the gastrocnemius muscle. Before and after the application of the TENS, subjects' balance ability and maximum plantar pressure were measured and compared. [Results] The scores improved in the balance tests, including the fall risk test and the stability limit test, after the application of TENS, and a statistically significant difference was noted in the stability limit test. The maximum plantar pressure after the application of TENS decreased except beneath the big toe, and statistically significant difference was found under the forefoot. [Conclusion] The results of present study suggest that TENS applied to the skin overlying gastrocnemius muscles is useful strategy that improves the balance ability of healthy adults.

Gait is a significant factor that affects human health, and monitoring a person’s gait with sensing devices during daily life can detect abnormal gait events that affect numerous physical health problems. In particular, flat feet can cause changes in alignment conditions of the foot, ankle, leg, pelvis and spine. The primary problem with previous studies of wearable devices for measuring gait have focused on quantitatively monitoring the degree of gait rather than the limited gait ability. The existing method of feeding back the degree of gait or activity does not consider the severity of the subject and is insufficient for qualitative evaluation or training of gait. The significance of this study is development of convenient detecting and long-term tracking tools that can be used by both patients and clinicians for prescreening flat feet and monitoring the progress of flat feet treatment. For wearable devices for flatfoot detection to be most effective, detection systems and algorithms must be accurate, robust, reliable and computationally-efficient. In this paper, we developed an integrated smart wearable gait-monitoring device comprised of three sensors: front force, rear force, and an ankle flex sensor. We propose a new flat feet detection methodology based on a dynamic sensing window and a deep neural network with scaled principal component analysis (PCA). We tested 24 subjects, including both those with healthy gait and flat-feet-affected gait. Our study shows that the proposed sensing devices could be worn comfortably. The proposed deep neural network (DNN) model outperformed the other five classifier algorithms considered, and the area under the curve (AUC) value of the method was 87.1%. This wearable device can thus be easily and simply used both by patients and doctors to monitor the progress of flat feet and prescreen for possible gait problems in daily life.

[Purpose] The primary objective of this study was to assess the effects of a skilled reaching task on cognition, as indexed by the pattern of GAP-43 expression in the hippocampus, following intracerebral hemorrhage (ICH) in rats (when the hippocampus plays a critical role in spatial memory and learning). [Subjects and Methods] The model of ICH used in the present study involved intrastriatal injection of collagenase. Sixty male Sprague-Dawley rats (aged 12 weeks) were randomly assigned to either a control (n = 30; CON) or skilled reaching training group (n = 30; SRT). The SRT group were trained 5 days per week for 4 weeks following ICH. Animals were sacrificed 1, 2, or 4 weeks after ICH. Western blot analysis was used to evaluate GAP-43 expression. [Results] GAP-43 expression was increased in the SRT group, in accordance with greater elapsed time, but decreased in the CON group. At 1 week post injury, there were no significant differences between the CON and SRT groups. However, there were significant differences at both 2 and 4 weeks. [Conclusion] The present findings suggest that increased GAP-43 expression in the hippocampus following skilled reaching training may result in enhanced cognition and neural plasticity following ICH.