Progressive global aging has increased the number of elderly individuals living alone. The consequent rise in fall accidents has worsened physical injuries, reduced the quality of life, and increased medical expenses. Existing wearable fall-detection devices may cause discomfort, and camera-based systems raise privacy concerns. Here, we propose a non-contact fall-detection system that integrates 4D imaging radar sensors with artificial intelligence (AI) technology to detect falls through real-time monitoring and visualization using a web-based dashboard and Unity engine-based avatar, along with immediate alerts. The system eliminates the need for uncomfortable wearable devices and mitigates the privacy issues associated with cameras. The radar sensors generate Point Cloud data (the spatial coordinates, velocity, Doppler power, and time), which allow analysis of the body position and movement. A CNN model classifies postures into standing, sitting, and lying, while changes in the speed and position distinguish falling actions from lying-down actions. The Point Cloud data were normalized and organized using zero padding and k-means clustering to improve the learning efficiency. The model achieved 98.66% accuracy in posture classification and 95% in fall detection. This study demonstrates the effectiveness of the proposed fall detection approach and suggests future directions in multi-sensor integration for indoor applications.

The present study proposes a music recommendation service in a mobile environment using the DASS-21 questionnaire to distinguish and measure certain psychological state instability symptoms—viz. anxiety, depression, and stress—that anyone can experience regardless of job or age. In general, the outcome of the DASS-21 from almost every participant did not reveal any single psychological state out of the abovementioned three states. Therefore, the weighted scores were calculated for each scale and fuzzy clustering was used to cluster users into groups with similar states. For the initial dataset’s generation, we used the DASS inventory collected from the Open-Source Psychometrics Project conducted from 2017 to 2019 on approximately 39,000 respondents, and the results of the survey showed that the average scores for each scale were 23.6 points for depression, 17.4 for anxiety, and 23.3 for stress. Based on the datasets collected from fuzzy clustering, the individuals were classified into three groups: Group 1 was recommended with music for “high” depression, “high” anxiety, and “low” stress; Group 2 was recommended with music for “normal” depression, “low” anxiety, and “normal” stress; and Group 3 was recommended with music for “high” depression, “high” anxiety, and “high” stress. Especially, the largest numbers of recommended music in the three groups were for Group 1 with “High” depressive (4.64), Group 2 for “Low” anxiety (4.54), and Group 3 for “High” anxiety (4.76). In addition, to compare the results of fuzzy clustering with other data, the silhouette coefficient of the samples extracted with the same severity ratio and those generated by simple random sampling were 0.641 and 0.586, respectively, which were greater than 0. The proposed service can recommend not only the music of users with similar trends at all psychological states, but also the music of users with similar psychological states in part.

A non-contact fall detection system, which integrates 4D imaging radar sensors with arti-ficial intelligence (AI) technology, is proposed to monitor fall accidents among the elderly. Existing wearable devices may cause discomfort during use, and camera-based systems raise privacy concerns. The solution developed in this study addresses these issues by adopting 4D radar sensors. The radar sensors generate Point Cloud data to enable the system to analyze the positions and postures of the body. Using a CNN model, these pos-tures are classified into standing, sitting, and lying, while criteria based on changes in the speed and position distinguish between falls and slow-lying movements. The Point Cloud data were normalized and organized using zero padding and k-means clustering to en-hance the learning efficiency. The proposed model achieved 98.66% accuracy in posture classification and 95% in fall detection. The monitoring system provides real-time visual representations through a web-based dashboard and Unity-based 3D avatars, along with immediate alerts in case of a fall. In conclusion, this study demonstrates the effectiveness of real-time fall detection technology and highlights the need for further research on mul-ti-sensor integration and application in various indoor environments.

본 연구에서는 낙상을 효과적으로 검출하기 위해 4D 이미징 레이더 센서와 인공지능(AI) 기반의 비접촉 낙상 감지 시스템을 제안하였다. 이 시스템은 레이더 센서를 통해 추출한 Point Cloud 데이터를 가공하여 신체의 위치와 자세를 분석하며, CNN 모델을 활용한 서기, 앉기, 눕기 등의 자세를 분류한 후 낙상과 천천히 눕는 동작을 구별하기 위해 속도 및 위치 변화를 비교하는 판별 기준을 수립하였다. 이에 대한 결과로는 낙상 감지정확도 90%, 자세 분류 정확도 98.66%를 달성하였다. 더불어, 웹 기반 인터페이스와 Unity 엔진을 활용한 아바타 시뮬레이션을 통합하여 사용자 친화적인 실시간 모니터링 환경을 제공하고자 하였다. 향후 연구에서는 다양한 환경에서 추가적인 검증을 수행하고, 낙상의 단계를 더욱 세분화하는 한편, 레이더 센서 외에 추가적인 센서를 융합하여 통합 헬스케어 플랫폼으로 발전시킬 필요가 있다.

본 논문에서는 IMU(Inertial Measurement Unit)을 사용하여 한글 문자의 개인별 필기 패턴을 분석하고자 하였다. 한글의 자음과 모음을 6개의 기본 형태소 분류하였으며, 이에 대한 기준(Reference) 파형을 측정하고 분석하였다. 또한, 스포팅(Spotting) 구간에서의 3가지 성분(가속도, 자이로스코프, 각도)에 대한 파형 변화를 비교하여 실험자의 필기 습관에 대한 특징을 분석하고자 하였다. 이에 대한 실험 결과로는 가속도 성분이 기준 파형과 유사하게 나타나고 있지만, 자이로스코프와 각도 성분에서는 실험 대상자마다 서로 다른 파형을 보여주었다. 이러한 결과는 실험 대상자마다 가지고 있는 고유의 필기 습관이나 자세 때문에 나타나는 것으로 볼 수 있다.