Earticle

An emergency monitoring system for the elderly, which uses acceleration data measured with an accelerometer, angular velocity data measured with a gyroscope, and heart rate measured with an electrocardiogram, is proposed. The proposed fall detection algorithm uses multiple parameter combinations in which all parameters, calculated using tri-axial accelerations and bi-axial angular velocities, are above a certain threshold within a time period. Further, we propose an emergency detection algorithm that monitors the movements of the fallen elderly person, after a fall is detected. The results show that the proposed algorithms can distinguish various types of falls from activities of daily living with 100% sensitivity and 98.75% specificity. In addition, when falls are detected, the emergency detection rate is 100%. This suggests that the presented fall and emergency detection method provides an effective automatic fall detection and emergency alarm system. The proposed algorithms are simple enough to be implemented into an embedded system such as 8051-based microcontroller with 128 kbyte ROM.

Among the "five major injuries" in the construction industry-namely, falls from height, electric shock, object strike, mechanical injury, and collapse-fall accidents from height exhibit the highest incidence rate and pose significant dangers. To mitigate the frequency of fall accidents, a fall detection algorithm was developed based on three-axis acceleration sensors. This intelligent algorithm analyzes the acceleration data of the human body during different motion states. It extracts eigenvalues from the acceleration data, processes and analyzes them using the support vector machine method, and performs data classification to determine if a person has undergone a fall. Through experimental testing and validation, this method has demonstrated a high level of reliability in the detection of falling behavior. The accuracy of this algorithm surpasses that of traditional threshold detection methods and decision tree-based algorithms. This enhancement improves its potential for application in the detection of falls from heights in construction settings.

본 논문은 영상처리 기반의 낙상 감지 알고리즘의 설계 및 구현에 관한 내용을 기술한다. 영상처리 기반의 낙상 감지 알고리즘은카메라로 획득한 입력 영상을 그레이 스케일 변환 후 배경차분과 이진화를 통해 객체를 분리하고, 라벨링을 통해 인체를 인식한다. 인식된 인체는 출력 영상으로 확인이 가능하며 낙상을 감지하게 되면 알람이 발생한다. 컴퓨터 시뮬레이션을 통하여 제안한 알고리즘을 실험한 결과 90%의 검출율을 보여주었다. DSP 영상처리 보드에 구현한 시제품 시험을 통하여 기능을 검증함으로서 실용화가능성을 확인하였다.

This paper describes the design and implementation of fall-down detection algorithm based on image processing. The fall-down detection algorithm separates objects by using background subtraction and binarization after grayscale conversion of the input image acquired by the camera, and recognizes the human body by using labeling operation. The recognized human body can be monitored on the display image, and an alarm is generated when fall-down is detected. By using computer simulation, the proposed algorithm has shown a detection rate of 90%. We verify the feasibility of the proposed system by verifying the function by using the prototype test implemented on the DSP image processing board.

The purpose of this paper was designing a fall detection algorithm as a solution so victims who are unable to move after accident could get help. Data were gathered by experimenting using accelerometer to find standby threshold and fall threshold. Data for standby threshold were processed by calculating all the differences and thus use the maximum difference for a threshold. Data for fall threshold were also processed by calculating all the differences and thus use the minimum difference from activities that were ended with a fall. Those thresholds were implemented into prototype application and tested again to find its accuracy. Results were 1 on sensitivity and 0.96 on specificity. It could be concluded that this fall detection algorithm has a 0.98 on its accuracy.

Currently, falling to industrial field workers is causing serious injuries. Therefore, many researchers are actively studying the fall by using acceleration sensor, gyro sensor, pressure sensor and image information.Also, as the spread of smartphones becomes common, techniques for determining the fall by using an acceleration sensor built in a smartphone are being studied. The proposed method has complexity due to fusion of various sensor data and it is still insufficient to develop practical application. Therefore, in this paper, we use acceleration sensor module built in smartphone to collect acceleration data, propose a simple falling algorithm based on accelerometer sensor data after normalization and preprocessing, and implement an Android based app.

This study represents research on falls while riding bicycles. We present an algorithm that detects the occurrence and direction of falls during bicycle riding through the use of a sensor module installed on the helmet. The sensor module is equipped with a tri-axial accelerometer, tri-axial gyroscope, and tri-axial magnetometer to record acceleration and tilt signals. We determine the threshold of the algorithm for fall detection based on experiments of riding over speed bumps and performing warm-up exercise motions prior to riding a bicycle. The algorithm we developed achieved 98% accuracy in fall detection and 98.5% accuracy in detection of fall direction. This study performed simulations indoors for the sake of subjects’ safety; however, we expect to produce the research in an environment more similar to real-life situations. In future, it is also possible that the developed system will be applied to an emergency reporting system for bicycle falls.

구글사에서 출시된 ML Kit API의 Pose detection를 사용한 영상기반 낙상 알고리즘을 제안한다. Pose detection 알고리듬을 사용하여 추출된 신체의 33개의 3차원 특징점을 활용하여 낙상을 인식한다. 추출된 특징점을 분석하여 낙상을 인식하는 알고리듬은 k-NN을 사용한다. 영상의 크기와 영상내의 인체의 크기에 영향을 받지 않도록 정규화과정을 거치며 특징점들의 상대적인 움직임을 분석하여 낙상을 인식한다. 본 실험을 위해 사용한 13개의 테스트 영상중 13개의 영상에서 낙상을 인식하여 100%의 성공률을 보였다.

We propose a fall recognition system using the Pose Detection of Google ML kit using video data. Using the Pose detection algorithm, 33 three-dimensional feature points extracted from the body are used to recognize the fall. The algorithm that recognizes the fall by analyzing the extracted feature points uses k-NN. While passing through the normalization process in order not to be influenced in the size of the human body within the size of image and image, analyzing the relative movement of the feature points and the fall recognizes, thirteen of the thriteen test videos recognized the fall, showing an 100% success rate.

영상 정보를 이용한 자동 낙상 감지 알고리즘을 제안한다. 자동으로 낙상을 감지하기 위한 낙상 특징 파라미터를 추출하기 위해서 영상정보를 광류 방식에 적용하여 움직임 값들을 추출하고 이 움직임 값들에 대한 전체적인 변화의 정도와 기울기, 중심점을 주성분 분석 방법으로 계산한다. 계산된 고유값과 고유 벡터를 사용하여 6가지 낙상 특징 파라미터를 정의한다. 이 낙상특징파라미터가 미리 정해둔 임계값을 초과하는 경우를 낙상으로 판단하는 단순 임계치 방법과 낙상특징파라미터를 은닉 마르코프 모델(Hidden Markov Model; HMM)에 적용시켜 낙상을 판단하는 방법과 단순임계치와 은닉 마르코프 모델을 결합한 낙상 감지 방법을 제안하고 그 결과를 비교 및 분석한다. 단순 임계치와 은닉 마르코프 모델을 결합한 방법은 단순임계치 방법으로 낙상 가능한 행동들을 결정하고 이 결정된 낙상 행동들만을 은닉 마르코프 모델을 적용하여 낙상을 감지한다. 이 방법은 계산량을 줄이면서 감지 정확도를 유지하는 결과를 보인다.

Automatic fall-detection algorithms using video-data are proposed. Six types of fall-feature parameters are defined applying the optical flows extracted from differential images to principal component analysis(PCA). One fall-detection algorithm is the simple threshold method that a fall is detected when a fall-feature parameter is over a threshold, another is to use the HMM, and the other is to combine the simple threshold and HMM. Comparing the performances of three types of fall-detection algorithm, the algorithm combining the simple threshold and HMM requires less computational resources than HMM and exhibits a higher accuracy than the simple threshold method.

독거노인이 증가하면서 낙상 사고 빈도도 높아지고 있다. 낙상은 노인들의 건강을 위협할 뿐만 아니라, 독립적인 생활을 위협할 수 있다. 이 문제를 해결하기 위해서는 독거노인의 위급한 상태를 인식하고 대응할 수 있는 실시간 기술이 필요하다. 따라서 본 논문은 독거노인을 위해 긴급 상황 중 하나인 낙상을 실시간으로 확인할 수 있는 YOLO-KCF를 기반 개선된 낙상 감지 알고리즘을 제안한다. YOLO는 물체의 검출뿐 아니라 서 있는 행동과 쓰러지는 행동 유행을 감지할 수 있다. 따라서 본 논문은 서 있는 행동 유형과 쓰러지는 행동 유형간의 경계 박스의 형태 변화를 이용하여 낙하를 검출할 수 있으며, KCF의 단점을 개선할 수 있다.

As the number of elderly people living alone increases, the frequency of fall accidents is also increasing. Falls are a threat to the health of older adults and can reduce their ability to remain independent. To solve this problem, we need real-time technology to recognize and respond to the critical condition of the elderly living alone. Therefore, this paper proposes a modified fall detection algorithm based on YOLO-KCF that can check one of the emergency situations in real time for the elderly living alone. YOLO can detect not only the detection of objects, but also the behavior of objects, namely stand and fall. Therefore, this paper can detect fall using the ratio of change of boundary box between stand and falling situation, and this algorithm can improve the shortcomings of KCF.

고령화 사회로 인한 노인의 건강과 안전에 대한 관심이 증가함에 따라 낙상 문제는 더욱 중요해졌다. 기존 연구들은 영상에서 인체의 관절위치를 측정하고 이것만을 활용하여 낙상을 감지했지만, 본 논문에서는 방향과 속력 정보를 추가하여 탐지 능력을 향상시켰다. 실험결과 기존 방식에 비해 향상된 성능을 관찰할 수 있었다.

최근 사회에서 자전거 이용 인구는 증가하고 있다. 그에 따라 자전거 사고가 사회 문제로 대두되고 있다. 자전거 특성상 도로 위에서 차량에 비해 위험이 높을 수밖에 없는데, 자전거 사고는 자칫 인명피해로 이어질 수 있다. 자전거 이용자가 뺑소니를 당하거나 인적이 드문 곳에서 낙상 사고를 당했을 때, 이용자가 의식을 잃는다면 큰 위험에 처하게 된다. 따라서 본 논문에서는 3축 가속도 센서 기반으로 낙상 사고를 감지하고, 사고 발생 시 신고를 하는 시스템을 구축하였다. 3축 가속도 센서를 헬멧에 부착하여 자전거 낙상 사고가 발생했을 때 값의 변화를 측정한다. 낙상 사고가 확인되면 사용자의 스마트폰으로 신호를 보내고 응급 구조를 요청한다. 낙상 사고 감지 과정에서 가속도 문턱값을 이용하였다. 상황 가정 실험을 통하여 사고 감지 정확성과 오작동 발생 빈도를 확인하여, 제안된 방식이 효과적으로 낙상 사고를 감지함을 확인하였다

In recent society, the number of people using bicycles is increasing. As a result, bicycle accidents are becoming a social problem. Due to the nature of the bicycle, the risk is inevitably higher than that of the vehicle on the road, which could lead to the loss of life. When a cyclist is hit and run or falls in a deserted area, he or she is in great danger if the user loses consciousness. Thus, in this paper, a system for detecting falls accidents based on three-axis acceleration sensors and reporting in case of accidents was established. Attach a three-axis acceleration sensor to the helmet to measure the change in value in the event of a bicycle fall accident. When a fall accident is confirmed, it sends a signal to a user's smartphone and requests emergency help. Acceleration thresholds were used during fall accident detection. Through situational experiments, the accuracy of accident detection and frequency of malfunction occurrence were verified to ensure that the proposed method effectively detects fall events.