• 센서학회지
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• 제33권1호
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• pp.18-23
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• 2024

Humanoid robots, designed to interact in human environments, require stable mobility to ensure safety. When a humanoid robot falls, it causes damage, breakdown, and potential harm to the robot. Therefore, fall detection is critical to preventing the robot from falling. Prevention of falling of a humanoid robot requires an operator controlling a crane. For efficient and safe walking control experiments, a system that can replace a crane operator is needed. To replace such a crane operator, it is essential to detect the falling conditions of humanoid robots. In this study, we propose falling detection methods using Convolution Neural Network (CNN) model. The image data of a humanoid robot are collected from various angles and environments. A large amount of data is collected by dividing video data into frames per second, and data augmentation techniques are used. The effectiveness of the proposed CNN model is verified by the experiments with the humanoid robot MAX-E1.

• 센서학회지
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• 제22권1호
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• pp.54-64
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• 2013

In this study, we developed a falling recognition system to transmit SMS data through CDMA communication using a three axises acceleration sensor and a two axises gyro sensor. 5 healthy men were selected into a control group, and the fall recognition system using the three axises acceleration sensor and the two axises gyro sensor was devised to conduct an experiment. The system was attached to the upper of their sternum. According to the experiment protocol, the experiment was carried out 3 times repeatedly divided into 3 specific protocols: falling during gait, falling in stopped state, and falling in everyday life. Data obtained in the falling recognition system and LabVIEW 8.5 were used to decide if falling corresponds to that regulated in an analysis program applying an algorithm proposed in this study. In addition, results from falling recognition were transmitted to designated cellular phone in a SMS (Shot Message Service) form. These research results show that an erroneous detection rate of falling reached 19% in applying an acceleration signal only; 6% in applying an angular velocity; and 2% in applying a proposed algorithm. Such finding suggests that an erroneous detection rate of falling is improved when the proposed algorithm is applied incorporated with acceleration and angular velocity. In this study therefore, we proposed that a falling recognition system implemented in this study can make a contribution to the recognition of falling of the aged or the disabled.

• 한국산업정보학회논문지
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• 제21권2호
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• pp.31-37
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• 2016

낙상은 응급상황이 발생한 노인에게는 적절한 시간이 응급처치가 요구되는 주요한 상태이다. 응급상황의 경우, 낙상의 발생과 낙상 방향은 초기 상태의 응급처치를 위한 중요한 정보로 사용될 수 있다. 본 논문에서는 낙상의 발생과 방향을 정확히 판단하는 시스템을 구현하였다. 낙상과 방향을 감지하기 위하여 하나의 3축 자이로도센서(MPU-6050)를 사용하였다. 제안된 낙상 방향 알고리듬은 X와 Y축 가속도값을 사용하여 낙상여부와 앞, 뒤 좌,우 및 중간방향을 포함한 8개 낙상방향을 감지하였다. 제안된 시스템은 선택적인 가속도 임계값을 사용하여 97% 이상의 낙상과 낙상방향을 성공적으로 감지함을 보였다.

• 한국재난정보학회 논문집
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• 제19권3호
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• pp.498-509
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• 2023

연구목적: 본 논문은 CCTV 영상을 활용한 딥러닝 객체 인식 기술을 적용해 지하공동구 내 쓰러진 관리인력의 검출 방법을 제시하고, 제안 방법의 관리인력 모니터링 적용성을 평가한다. 연구방법: 사람 검출 목적으로 사전 훈련된 YOLOv5와 OpenPose 모델의 추론 결과로부터 쓰러짐을 판별할 수 있는 규칙을 제안하고, 각 모델의 결과를 통합해 지하공동구 내 작업자 쓰러짐 검출에 적용하였다. 연구결과: 제안된 모델로 작업인력의 감지 및 쓰러짐을 판단할 수 있었으나, CCTV와 작업자 간격 및 작업자가 쓰러진 방향에 의존해 검출성능이 영향을 받았다. 또한 지하공동구 작업자에 대해 YOLOv5 기반 쓰러짐 판별 규칙 적용 모델이 거리 및 쓰러짐 방향 의존성이 낮아 OpenPose 기반 모델에 비해 우수한 성능을 보였다. 그 결과 통합된 이중 딥러닝 모델의 쓰러짐 검출 결과는 YOLOv5 결과에 종속되었다. 결론: 제안 모델을 통해 지하공동구 작업자의 이상상황 검출이 가능함을 보였으나, 개별 딥러닝 모델별 사람 감지 성능 차이로 인해 YOLOv5 기반 모델 대비 통합 모델의 쓰러짐 검출 성능 개선은 미미하였다.

• 한국IT서비스학회지
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• 제12권4호
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• pp.435-447
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• 2013

If we use a smartphone to analyze and detect falling, it is a huge advantage that the person with a sensor attached to one's body is free from awareness of difference and limitation of space, unlike attaching sensors on certain fixed areas. In this paper, we suggested effective posture analysis of smartphone users, and fall detecting system. Suggested algorithm enables to detect falling accurately by using the fact that instantaneous change of acceleration sensor is different according to user's posture. Since mobile applications working on smart phones are low in compatibility according to mobile platforms, it is a constraint that new development is needed which is suitable for sensor equipment's characteristics. In this paper, we suggested posture analysis algorithm using smartphones to solve the problems related to user's inconvenience and limitation of development according to sensor equipment's characteristics. Also, we developed fall detection system with the suggested algorithm, using hybrid mobile application which is not limited to platform.

• 제어로봇시스템학회논문지
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• 제16권9호
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• pp.827-832
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• 2010

Many researchers have tried to detect the falling and to reduce the injury associated with falling. Normally the method of detection of a loss of balance is more efficient than that of a compensatory motion in order to predict the falling. The detection algorithm of the loss of balance was composed of three main parts: parts of processing of measured data, construction of an internal model and detection of the loss of balance. The internal model represented a simple dynamic motion balancing with two rear legs of a four-legged chair and was a simplified model of a central nervous system of a person. The internal model was defined by the experimental data obtained within a fixed time interval, and was applied to the detecting algorithm to the end of the experiment without being changed. The balancing motion controlled by the human brain was improved in process of time because of the experience accruing to the brain from controlling sensory organs. In this study a reconstruction method of the internal model was used in order to improve the success rate and the detecting time of the algorithm and was changed with time the same as the brain did. When using the reconstruction method, the success rate and the detecting time were 95 % and 0.729 sec, respectively and those results were improved by about 7.6 % and 0.25 sec in comparison to the results of the paper of Ahmed and Ashton-Miller. The results showed that the proposed reconstruction method of the internal model was efficient to improve the detecting performance of the algorithm.

• IEIE Transactions on Smart Processing and Computing
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• 제5권5호
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• pp.327-330
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• 2016

Currently, the world's elderly population continues to grow at a dramatic rate. As the number of senior citizens increases, detection of someone falling has attracted increasing attention for visual surveillance systems. This paper presents a novel fall-detection algorithm using motion estimation and an integrated spatiotemporal energy map of the object region. The proposed method first extracts a human region using a background subtraction method. Next, we applied an optical flow algorithm to estimate motion vectors, and an energy map is generated by accumulating the detected human region for a certain period of time. We can then detect a fall using k-nearest neighbor (kNN) classification with the previously estimated motion information and energy map. The experimental results show that the proposed algorithm can effectively detect someone falling in any direction, including at an angle parallel to the camera's optical axis.

• 한국전문물리치료학회지
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• 제9권3호
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• pp.47-65
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• 2002

This study examined the correlations among the Berg Balance Scale, which is a clinical tool used to evaluate balance ability, spatiotemporal parameters of gait, and falling; determined the parameters most closely related to falling; and identified a discriminatory parameter and its predictability. Thirty-four subjects aged 72 to 92 years participated in this study. Following a questionnaire survey about falling, the Berg Balance Scale and spatiotemporal parameters of gait were measured. The results revealed that the incidence of falls increased with aging and an accompanying reduction in the flexion range of motion of the hip joint. The gait characteristics of elderly people who fell easily included a slower walking speed, shorter stride, and longer stance time than other elderly. When the cutoff score was set at 45, the Berg Balance Scale was able to identify correctly those individuals who truly have experience of falling than when the cutoff score was set at 39. But when the cutoff score was set at 39, the scale's specificity identifying correctly those individuals who truly have not experience of falling was higher than at the cutoff score of 45. Therefore, the Berg Balance Scale is an appropriate screening method in a clinical setting for the early detection of elderly people at risk of falling. In conclusion, elderly people with a Berg Balance Scale score. below 45 are the most likely to fall owing to their decreased balance ability.

• 한국소음진동공학회논문집
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• 제16권1호
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• pp.12-18
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• 2006

This research investigates the electromechanical characteristics of a spindle motor in a free-falling mobile hard disk drive before unexpected shock. Electromechanical simulation includes a time-stepping finite element analysis of the magnetic field in a speed controlled brushless DC motor and dynamic analysis of the stationary and rotating part linked by the fluid dynamic bearing under the free-falling condition. Analysis results show that the dynamic characteristics of the rotating spindle system during free-falling state have an effect on the relative motion between the stationary and rotating part of HDD. It results from the variation of reaction force in the bearing area due to the gravity force exerted on the rotating part of HDD, and the free-falling condition can be detected by observing the signal of the spindle motor and disk-head interface without using an accelerometer.

• 한국정보통신학회논문지
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• 제26권8호
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• pp.1165-1171
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• 2022

가속도 센서를 이용한 인간의 행동인식은 다양한 분야에 적용되고 있다. 스마트폰의 보급이 일반화되면서 스마트폰에 내장된 가속도 센서를 이용한 인간의 행동인식 방법이 연구되고 있다. 노인의 경우 넘어지게 되면 심각한 부상으로 이어지는 경우가 많으며 공사 현장에서도 넘어짐은 중요한 사고원인 중 하나이다. 본 연구는 스마트폰에 내장된 가속도 센서와 방향 센서를 이용하여 사람의 넘어지는 방향에 대해 연구하였다. 기존에는, 인간의 행동을 인식하기 위해서 가속도벡터의 크기를 활용하는 것이 일반적인 방법이었다. 본 연구는 최근 많이 활용되고 있는 딥러닝 기법을 적용하여 인간의 넘어지는 방향을 인식하는 방법을 제안하였다.