• 제목/요약/키워드: falls detection

검색결과 82건 처리시간 0.021초

Facial fractures and associated injuries in high- versus low-energy trauma: all are not created equal

  • Hilaire, Cameron St.;Johnson, Arianne;Loseth, Caitlin;Alipour, Hamid;Faunce, Nick;Kaminski, Stephen;Sharma, Rohit
    • Maxillofacial Plastic and Reconstructive Surgery
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    • 제42권
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    • pp.22.1-22.6
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    • 2020
  • Introduction: Facial fractures (FFs) occur after high- and low-energy trauma; differences in associated injuries and outcomes have not been well articulated. Objective: To compare the epidemiology, management, and outcomes of patients suffering FFs from high-energy and low-energy mechanisms. Methods: We conducted a 6-year retrospective local trauma registry analysis of adults aged 18-55 years old that suffered a FF treated at the Santa Barbara Cottage Hospital. Fracture patterns, concomitant injuries, procedures, and outcomes were compared between patients that suffered a high-energy mechanism (HEM: motor vehicle crash, bicycle crash, auto versus pedestrian, falls from height > 20 feet) and those that suffered a low-energy mechanism (LEM: assault, ground-level falls) of injury. Results: FFs occurred in 123 patients, 25 from an HEM and 98 from an LEM. Rates of Le Fort (HEM 12% vs. LEM 3%, P = 0.10), mandible (HEM 20% vs. LEM 38%, P = 0.11), midface (HEM 84% vs. LEM 67%, P = 0.14), and upper face (HEM 24% vs. LEM 13%, P = 0.217) fractures did not significantly differ between the HEM and LEM groups, nor did facial operative rates (HEM 28% vs. LEM 40%, P = 0.36). FFs after an HEM event were associated with increased Injury Severity Scores (HEM 16.8 vs. LEM 7.5, P <0.001), ICU admittance (HEM 60% vs. LEM 13.3%, P <0.001), intracranial hemorrhage (ICH) (HEM 52% vs. LEM 15%, P <0.001), cervical spine fractures (HEM 12% vs. LEM 0%, P = 0.008), truncal/lower extremity injuries (HEM 60% vs. LEM 6%, P <0.001), neurosurgical procedures for the management of ICH (HEM 54% vs. LEM 36%, P = 0.003), and decreased Glasgow Coma Score on arrival (HEM 11.7 vs. LEM 14.2, P <0.001). Conclusion: FFs after HEM events were associated with severe and multifocal injuries. FFs after LEM events were associated with ICH, concussions, and cervical spine fractures. Mechanism-based screening strategies will allow for the appropriate detection and management of injuries that occur concomitant to FFs. Type of study: Retrospective cohort study. Level of evidence: Level III.

스마트폰 환경에서 가속도 벡터의 성분과 방향센서를 활용한 넘어지는 방향 측정 (Fall Direction Detection using the Components of Acceleration Vector and Orientation Sensor on the Smartphone Environment)

  • 이우식;송특섭
    • 한국멀티미디어학회논문지
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    • 제18권4호
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    • pp.565-574
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    • 2015
  • Falls are the main cause of serious injuries and accidental deaths in people over the age of 65. Due to widespread adoption of smartphones, there has been a growing interest in the use of smartphones for detecting human behavior and activities. Modern smartphones are equipped with a wide variety of sensors such as an accelerometer, a gyroscope, camera, GPS, digital compass and microphone. In this paper, we introduce a new method that determines the fall direction of human subjects by analyzing the three axis components of acceleration vector.

이동 로봇을 위한 3차원 거리 측정 장치기반 비포장 도로 인식 (3D Depth Measurement System-based Unpaved Trail Recognition for Mobile Robots)

  • 김성찬;김종만;김형석
    • 제어로봇시스템학회논문지
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    • 제12권4호
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    • pp.395-399
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    • 2006
  • A method to recognize unpaved road region using a 3D depth measurement system is proposed for mobile robots. For autonomous maneuvering of mobile robots, recognition of obstacles or recognition of road region is the essential task. In this paper, the 3D depth measurement system which is composed of a rotating mirror, a line laser and mono-camera is employed to detect depth, where the laser light is reflected by the mirror and projected to the scene objects whose locations are to be determined. The obtained depth information is converted into an image. Such depth images of the road region represent even and plane while that of off-road region is irregular or textured. Therefore, the problem falls into a texture identification problem. Road region is detected employing a simple spatial differentiation technique to detect the plain textured area. Identification results of the diverse situation of unpaved trail are included in this paper.

진동가속도센서를 이용한 Reduction Gear Box Noise 검출시스템 개발 (A Development of Noise Detection System Utilizing the Vibrating Accelerative Sensor for the Reduction Gear Box)

  • 천종필;편영식
    • 한국소음진동공학회:학술대회논문집
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    • 한국소음진동공학회 2009년도 춘계학술대회 논문집
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    • pp.274-279
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    • 2009
  • Reduction Gear Box where from productive site uses the gear with power delivery with high mechanical efficiency of power a deceleration and as the mechanical element union product which has the velocity ratio which is various together is produced with the power occurrence motor and leads gets a high driving force is plentifully used. The above occurs from gear drive issue sound Whine, Noise and Vibration as occurring from the rim process which the gear will bite mainly is delivered with the case etc. gear drive whole which leads the axis and the bearing. The productivity falls with the going straight rate decrease which with like this problem point is caused by with rework the problem point where the cost of production rises under improving boil many kinds analyzed the plan and investigates the resultant acceleration sensor which and a frequency analysis system and was made to apply.

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3D Vision-based Security Monitoring for Railroad Stations

  • Park, Young-Tae;Lee, Dae-Ho
    • Journal of the Optical Society of Korea
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    • 제14권4호
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    • pp.451-457
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    • 2010
  • Increasing demands on the safety of public train services have led to the development of various types of security monitoring systems. Most of the surveillance systems are focused on the estimation of crowd level in the platform, thereby yielding too many false alarms. In this paper, we present a novel security monitoring system to detect critically dangerous situations such as when a passenger falls from the station platform, or when a passenger walks on the rail tracks. The method is composed of two stages of detecting dangerous situations. Objects falling over to the dangerous zone are detected by motion tracking. 3D depth information retrieved by the stereo vision is used to confirm fallen events. Experimental results show that virtually no error of either false positive or false negative is found while providing highly reliable detection performance. Since stereo matching is performed on a local image only when potentially dangerous situations are found; real-time operation is feasible without using dedicated hardware.

가중치를 부여한 QPSK/PCM 음성신호의 소거대역 설정에 의한 신호수신 (Weighted QPSK/PCM Speech Signal Detection with the Erasure Zone)

  • 안승춘;이문호
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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    • pp.179-182
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    • 1988
  • Since the bits in any encoded PCM word are of different importance to the bit positions, in order to improve the signal to noise ratio the technique that the encoded signal bits are weighted for the QPSK transmission system, is presented. Also the erasure zone is established at the detector, such that if the output falls into the erasure zone, the regenerated sample is replaced by interpolation. Two weighting methods are shown here. One is the method that the same weighting profile is used to Q and I dimension in QPSK signal constellations. The other is diferent weighting to Q and I dimension. The gains of this new technique in overall signal s/n compared to conventional QPSK transmission system were 5 db and 2db, respectively.

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Improvement in Transformer Diagnosis by DGA using Fuzzy Logic

  • Dhote, Nitin K.;Helonde, J.B.
    • Journal of Electrical Engineering and Technology
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    • 제9권2호
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    • pp.615-621
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    • 2014
  • Power transformer is one of the most important equipments in electrical power system. The detection of certain gases generated in transformer is the first indication of a malfunction that may lead to failure if not detected. Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results falls outside conventional method codes or when more than one fault exist in transformer. To overcome these limitations, fuzzy inference system (FIS) is proposed. 250 different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

딥러닝 기반 낙상 인식 알고리듬 (Fall detection algorithm based on deep learning)

  • 김남호
    • 한국정보통신학회:학술대회논문집
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    • 한국정보통신학회 2021년도 춘계학술대회
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    • pp.552-554
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    • 2021
  • 도플러 레이더 센서로 취득한 움직임 데이터를 딥러닝 알고리듬을 사용한 낙상 인식 시스템을 제안한다. 딥러닝 알고리듬중 시계열 데이터에 장점을 가지는 RNN을 사용하여 낙상 인식에 적용한다. 도플러 레이더 센서의 낙상데이터는 시계열 데이터로 시간적인 특성을 가지고 있으며 결과는 낙상인지 아닌지 만을 판단하기 때문에 RNN의 구조를 시퀀스 입력에 고정 크기를 출력하는 구조로 설계하였다.

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가속도 센서를 이용한 실시간 스포츠 동작 분류.모니터링에 관한 연구 (A Study on Real-Time Sports Activity Classification & Monitoring Using a Tri-axial Accelerometer)

  • 강동원;최진승;탁계래
    • 한국운동역학회지
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    • 제18권2호
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    • pp.59-64
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    • 2008
  • 본 연구는 3축 가속도 센서를 허리에 부착하여 실시간으로 스포츠 동작분류를 할 수 있는 모니터 링에 관한 연구를 실시하였다. 이 모니터링 시스템은 스포츠 동작의 기본이라고 할 수 있는 걷기, 달리기, 자세변화 동지 정지상태의 동작들과 추가적으로 사이클링 동작을 분류할 수 있도록 하였다. 또한 운동 시에 발생할 수 있는 낙상을 감지하여 위급상황에 대한 정보도 나타나게 하였다. 가속도센서모듈은 인체에 부착된 형태로 스포츠 활동을 모니터링하기 위하여 소형으로 설계되었으며 활동에 방해가 되지 않게 허리에 부착되었다. 측정된 데이터는 RF통신을 통해 PC로 전송되며 알고리즘을 통해 실시간으로 동작분류를 시행하게 된다. 개발된 알고리즘을 검증하기 위한 실험으로 5명의 피험자를 대상으로 서로 다른 속도의 걷기, 달리기, 사이클링 동작을 각각 100초간 실시하였으며 낙상과 자세변화 동작(앉았다 일어서기, 누웠다 일어서기, 서있다 앉기, 누웠다 앉기, 서있다 눕기, 앉았다 눕기)은 각각 20회씩 실행하였다. 그 결과 동작분류 정확도는 95.4%를 나타내었다. 이번 연구에서 스포츠 모니터링을 통하여 정확한 자신의 운동 정보를 알려주고 운동 시에 발생하는 낙상에 대한 위급상황을 알려줌으로써 스포츠 활동에 도움을 주고자 하였으며, 추가적인 연구로 각각의 스포츠 활동에 대한 정확한 에너지 소비 추정 알고리즘을 개발 중에 있다.

딥러닝을 이용한 조선소에서 쓰러진 작업자의 검출에 관한 연구 (A Study on the Detection of Fallen Workers in Shipyard Using Deep Learning)

  • 박경민;김선덕;배철오
    • 해양환경안전학회지
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    • 제26권6호
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    • pp.601-605
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    • 2020
  • 선박은 크고, 복잡한 구조로 되어 있기 때문에 다른 작업자의 위치를 알아내기 어려우며, 특히 작업자가 쓰러진 경우에는 발견하기가 쉽지 않아 신속한 대처가 어렵다. 그리하여, 신체에 디바이스를 부착하는 방법이나 카메라를 이용하여 쓰러짐을 검출하기 위한 연구가 진행되고 있다. 기존의 영상기반 쓰러짐 검출은 사람의 신체부위를 검출하여 쓰러짐을 판단하였으나, 조선소에서는 다양한 복장과 자세로 작업으로 인해 검출하기가 어렵다. 본 논문에서는 쓰러짐 영역 전체를 추출하여 딥러닝 학습으로 선박 작업자의 쓰러짐을 이미지 기반으로 검출하였다. 학습에 필요한 데이터는 조선소의 건조중인 선박에서 쓰러진 모습을 연출하여 획득하였으며, 이미지를 좌우대칭, 크기조절, 회전하여 학습 데이터의 수를 증가하였다. 성능평가는 정밀도, 재현율, 정확도 그리고 오차율로 평가하였으며, 데이터의 수가 많을수록 정밀도가 향상되었다. 다양한 데이터를 보강하면 카메라를 이용한 쓰러짐 검출 모델의 실효성이 향상됨으로서 안전 분야에 기여할 수 있을 것으로 사료된다.