• 센서학회지
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• 제31권2호
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• 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.

• 센서학회지
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• 제24권1호
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• pp.47-53
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• 2015

Fall-related injuries in elderly people are a major health care problem. This paper introduces determination of fall direction before impact using support vector machine (SVM). Once a falling phase is detected, dynamic characteristic parameters measured by the accelerometer and gyroscope and then processed by a Kalman filter are used in the SVM to determine the fall directions, i.e., forward (F), backward (B), rightward (R), and leftward (L). This paper compares the determination sensitivities according to the selected parameters for the SVM (velocities, tilt angles, vs. accelerations) and sensor attachment locations (waist vs. chest) with regards to the binary classification (i.e., F vs. B and R vs. L) and the multi-class classification (i.e., F, B, R, vs. L). Based on the velocity of waist which was superior to other parameters, the SVM in the binary case achieved 100% sensitivities for both F vs. B and R vs. L, while the SVM in the multi-class case achieved the sensitivities of F 93.8%, B 91.3%, R 62.3%, and L 63.6%.

• 한국산학기술학회논문지
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• 제11권5호
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• pp.1564-1572
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• 2010

본 연구에서는 3축 가속도 신호를 이용하여 낙상과 낙상 방향을 검출하는 시스템을 구현하였다. 가속도 신호는 3축 가속도 센서로부터 획득하였으며, 획득된 신호를 USB 인터페이스를 통하여 PC에 전달하였다. PC에 전송된 신호를 제안한 알고리즘을 사용하여 낙상을 검출하였으며, 퍼지 분류기를 사용하여 낙상의 방향을 분류하였다. 실험을 위하여 실험대상군 6명 선정하였으며, 가슴에 가속도계를 부착한 후 실험을 수행하였다. 실험대상자는 5초 동안 정상 보행을 한 후 4 가지 방향(전 후 좌 우)으로 낙상이 발생하도록 하였으며, 낙상에 소요되는 시간은 최소 2초로 설정하였다. 본 연구에서 제안된 알고리즘을 이용하여 낙상을 검출하였으며 낙상 발생 후 1초부터 데이터를 분석하고 퍼지 분류기를 이용하여 낙상방향을 분류하였다. 낙상 검출율은 평균 94.79%이었다. 낙상 방향에 따른 분류율은 front_fall은 95.83%, back_fall은 100%, left_fall 은 87.5%, right_fall은 95.83%이었다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 추계학술대회 논문집
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• pp.447-448
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• 2006

• 한국전문물리치료학회지
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• 제28권1호
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• pp.84-91
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• 2021

Background: A hip fracture may occur spontaneously prior to the hip impact, due to the muscle pulling force exceeding the strength of the femur. Objects: We conducted falling experiments with humans to measure the activity of the hip muscles, and to examine how this was affected by the fall type. Methods: Eighteen individuals fell and landed sideways on a mat, by mimicking video-captured real-life older adults' falls. Falling trials were acquired with three fall directions: forward, backward, or sideways, and with three knee positions at the time of hip impact, where the landing side knee was free of constraint, or contacted the mat or the contralateral knee. During falls, the activities of the iliopsoas (Ilio), gluteus medius (Gmed), gluteus maximus (Gmax) and adductor longus (ADDL) muscles were recorded. Outcome variables included the time to onset, activity at the time of hip impact, and timing of the peak activity with respect to the time of hip impact. Results: For Ilio, Gmed, Gmax, and ADDL, respectively, EMG onset averaged 292, 304, 350, and 248 ms after fall initiation. Timing of the peak activity averaged 106, 96, 84, and 180 ms prior to the hip impact, and activity at the time of hip impact averaged 72.3, 45.2, 64.3, and 63.4% of the peak activity. Furthermore, the outcome variables were associated with fall direction and/or knee position in all but the iliopsoas muscle. Conclusion: Our results provide insights on the hip muscle activation during a fall, which may help to understand the potential injury mechanism of the spontaneous hip fracture.

• 한국전문물리치료학회지
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• 제30권1호
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• pp.78-85
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• 2023

Background: Despite fall prevention strategies suggested by researchers, falls are still a major health concern in older adults. Understanding factors that differentiate successful versus unsuccessful balance recovery may help improve the prevention strategies. Objects: The purpose of this review was to identify biomechanical factors that differentiate successful versus unsuccessful balance recovery in the event of a fall. Methods: The literature was searched through Google Scholar and PubMed. The following keywords were used: 'falls,' 'protective response,' 'protective strategy,' 'automated postural response,' 'slips,' 'trips,' 'stepping strategy,' 'muscle activity,' 'balance recovery,' 'successful balance recovery,' and 'failed balance recovery.' Results: A total of 64 articles were found and reviewed. Most of studies included in this review suggested that kinematics during a fall was important to recover balance successfully. To be successful, appropriate movements were required, which governed by several things depending on the direction and characteristics of the fall. Studies also suggested that lower limb muscle activity and joint moments were important for successful balance recovery. Other factors associated with successful balance recovery included fall direction, age, appropriate protective strategy, overall health, comorbidity, gait speed, sex and anticipation of the fall. Conclusion: This review discusses biomechanical factors related to successful versus unsuccessful balance recovery to help understand falls. Our review should help guide future research, or improve prevention strategies in the area of fall and injuries in older adults.

• 재활간호학회지
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• 제1권1호
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• pp.43-50
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• 1998

The literature review for fall in the elderly has been done for the better quality of life of increasing elderly people toward 21 st century. Because 30 to 50% of over sixty five years old persons have experiences of fall, five percent of the fallen have trauma such as bone fracture requiring hospitalization and three quarter of people who die as fall are over 65 year old, fall is important health problem of them. There are very little societal interest in and research related to fall. Therefore, among recent foreign and our literatures studying literature review of frequency of fall, risk factors and assessing method for tall, and the management of fall prevention program, I would like to find research direction. Conclusivelly, we should study extensively the survey of the elderly's fall and on the basis of it. developing fall prevention program, promote the elderly's health through fall prevention.

• 한국정보통신학회논문지
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• 제19권2호
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• pp.336-342
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• 2015

넘어짐은 노인이나 산업현장에서 일하는 사람들에게 심각한 부상을 일으키는 원인이 되기 때문에 센서를 사용하여 넘어짐을 판단하고자 하는 연구가 활발히 진행되고 있다. 최근 들어 스마트폰의 보급이 일반화 되면서 스마트 폰에 내장된 센서를 사용하여 넘어짐을 판단하는 방법이 연구되고 있다. 가속도 센서에서 추출된 가속도벡터의 변화량을 분석하면 넘어짐은 어렵지 않게 판단할 수 있지만, 넘어지는 방향을 판단하기 위해서는 가속도벡터의 크기의 변화나 방향으로의 변화로 판단하기 어렵다. 일반적으로 가속도 벡터의 방향은 물체의 움직임의 방향을 의미하지 않기 때문이다. 한편, 속도 벡터는 물체가 움직이는 방향의 접선방향으로 나타나는 성질을 사용하여 넘어지는 방향을 판단하는 방법을 제안하였다.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2003년도 추계학술발표회
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• pp.163-166
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• 2003

The seasonal mean wind direction and wind speed in a greater coasting area are investigated using the ECMWF(European Centre for Medium-Range Weather Forecasts) data for 11 years from 1985 to 1995. In winter, the main wind direction in Korea and vicinity, Taiwan and vicinity, and the North Pacific Ocean of middle latitudes is a northwesterly wind, northeasterly wind, and westerly wind respectively. The wind speed is strongest in the East China Sea, the South China Sea, and the North Pacific Ocean of low latitudes(Beaufort wind scale 5-6). A distribution pattern of wind direction in spring and fall is similar to that in winter. Seasonal mean wind speed is strongest in winter and the next is fall. The wind speed in summer is generally weak. However, that in the Indochina and vicinity is strong by the influence of Asian monsoon.