Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model’s performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

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.

본 연구는 항공기 및 자동차 기술의 융합과 파급이 자동차 기업의 성과에 미친 영향을 분석한다. 구체적으로 본 연구는 항공기 관련 기술을 도입한 자동차 기업들의 재무 성과 변화를 측정하여 분석한다. 이를 위해 본 연구는 자동차 기업들이 항공기 기술과 관련 된 특허 변수들을 활용하는 정도와 방식을 측정하고, 이 변수들이 자동차 관련 기업들의 시장가치와 매출액에 미치는 영향을 분석한다. 분석 결과, 자동차 기업이 항공기 관련 기술을 활용할수록 시장가치가 상승하였다. 반면, 항공기 기술의 활용과 자동차 기업의 매출액 간에 는 유의미한 관계가 발견되지 않았다. 이는 항공기 관련 기술의 보유 및 사용이 상품 시장에서 직접적인 매출액 증가 보다는 금융 시장을 통해 기업의 기술력에 대한 신호 기능을 수행 하고 있음을 시사한다.