Background: Falls represent a critical public health issue involving neuromuscular control strategies to execute appropriate motor plans. While self-initiated falls (SIF) and motor-induced falls (MIF) are common laboratory paradigms, the continuous temporal differences in their underlying neuromuscular strategies remain poorly understood. Objects: We conducted experiments to identify and compare the neuromuscular control strategies between SIF and MIF across three directions using continuous waveform analysis. Methods: Eight healthy young adults performed SIF and MIF in forward, backward, and sideways directions while surface electromyography was recorded from the bilateral tibialis anterior (TA), lateral gastrocnemius, and medial gastrocnemius. Data were normalized via maximum voluntary isometric contraction and reference voluntary contraction and analyzed using statistical non-parametric mapping over the entire fall duration (0%–100%). Results: Neuromuscular divergence was strategically concentrated within three critical windows: initial, mid-to-terminal, and terminal phases. For the TA, MIF elicited significantly higher activation during the initial phase of sideways falls (1%–5%, p < 0.05) and the mid-to-terminal phase of backward falls (62%–75%, p < 0.05). For the GCM, significantly higher activation in MIF was observed during the terminal phase of forward falls (70%–99%, p < 0.05), both early (1%–24%, p < 0.05), and late (58%–98%, p < 0.05) phases of sideways falls, and the initial response phase of backward falls (1%–5%, p < 0.05). Conclusion: The central nervous system is characterized by high-amplitude activation patterns, which may reflect a response to unpredictable threats (MIF) compared to planned descents (SIF). These findings suggest the potential importance of direction-specific and timesensitive neuromuscular training to better prepare for real-world falls.

Background: Sarcopenia is a progressive age-related musculoskeletal disorder. Early identification is critical for effective intervention. However, current criteria are time-consuming and require various equipment, limiting their utility. Therefore, we utilized a single inertial measurement unit (IMU) sensor and machine learning to classify possible sarcopenia. Objects: This study aimed to develop a practical machine learning based classification model for possible sarcopenia. Methods: A total of 57 older adults participated and were classified into possible sarcopenia (n = 20) and non-possible sarcopenia (n = 37) groups based on the Asian Working Group on Sarcopenia 2019 guideline. We extracted the mean and variance from the whole-body IMU sensor data during gait and developed five machine learning classification models. Results: The left lower leg sensor demonstrated the highest classification performance among the whole-body sensors. Using the left lower leg sensor data, the support vector machine yielded an area under the receiver operating characteristic curve (AUROC) of 0.79. Notably, integrating demographic variables with IMU sensor features significantly enhanced the model’s performance, achieving an AUROC of 0.92. Conclusion: This study identified the lower leg as the optimal IMU sensor placement for screening possible sarcopenia. Furthermore, the proposed multimodal model, combining IMU sensor data with demographic information, serves as a highly accurate screening tool for possible sarcopenia. This practical model can help early detection of sarcopenia in community settings.

Background: Lower back pain/injuries are common in caregivers, and physical stresses at the lower back during patient care are considered a primary cause. An instrumented hospital bed my help reduce the physical loads during patient repositioning. Objects: We estimated the physical stresses at the lower back during patient repositioning to assess biomechanical benefits of the instrumented hospital bed. Methods: Fourteen individuals repositioned a patient lying on an instrumented hospital bed. Trials were acquired for three types of repositioning (boosting superiorly, pulling laterally, and rolling from supine to side-lying). Trials were also acquired with two bed heights (10 and 30 cm below the anterior superior iliac spine), and with and without the bed tilting feature. During trials, kinematics of an upper body and hand pulling forces were recorded to determine the compressive and shear forces using static equilibrium equations. Repeated measures ANOVA was used to test if the peak compressive and shear forces were associated with repositioning type (3 levels), bed height (2 levels), and bed feature (2 levels). Results: The peak compressive force ranged from 836 N to 3,954 N, and was associated with type (F = 14.661, p < 0.0005) and height (F = 10.044, p = 0.007), but not with bed feature (F = 0.003, p = 0.955). The peak shear force ranged from 66 to 473 N, and was associated with type (F = 8.021, p < 0.005), height (F = 6.548, p = 0.024), and bed feature (F = 22.978, p < 0.0005). Conclusion: The peak compressive force at the lower back during patient repositioning, draws one’s attention as it is, in some trials, close to or greater than the National Institute for Occupational Safety and Health safety criterion (3,400 N). Furthermore, the physical stress decreases by adjusting bed height, but not by using tilting feature of an instrumented bed.

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.