Background: In this study, kinematics and kinetics of the lower extremity joint and the lumbar lordosis during two different symmetrical lifting techniques(squat and stoop) were examined using the three-dimensional motion analysis. Methods: Twenty-six young male volunteers were selected for the subjects in this study. While they lifted boxes weighing 5, 10 and 15 kg by both squat and stoop lifting techniques, their motions were captured and analyzed using the 3D motion analysis system which was synchronized with two forceplates and the electromyographic system. Joint kinematics was determined by the forty-three reflective markers which were attached on the anatomical locations based on the VICON Plug-in-Gait marker placement protocol. Joint kinetics was analyzed by using the inverse dynamics. Paired t-test and Kruskal-Wallis test was used to compare the differences of variables between two techniques, and among three different weights. Correlation coefficient was calculated to explain the role of lower limb joint motion in relation to the lumbar lordosis. Results: There were not significant differences in maximum lumbar joint moments between two techniques. The hip and ankle contributed the most part of the support moment during squat lifting, and the knee flexion moment played an important role in stoop lifting. The hip, ankle and lumbar joints generated power and only the knee joint absorbed power in the squat lifting. The knee and ankle joints absorbed power, the hip and lumbar joints generated power in the stoop lifting. The bi-articular antagonist muscles' co-contraction around the knee joint during the squat lifting and the eccentric co-contraction of the gastrocnemius and the biceps femoris were found important for maintaining the straight leg during the stoop lifting. At the time of lordotic curvature appearance in the squat lifting, there were significant correlations in all three lower extremity joint moments with the lumbar joint. Differently, only the hip moment had significant correlation with the lumbar joint in the stoop lifting. Conclusion: In conclusion, the knee extension which is prominent kinematics during the squat lifting was produced by the contributions of the kinetic factors from the hip and ankle joints(extensor moment and power generation) and the lumbar extension which is prominent kinematics during the stoop lifting could be produced by the contributions of the knee joint kinetic factors(flexor moment, power absorption, bi-articular muscle function).

In this study, pre-impact fall detection algorithms were developed based on data gathered by a custom-made inertial measurement unit (IMU). Four types of simulated falls were performed by 40 healthy subjects (age: 23.4 ± 4.4 years). The IMU recorded acceleration and angular velocity during all activities. Acceleration, angular velocity, and trunk inclination thresholds were set to 0.9 g, 47.3°/s, and 24.7°, respectively, for a pre-impact fall detection algorithm using vertical angles (VA algorithm); and 0.9 g, 47.3°/s, and 0.19, respectively, for an algorithm using the triangle feature (TF algorithm). The algorithms were validated by the results of a blind test using four types of simulated falls and six types of activities of daily living (ADL). VA and TF algorithms resulted in lead times of 401 ± 46.9 ms and 427 ± 45.9 ms, respectively. Both algorithms were able to detect falls with 100% accuracy. The performance of the algorithms was evaluated using a public dataset. Both algorithms detected every fall in the SisFall dataset with 100% sensitivity). The VA algorithm had a specificity of 78.3%, and TF algorithm had a specificity of 83.9%. The algorithms had higher specificity when interpreting data from elderly subjects. This study showed that algorithms using angles could more accurately detect falls. Public datasets are needed to improve the accuracy of the algorithms.

Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously.

Workers at construction sites are prone to fall-from-height (FFH) accidents. The severity of injury can be represented by the acceleration peak value. In the study, a risk prediction against FFH was made using IMU sensor data for accident prevention at construction sites. Fifteen general working movements (NF: non-fall), five low-hazard-fall movements, (LF), and five high-hazard-FFH movements (HF) were performed by twenty male subjects and a dummy. An IMU sensor was attached to the T7 position of the subject to measure the three-axis acceleration and angular velocity. The peak acceleration value, calculated from the IMU data, was 4 g or less in general work movements and 9 g or more in FFHs. Regression analysis was performed by applying various deep learning models, including 1D-CNN, 2D-CNN, LSTM, and Conv-LSTM, to the risk prediction, and then comparing them in terms of their mean absolute error (MAE) and mean squared error (MSE). The FFH risk level was estimated based on the predicted peak acceleration. The Conv-LSTM model trained by MAE showed the smallest error (MAE: 1.36 g), and the classification with the predicted peak acceleration showed the best accuracy (97.6%). This study successfully predicted the FFH risk levels and could be helpful to reduce fatal injuries at construction sites.