• Journal of the Korean Society of Clothing and Textiles
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• v.40 no.3
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• pp.495-505
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• 2016

This study explores and selects an appropriate material that considers light and soft physical properties as well as activity for impact absorption pads that can be used to develop practical impact protective clothes worn during daily life by the elderly to reduce the impact of falls. Physical properties, impact absorption performance, and compression characteristics were evaluated on 5 types of foam, 2 types of 3D spacer fabric, and 3 types of polymer gel to select a material appropriate for the pad to be inserted into impact protective clothes. The evaluation of the physical properties showed that 3D spacer fabrics had lower density compared to other materials and polymer gels had the highest density. The elongation percentage was higher in foams than 3D spacer fabrics and EPDM foam had the highest elongation percentage. The impact absorption performance of honeycomb polymer gel was better than foams and 3D spacer fabrics. As a result of looking into compression energy and compression characteristics of materials, 'CR foam A' was found to absorb the largest amount of compression energy, 24.1%, among foams and polymer gels. A high energy absorption rate of 50.0% (or above) was indicated by 3D spacer fabrics; however, foams and polymer gels showed a progressive deformation of energy compression / recovery curve with 3D spacer fabrics that showed drastic deformation. Based on characteristics of materials, 'CR foam C' and EPDM with relatively high absorption performance can be used as protective pad materials among foams. Among polymer gels, 2 open-type polymer gels that have relatively low impact protective performance but a relatively lighter weight on human body (compared to closed-type) are considered appropriate protective pad materials.

• Journal of Sensor Science and Technology
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• v.23 no.4
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• pp.251-258
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• 2014

While the feasibility of vertical velocity as a threshold parameter for pre-impact fall detection has been verified, effects of sensor attachment locations and methods calculating vertical acceleration and velocity on the detection performance have not been studied yet. Regarding the vertical velocity-based pre-impact fall detection, this paper investigates detection accuracies of eight different cases depending on sensor locations (waist vs. sternum), vertical accelerations (accurate acceleration based on both accelerometer and gyroscope vs. approximated acceleration based on only accelerometer), and vertical velocities (velocity with attenuation vs. velocity difference). Test results show that the selection of waist-attached sensor, accurate acceleration, and velocity with attenuation based on accelerometer and gyroscope signals is the best in overall in terms of sensitivity and specificity of the detection as well as lead time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1204-1208
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• 2011

In this study, we proposed the system that calculates the algorithm with an accelerometer signal and detects the fall shock and it's direction. In order to gather the activity patterns of fall status and attach on the subject's body without consciousness, the device needs to be small. With this aim, it is attached on the right side of subject's waist. With roll and pitch angle which represent the activity of upper body, the fall situation is determined and classified into the posture pattern. The impact is calculated by the vector magnitude of accelerometer signal. And in the case of the elderly keep the same posture after fall, it can distinguish the situation whether they can stand by themselves or not. Our experimental results showed that 95% successful detection rate of fall activity with 10 subjects. For further improvement of our system, it is necessary to include tasks-oriented classifying algorithm to diverse fall conditions.

• Journal of Sensor Science and Technology
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• v.24 no.1
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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%.

• Journal of Sensor Science and Technology
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• v.31 no.2
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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.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.438-448
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• 2010

The social activities of the elderly have been increasing as our society progresses toward an aging society. As their activities are increased, the occurrence of falls that could lead to fractures are increased. Falls are serious health hazards to the elderly and we need more thorough understanding of falls including the progress of falls and the impact area in various fall directions. Many of the traditional methods of falls research dealt with voluntary falls by younger subject since older subject can easily get fracture from voluntary falls. So, it has been difficult to get exact data about falls of the elderly. Here, we tried to capture the characteristics of the movements of major joints using three dimensional motion capture system during falls experiments using a moving mattress that can safely induce unexpected falls. Healthy younger subjects participated in the actual falls experiment and the moving mattress was actuated by a pneumatic system. The kinematic parameters such as velocities of major segments were imported to a computer simulation environment and falls to hard surfaces were simulated in a computational environment using a realistic human model of aged persons. The simulation was able to give approximations to contact forces which can occur during actual falls. And we designed impact absorption system to reduce the impact during falls. We can adapt this system to fracture prevention system that we are going to study.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.384-391
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• 2023

Among the elderly, fatal injuries and deaths are significantly attributed to falls. Therefore, a pre-impact fall detection system is necessary for injury prevention. In this study, a robust threshold-based algorithm was proposed for pre-impact fall detection, reducing false positives in highly dynamic daily-living movements. The algorithm was validated using public datasets (KFall and FARSEEING) that include the real-world elderly fall. A 6-axis IMU sensor (Movella Dot, Movella, Netherlands) was attached to S2 of 20 healthy adults (aged 22.0±1.9years, height 164.9±5.9cm, weight 61.4±17.1kg) to measure 14 activities of daily living and 11 fall movements at a sampling frequency of 60Hz. A 5Hz low-pass filter was applied to the IMU data to remove high-frequency noise. Sum vector magnitude of acceleration and angular velocity, roll, pitch, and vertical velocity were extracted as feature vector. The proposed algorithm showed an accuracy 98.3%, a sensitivity 100%, a specificity 97.0%, and an average lead-time 311±99ms with our experimental data. When evaluated using the KFall public dataset, an accuracy in adult data improved to 99.5% compared to recent studies, and for the elderly data, a specificity of 100% was achieved. When evaluated using FARSEEING real-world elderly fall data without separate segmentation, it showed a sensitivity of 71.4% (5/7).

• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.136-143
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• 2015

Snow-melting system has been applied not only to roads for car traffic but also to pavement for the pedestrians safety reason in some of the developed countries such as USA and Canada based on countermeasures against Natural Disasters Act revised in 2000. Even though this system was introduced in korea in 2006 and has been partly applied to car traffic roads, there is few places that the system has been applied. Therefore, in this research a snow-melting system with a block-type to cover a pavement that efficiently transfers heat form heat rays to the top of a pavement and protects the heat rays. A quality check showed that compression and bending strength was improved approximately 5 times stronger and 7 to 10 times more absorption rate than the KS(Korea Industrial Standard) requirement. Moreover, only 10 minute was required to increase temperature above zero with a block-type snow-melting system whereas approximately 180 minute was spent with the existing system. This research is expected to contribute to environmental issues and reduce accidents on a slippery road.