Design and Research of Wearable Fall Protection Device for the Elderly / 中国医疗器械杂志

A protective device was designed that can be worn on the elderly, which consists of protective airbag, control box and protective mechanism. The combined acceleration, combined angular velocity and human posture angle are selected as the parameters to determine the fall, and the threshold algorithm and SVM algorithm are used to detect the fall. The protective mechanism is an inflatable device based on CO2 compressed air cylinder, and the equal-width cam structure is applied to its transmission part to improve the puncture efficiency of the compressed gas cylinder. A fall experiment was designed to obtain the combined acceleration and angular velocity eigenvalues of fall actions (forward fall, backward fall and lateral fall) and daily activities (sitting-standing, walking, jogging and walking up and down stairs), showing that the specificity and sensitivity of the protection module reached 92.1% and 84.4% respectively, which verified the feasibility of the fall protection device.

Turn intention perception and fall detection for smart walkers / 中国康复理论与实践

ObjectiveTo improve the anti-fall capacity and safety of the smart walkers. MethodsTwo pressure sensors were placed on the handles on both sides of the walker. The confusion matrix was obtained, the corresponding operational intent labels were manually labeled, using a support vector machine (SVM) classifier for model prediction to predict the travel intent of the users. The user wore a gyroscope and the walker was equipped with a laser sensor, to measure the angular velocity, angular acceleration and the distance data, respectively, to detect the user's fall. ResultsThe classifier model established by SVM successfully predicted three operating states of the walker, namely straight ahead, left turning and right turning. The user's fall was detected by the sudden change of the following data: the combined angular velocity was greater than 100°/s, the combined angular acceleration was greater than 1.3 G, the angular acceleration of Z-axis was greater than 0.7 G or less than 0.2 G, and the distance was greater than 600 mm or less than 300 mm. ConclusionThe improvement of the walker can predict the turn intention of the user, and detect the user's fall.

Fall Detection Based on Multi-feature Fusion of Human Body Acceleration and K-Nearest Neighbor / 中国康复理论与实践

@#Objective To develop a kind of algorithm for fall detection based on human acceleration. Methods From September to November, 2017, six healthy postgraduates participating in the experiment completed 13 acts of falls and eleven of activities of daily life. The information of activities was collected through two acceleration sensors, 81 acceleration features were extracted from each sensor, and were reduced dimension through principal component analysis. K-nearest neighbor was used to detect the falls and activities of daily living. Results The sensitivity of fall detection was 100%, the specificity was 99.76%, and the detection time was 216 ms. Conclusion The algorithm of multi-feature fusion of human body acceleration and K-nearest neighbor is accurate and timely.

Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data / 대한의료정보학회지

OBJECTIVES: Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. METHODS: We first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. RESULTS: To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. CONCLUSIONS: In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios.