The purpose of this study was to develop an accurate and efficient algorithm for post-fall detection. Thirty healthy male subjects were recruited and asked to perform 23 movements, comprising 14 activities of daily living and nine fall motions. The algorithm was developed using the ANN toolbox provided with MATLAB and inertial measurement unit (IMU) data were used to distinguish between fall and non-fall cases. An IMU sensor was located at the center between the left and right anterior superior iliac spines. A total of 32 feature vectors were extracted from 3-axis acceleration and angular velocity signals. Based on the five different ranking algorithms (relief-F, T-score, correlation, Fisher score, and minimum redundancy maximum relevance) used, feature vector subsets comprising the feature vectors were created and subsequently evaluated. Accuracy was compared according to the number of feature vectors constituting the subset, which were based on rank-lists. The results showed that the subset comprising all the feature vectors showed the best accuracy (99.86%), but a similar accuracy could be obtained with a subset comprising fewer feature vectors. The T-score was found to be the most optimal among the five ranking algorithms. Furthermore, T-score with two feature vectors achieved an accuracy of 99.17%. The results of this study are expected to assist in the construction of subsets of feature vectors based on ranking algorithms for post-fall detection with high accuracy and less computational cost.

这项研究的目的是开发一种准确有效的跌倒后检测算法。招募了30名健康的男性受试者，并要求他们进行23次运动，包括14次日常生活活动和9次跌倒运动。该算法是使用MATLAB提供的ANN工具箱开发的，惯性测量单位（IMU）数据用于区分跌倒和非跌倒情况。一个IMU传感器位于左右terior前上棘之间的中心。从3轴加速度和角速度信号中总共提取了32个特征向量。基于所使用的五种不同的排序算法（浮雕F，T分数，相关性，Fisher评分和最小冗余最大相关性），创建并随后评估了包含特征向量的特征向量子集。根据构成子集的特征向量的数量（基于等级列表）比较准确性。结果表明，包含所有特征向量的子集显示出最佳的准确性（99.86％），但是包含较少特征向量的子集可以获得相似的准确性。发现T分数是五种排名算法中最佳的。此外，具有两个特征向量的T得分的准确性达到了99.17％。预期该研究的结果将有助于基于排名算法的特征向量子集的构建，以用于落后检测，具有较高的准确性和较低的计算成本。结果表明，包含所有特征向量的子集显示出最佳的准确性（99.86％），但是使用包含较少特征向量的子集可以获得类似的准确性。发现T分数是五种排名算法中最佳的。此外，具有两个特征向量的T得分的准确性达到了99.17％。预期该研究的结果将有助于基于排名算法的特征向量子集的构建，以用于落后检测，具有较高的准确性和较低的计算成本。结果表明，包含所有特征向量的子集显示出最佳的准确性（99.86％），但是使用包含较少特征向量的子集可以获得类似的准确性。发现T分数是五种排名算法中最佳的。此外，具有两个特征向量的T得分的准确性达到了99.17％。预期该研究的结果将有助于基于排名算法的特征向量子集的构建，以用于落后检测，具有较高的准确性和较低的计算成本。