老年人群中的跌倒会带来重大的健康风险,通常导致发病率和生活质量下降。传统的跌倒检测方法,即可穿戴设备和相机,有限制,如照明条件和隐私问题。基于雷达的跌倒检测已经成为一种有希望的替代方案,提供不引人注目的技术。在这项研究中,已经尝试使用平滑的伪wigner-ville分布(SPWVD)图像和XGBoost学习对跌倒检测进行分类。为此,在线公开可用的雷达数据库(N=15)被考虑。雷达信号用于时频表示图像的SPWVD。提取十个特征并将其应用于XGBoost学习。进行实验并使用10倍交叉验证评估性能。所提出的方法能够区分老年人跌倒。使用XGBoost学习,该方法产生最大的平均分类精度,f1-score,精度,灵敏度,特异性,kappa得分为87.47%,87.38%,88.12%,86.81%,分别为88.31%和74.94%。传统特征与浓度测量和中值频率的组合获得了第二好的性能。因此,拟议的框架可用于准确有效地检测老年人在其私人空间中的跌倒情况。
Falls among the elderly population pose significant health risks, often leading to morbidity and decreased quality of life. Traditional fall detection methods, namely wearable devices and cameras, have limitations such as lighting conditions and privacy concerns.
Radar-based fall detection has emerged as a promising alternative, offering unobtrusive technique. In this study, an attempt has been made to classify fall detection using smoothed pseudo wigner-ville distribution (SPWVD) images and XGBoost learning. For this, online publicly available
radar database (N=15) is considered.
Radar signals is employed to SPWVD for time-frequency representation images. Ten features are extracted and applied to XGBoost learning. Experiments are performed and performance is evaluated using 10-fold cross validation. The proposed approach is able to discriminate elderly fall. Using XGBoost learning, the approach yields a maximum average classification accuracy, f1-score, precision, sensitivity, specificity, and kappa scores of 87.47%, 87.38%, 88.12%, 86.81%, 88.31% and 74.94% respectively. The combination of conventional features with concentration measures and median frequency obtained the second best performance. Thus, the proposed framework could be utilized for accurate and efficient detection of falls among the elderly population in their private spaces.