Department of Computer Software Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
Sensors (Basel). 2021 Mar 12;21(6):2006. doi: 10.3390/s21062006.
Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities in order to provide immediate medical assistance. Hence, the integrity of collected data becomes imperative. Presence of missing values in data, caused by unreliable data delivery, lossy sensors, local interference and synchronization disturbances and so forth, greatly hamper the credibility and usefulness of data making it unfit for reliable fall detection. This paper presents a noise tolerant FDS performing in presence of missing values in data. The work focuses on Deep Learning (DL) particularly Recurrent Neural Networks (RNNs) with an underlying Bidirectional Long Short-Term Memory (BiLSTM) stack to implement FDS based on wearable sensors. The proposed technique is evaluated on two publicly available datasets-SisFall and UP-Fall Detection. Our system produces an accuracy of 97.21% and 97.41%, sensitivity of 96.97% and 99.77% and specificity of 93.18% and 91.45% on SisFall and UP-Fall Detection respectively, thus outperforming the existing state of the art on these benchmark datasets. The resultant outcomes suggest that the ability of BiLSTM to retain long term dependencies from past and future make it an appropriate model choice to handle missing values for wearable fall detection systems.
鉴于老年人非故意摔倒的高发生率和不利影响，摔倒检测已成为公众关注的焦点。摔倒检测系统（FDS）从传感器收集信息，以区分摔倒和日常活动，从而提供即时医疗援助。因此，收集数据的完整性变得至关重要。由于数据传输不可靠、传感器损耗、本地干扰和同步干扰等原因导致数据中存在缺失值，极大地影响了数据的可信度和有用性，使其不适合可靠的摔倒检测。本文提出了一种在数据中存在缺失值的情况下具有抗噪能力的 FDS。这项工作专注于深度学习（DL），特别是具有底层双向长短期记忆（BiLSTM）堆叠的递归神经网络（RNN），以基于可穿戴传感器实现 FDS。所提出的技术在两个公开可用的数据集-SisFall 和 UP-Fall Detection 上进行了评估。我们的系统在 SisFall 和 UP-Fall Detection 上分别产生了 97.21%和 97.41%的准确率、96.97%和 99.77%的敏感度以及 93.18%和 91.45%的特异性，从而在这些基准数据集上优于现有技术水平。结果表明，BiLSTM 保留过去和未来长期依赖关系的能力使其成为处理可穿戴摔倒检测系统中缺失值的合适模型选择。
