PDF(Pubmed)
Abstract：
Elderly falls are a major concerning threat resulting in over 1.5-2 million elderly people experiencing severe injuries and 1 million deaths yearly. Falls experienced by Elderly people may lead to a long-term negative impact on their physical and psychological health conditions. Major healthcare research had focused on this lately to detect and prevent the fall. In this work, an Artificial Intelligence (AI) edge computing based wearable device is designed and developed for detection and prevention of fall of elderly people. Further, the various deep learning algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) are utilized for activity recognition of elderly. Also, the CNN-LSTM, RNN-LSTM and GRU-LSTM with and without attention layer respectively are utilized and the performance metrics are analyzed to find the best deep learning model. Furthermore, the three different hardware boards such as Jetson Nano developer board, Raspberry PI 3 and 4 are utilized as an AI edge computing device and the best deep learning model is implemented and the computation time is evaluated. Results demonstrate that the CNN-LSTM with attention layer exhibits the accuracy, recall, precision and F1_Score of 97%, 98%, 98% and 0.98 respectively which is better when compared to other deep learning models. Also, the computation time of NVIDIA Jetson Nano is less when compared to other edge computing devices. This work appears to be of high societal relevance since the proposed wearable device can be used to monitor the activity of elderly and prevents the elderly falls which improve the quality of life of elderly people.
摘要：
老年人跌倒是一个主要的威胁，每年导致150-200万老年人遭受严重伤害和100万人死亡。老年人遭受的跌倒可能会对他们的身心健康状况产生长期的负面影响。最近，主要的医疗保健研究集中在这一点上，以检测和防止跌倒。在这项工作中,设计并开发了一种基于人工智能（AI）边缘计算的可穿戴设备，用于检测和预防老年人跌倒。Further,各种深度学习算法，如卷积神经网络(CNN)，循环神经网络(RNN)长短期记忆(LSTM)门控递归单元（GRU）用于老年人的活动识别。此外，CNN-LSTM,分别利用具有和不具有关注层的RNN-LSTM和GRU-LSTM，并分析性能指标以找到最佳的深度学习模型。此外，三个不同的硬件板，如JetsonNano开发板，树莓PI3和4被用作AI边缘计算设备，并实现了最佳的深度学习模型并评估了计算时间。结果表明，具有注意层的CNN-LSTM具有准确性，召回，精度和F1分数为97%，98%,98%和0.98，与其他深度学习模型相比更好。此外，与其他边缘计算设备相比，NVIDIAJetsonNano的计算时间更短。这项工作似乎具有很高的社会相关性，因为所提出的可穿戴设备可以用于监测老年人的活动并防止老年人跌倒，从而改善老年人的生活质量。
