Dynamic Fall Detection Using Graph-Based Spatial Temporal Convolution and Attention Network
نویسندگان
چکیده
The prevention of falls has become crucial in the modern healthcare domain and society for improving ageing supporting daily activities older people. Falling is mainly related to age health problems such as muscle, cardiovascular, locomotive syndrome weakness, etc. Among elderly people, number increasing every year, they can life-threatening if detected too late. Most time, people consume prescription medication after a fall and, Japanese community, suicide attempts due taking an overdose urgent. Many researchers have been working develop detection systems observe notify about real-time using handcrafted features machine learning approaches. Existing methods may face difficulties achieving satisfactory performance, limited robustness generality, high computational complexity, light illuminations, data orientation, camera view issues. We proposed graph-based spatial-temporal convolutional attention neural network (GSTCAN) with model overcome current challenges advanced medical technology system. system recently proven power its efficiency effectiveness various fields human activity recognition text tasks. In procedure, we first calculated motion along consecutive frame, then constructed graph applied spatial temporal extract contextual relationships among joints. Then, module selected channel-wise effective features. same repeat it six times GSTCAN fed network. Finally, softmax function classifier achieved accuracies 99.93%, 99.74%, 99.12% ImViA, UR-Fall, FDD datasets, respectively. high-performance accuracy three datasets proved system’s superiority, efficiency, generality.
منابع مشابه
Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition
Variations of human body skeletons may be considered as dynamic graphs, which are generic data representation for numerous real-world applications. In this paper, we propose a spatio-temporal graph convolution (STGC) approach for assembling the successes of local convolutional filtering and sequence learning ability of autoregressive moving average. To encode dynamic graphs, the constructed mul...
متن کاملdynamic coloring of graph
در این پایان نامه رنگ آمیزی دینامیکی یک گراف را بیان و مطالعه می کنیم. یک –kرنگ آمیزی سره ی رأسی گراف g را رنگ آمیزی دینامیکی می نامند اگر در همسایه های هر رأس v?v(g) با درجه ی حداقل 2، حداقل 2 رنگ متفاوت ظاهر شوند. کوچکترین عدد صحیح k، به طوری که g دارای –kرنگ آمیزی دینامیکی باشد را عدد رنگی دینامیکی g می نامند و آنرا با نماد ?_2 (g) نمایش می دهند. مونت گمری حدس زده است که تمام گراف های منتظم ...
15 صفحه اولSmoke Detection Using Spatial and Temporal Analyses
Video-based fire detection is currently a fairly common application with the growth in the number of installed surveillance video systems. Moreover, the related processing units are becoming more powerful. Smoke is an early sign of most fires; therefore, selecting an appropriate smoke-detection method is essential. However, detecting smoke without creating a false alarm remains a challenging pr...
متن کاملIdentifying Dynamic Network Modules with Temporal and Spatial Constraints
Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expressi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12153234