In order to improve the accuracy and automation of daily activity recognition and reduce manual intervention, a deep neural network based on wearable sensor signals was proposed for human activity recognition. This paper aims to identify and predict human fall behavior based on wearable inertial sensors. Selecting different device locations can have a significant impact on the efficiency of data collection. In this study, the data obtained by using sensors connected to the lower extremities should be much more effective than data obtained through the arms or other parts of the body when collecting exercise data related to the running behavior of the elderly. In addition to considering the validity of the data, we also need to consider how the data is related to the real situation and how comfortable the user can wear it in real life. Experimental data show that in order to obtain data sets that are easy to calculate, accurate and effective, two aspects need to be considered: the location of data collection equipment and the frequency of data collection. The experimental results show that the recognition accuracy is up to 93.7% after using the training decision tree algorithm with 562 features selected specifically for activity recognition. Then, the extracted features are trained by decision tree algorithm, and the recognition accuracy is only 82.8%. Finally, lSTM-RNN was used to directly train the original sensor data, and the accuracy of behavioral activity recognition in the elderly could reach 92.28%. In the aspect of behavior recognition, the traditional work mainly focuses on the identification of simple behaviors performed by a single person successively. But in real life, human behavior is complex, and applications often require high real-time recognition results.

Xianmin Ma. 5G Virtual Reality Internet of Things - Research on Human Fall Behavior Recognition and Prediction Based on Wearable Inertial Sensors. International Journal of Multimedia Computing (2022), Vol. 3, Issue 2: 29-41. https://doi.org/10.38007/IJMC.2022.030205.

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