Titlebook: Challenges and Trends in Multimodal Fall Detection for Healthcare; Hiram Ponce,Lourdes Martínez-Villase?or,Ernesto Mo Book 2020 Springer N

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https://doi.org/10.1007/978-3-8350-9117-7t role, because fall is the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. The three basic approaches used to develop fall detection systems include some sort of wearable, ambient or non-invasive based devices. Most of such syste

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Challenges and Trends in Multimodal Fall Detection for Healthcare978-3-030-38748-8Series ISSN 2198-4182 Series E-ISSN 2198-4190

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https://doi.org/10.1007/978-3-030-38748-8Fall Detection; Fall Classification; Human Fall Detection; Fall Detection data Set; Intelligent Real-Tim

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978-3-030-38750-1Springer Nature Switzerland AG 2020

說(shuō)笑

D. B. Hoyt,W. G. Junger,A. N. Ozkans located at the ankle, right pocket, belt, and neck of the subject. We utilize a grid search technique to evaluate variations of the Bi-LSTM model and identify a configuration that presents the best results. The best Bi-LSTM model achieved good results for precision and f1-score, 43.30 and 38.50%, respectively.

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Psycho- und soziosomatische Konzeptedentifying falls obtained results that surpassed the other models submitted to the test. They were successful in extracting various information from a highly sophisticated and incredibly dimensional dataset to help professionals from various areas expand their investigations in the field of falling people.

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Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Tes located at the ankle, right pocket, belt, and neck of the subject. We utilize a grid search technique to evaluate variations of the Bi-LSTM model and identify a configuration that presents the best results. The best Bi-LSTM model achieved good results for precision and f1-score, 43.30 and 38.50%, respectively.