2026-05-30T05:04:48Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/174002026-02-03T12:32:32Zcom_10630_2254col_10630_37959

Casilari-Pérez, Eduardo Lora Rivera, Raúl García-Lagos, Francisco 2019-03-04T07:34:14Z 2019-03-04T07:34:14Z 2019-03-04 https://hdl.handle.net/10630/17400 Owing to the effects of falls on quality of life of the elderly, automatic fall detection systems (FDS) have become a key research topic in the ambit of telecare. This works assesses the performance of convolutional neural networks when they are applied to identify fall accidents in a wearable FDS provided with a tri-axial accelerometer. The evaluation of the detection algorithm is carried out by employing a benchmarking repository with a wide set of traces captured from a wide group of volunteers that executed a programmed series of Activities of the Daily Living (ADLs) and emulated falls. Results show that the CNN can properly distinguish both types of movements with a success rate (specificity and sensitivity) around 99%. eng open access Redes Neuronales (Informática) Evaluation of a Fall Alerting System based on a Convolutional Deep Neural Network conference output