Markov transition field for fall detection using time-series data

Kalbermatter, Rebeca B.; Silva, Felipe G.; Pereira, Ana I.; Valente, António; Lima, José; Yahiaoui, Réda; Fayad, Moustafa

http://hdl.handle.net/10198/36870

Utilize este identificador para referenciar este registo.

Nome:	Descrição:	Tamanho:	Formato:
Markov Transition.pdf		1.27 MB	Adobe PDF	Ver/Abrir

Contacte-nos

Autores

Kalbermatter, Rebeca B.

Resumo(s)

Fall detection systems have traditionally relied on sequential pattern recognition methods, using, for example, time series data obtained from inertial sensors, such as accelerometers. This paper proposes a methodology for fall detection based on converting time series from accelerometer sensors into visual representations using the Markov Transition Field (MTF) method. The UP-Fall dataset was used to test the performance of a Convolutional Neural Network (CNN) model trained on the MTF images generated. A systematic analysis of the image generation parameters was carried out, including the window size, the percentage of overlap, and the number of bins used in the discretizations. The experiments showed that the configuration with 55 bins, a window of 200 samples, and 40% overlap resulted in the best accuracy (97.13%), demonstrating that the conversion of sensory signals into MTF images is a promising alternative for fall detection, allowing computer vision models to capture relevant temporal patterns with high efficiency.

Palavras-chave

Fall detection Markov transition field Convolutional neural network Time series segmentation Sensor data

URI

http://hdl.handle.net/10198/36870

Citação

Kalbermatter, Rebeca B.; Silva, Felipe G.; Pereira, Ana I.; Valente, António; Lima, José; Yahiaoui, Réda; Fayad, Moustafa (2025). Markov transition field for fall detection using time-series data. In IEEE International Conference on E-health Networking, Applications and Services, IEEE HealthCom 2025. IEEE. p. 1-6. ISBN 979-833150989-7