A data-driven approach to detect upper limb functional use during daily life in breast cancer survivors using wrist-worn sensors.

ABSTRACT: To gain insights into the impact of upper limb (UL) dysfunctions after breast cancer treatment, this study aimed to develop a temporal convolutional neural network (TCN) to detect functional daily UL use in breast cancer survivors using data from a wrist-worn accelerometer. A pre-existing dataset of 10 breast cancer survivors was used that contained raw 3-axis acceleration data and simultaneously recorded video data, captured during four daily life activities. The input of our TCN consists of a 3-axis acceleration sequence with a receptive field of 243 samples. The 4 ResNet TCN blocks perform dilated temporal convolutions with a kernel of size 3 and a dilation rate that increases by a factor of 3 after each iteration. Outcomes of interest were functional UL use (minutes) and percentage UL

SUBMITTER: Emmerzaal J 

PROVIDER: S-EPMC11303700 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature