Estimating intra-subject and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches

Oxygen consumption is an important parameter for exercise test, such as walking and running, that can be measured using portable spirometers or metabolic analyzers. However, these devices are not feasible for regular use by consumers as they intervene with the user’s physical integrity, and are expensive and difficult to operate. To circumvent these drawbacks, indirect estimation of using neural networks combined with motion parameters and heart rate measurements collected with consumer-grade sensors has been shown to yield reasonably accurate for intra-subject estimation. However, estimating with neural networks trained with data from other individuals than the user, known as inter-subject estimation, remains an open problem. In this paper, five types of neural network were tested in various configurations for inter-subject estimation. To analyse predictive performance, data from 16 participants walking and running at speeds between 1.0 m/s and 3.3 m/s were used. The most promising approach was XceptionNet, which in most configurations even yielded a lower average estimation error than the LSTM neural network from an earlier study for intra-subject estimation. This suggests that XceptionNet could be embedded in portable devices for real-time estimation of oxygen consumption during walking and running.