  In this study, we propose a method for single sensor-based activity
recognition, trained with data from multiple sensors. There is no doubt that
the performance of complex activity recognition systems increases when we use
enough sensors with sufficient quality, however using such rich sensors may not
be feasible in real-life situations for various reasons such as user comfort,
privacy, battery-preservation, and/or costs. In many cases, only one device
such as a smartphone is available, and it is challenging to achieve high
accuracy with a single sensor, more so for complex activities. Our method
combines representation learning with feature mapping to leverage multiple
sensor information made available during training while using a single sensor
during testing or in real usage. Our results show that the proposed approach
can improve the F1-score of the complex activity recognition by up to 17\%
compared to that in training while utilizing the same sensor data in a new user
scenario.
