'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
The use of deep learning for the activity recognition performed by wearables, such as smartwatches, is an understudied problem. To advance current understanding in this area, we perform a smartwatch-centric investigation of activity recognition under one of the most popular deep learning methods - Restricted Boltzmann Machines (RBM). This study includes a variety of typical behavior and context recognition tasks related to smartwatches (such as transportation mode, physical activities and indoor/outdoor detection) to which RBMs have previously never been applied. Our findings indicate that even a relatively simple RBM-based activity recognition pipeline is able to outperform a wide-range of common modeling alternatives for all tested activity classes. However, usage of deep models is also often accompanied by resource consumption that is unacceptably high for constrained devices like watches. Therefore, we complement this result with a study of the overhead of specifically RBM-based activity models on representative smartwatch hardware (the Snapdragon 400 SoC, present in many commercial smartwatches). These results show, contrary to expectation, RBM models for activity recognition have acceptable levels of resource use for smartwatch-class hardware already on the market. Collectively, these two experimental results make a strong case for more widespread adoption of deep learning techniques within smartwatch designs moving forward
