Distributed Convolutional Neural Networks for Human Activity Recognition in Wearable Robotics

We investigate distributing convolutional neural networks (CNNs) for human activity recognition across computing nodes collocated with sensors at specific regions (body, arms and legs) on the wearer. We compare four CNN architectures. A distributed CNN is implemented on a network of Intel Edison nodes, demonstrating the capability of performing real-time classification. Two use a centralized, monolithic approach, and two are distributed across a number of computing nodes. While the accuracy of the distributed approaches are slightly worse than those of the monolithic CNNs, exploiting the hierarchy of the problem turns out to require much less memory-and therefore computation-than the monolithic CNNs, and only modest communication rates between nodes in the model, making the approach viable for a wide range of distributed systems ranging from wearable robots to multi-robot swarms.