Abstract
Human activity recognition (HAR) based on IMU sensors is an essential domain in ubiquitous computing. Because of the improving trend to deploy artificial intelligence into IoT devices or smartphones, more researchers design the HAR models for embedded devices. We propose a plug-and-play HAR modeling pipeline with multi-level distillation to build deep convolutional HAR models with native support of embedded devices. SMLDist consists of stage distillation, memory distillation, and logits distillation, which covers all the information flow of the deep models. Stage distillation constrains the learning direction of the intermediate features. Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks. Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target. We compare the performance of accuracy, F1 macro score, and energy cost on the embedded platform of various state-of-the-art HAR frameworks with a MobileNet V3 model built by SMLDist. The produced model has well balance with robustness, efficiency, and accuracy. SMLDist can also compress the models with minor performance loss in an equal compression rate than other state-of-the-art knowledge distillation methods on seven public datasets.
Abstract (translated)
URL
https://arxiv.org/abs/2107.07331
