Name
Title | ||
|---|---|---|
Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs |
Abstract | ||
|---|---|---|
Human Activity Recognition (HAR) has become an increasingly popular task for embedded devices such as smartwatches. Most HAR systems for ultra-low power devices are based on classic Machine Learning (ML) models, whereas Deep Learning (DL), although reaching state-of-the-art accuracy, is less popular due to its high energy consumption, which poses a significant challenge for battery-operated and resource-constrained devices. In this work, we bridge the gap between on-device HAR and DL thanks to a hierarchical architecture composed of a decision tree (DT) and a one dimensional Convolutional Neural Network (ID CNN). The two classifiers operate in a cascaded fashion on two different sub-tasks: the DT classifies only the easiest activities, while the CNN deals with more complex ones. With experiments on a state-of-the-art dataset and targeting a single-core RISC-V MCU, we show that this approach allows to save up to 67.7% energy w.r.t. a “stand-alone” DL architecture at iso-accuracy. Additionally, the two-stage system either introduces a negligible memory overhead (up to 200 B) or on the contrary, reduces the total memory occupation. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/PRIME55000.2022.9816745 | 2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME) |
Keywords | DocType | ISBN |
Machine Learning,Adaptive Inference,Microcontrollers,Energy Efficiency | Conference | 978-1-6654-6701-8 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Daghero | 1 | 4 | 1.86 |
| Daniele Jahier Pagliari | 2 | 0 | 0.68 |
| Massimo Poncino | 3 | 0 | 0.34 |