# Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks

arXiv: https://arxiv.org/abs/2209.00839 · HTML: https://mlsanity.com/paper/2209.00839/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 58 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [PACT: Parameterized Clipping Activation for Quantized Neural Networks](https://mlsanity.com/paper/1805.06085/) (2018)
- [HAQ: Hardware-Aware Automated Quantization with Mixed Precision](https://mlsanity.com/paper/1811.08886/) (2018)
- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [Slimmable Neural Networks](https://mlsanity.com/paper/1812.08928/) (2018)
- [FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions](https://mlsanity.com/paper/2004.05565/) (2020)
- [Dynamic Channel Pruning: Feature Boosting and Suppression](https://mlsanity.com/paper/1810.05331/) (2018)
- [Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables](https://mlsanity.com/paper/1604.08880/) (2016)
- [MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks](https://mlsanity.com/paper/1711.06798/) (2017)
- [Rethinking Differentiable Search for Mixed-Precision Neural Networks](https://mlsanity.com/paper/2004.05795/) (2020)
- [UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones](https://mlsanity.com/paper/1611.07688/) (2016)
- [Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off](https://mlsanity.com/paper/1607.05418/) (2016)
- [Enabling Mixed-Precision Quantized Neural Networks in Extreme-Edge Devices](https://mlsanity.com/paper/2007.07759/) (2020)
- [A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices](https://mlsanity.com/paper/1909.12917/) (2019)
- [Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors](https://mlsanity.com/paper/2005.03948/) (2020)
- [Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors](https://mlsanity.com/paper/2205.12781/) (2022)
- [Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node](https://mlsanity.com/paper/2007.08952/) (2020)
- [TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference](https://mlsanity.com/paper/2203.12925/) (2022)
- [Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks](https://mlsanity.com/paper/2203.14768/) (2022)

## Built on by (most cited first, 6 of 6)

- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones](https://mlsanity.com/paper/2401.15236/) (2024)
- [Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes](https://mlsanity.com/paper/2306.09789/) (2023)
- [Low-Energy On-Device Personalization for MCUs](https://mlsanity.com/paper/2403.08040/) (2024)
- [γ-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition](https://mlsanity.com/paper/2509.22448/) (2025)
- [DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers](https://mlsanity.com/paper/2403.09035/) (2024)

## Abstract (arXiv)

Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on embedded devices, from smartphones to ultra low-power sensors. Due to the high computational complexity of deep learning models, most embedded HAR systems are based on simple and not-so-accurate classic machine learning algorithms. This work bridges the gap between on-device HAR and deep learning, proposing a set of efficient one-dimensional Convolutional Neural Networks (CNNs) deployable on general purpose microcontrollers (MCUs). Our CNNs are obtained combining hyper-parameters optimization with sub-byte and mixed-precision quantization, to find good trade-offs between classification results and memory occupation. Moreover, we also leverage adaptive inference as an orthogonal optimization to tune the inference complexity at runtime based on the processed input, hence producing a more flexible HAR system. With experiments on four datasets, and targeting an ultra-low-power RISC-V MCU, we show that (i) We are able to obtain a rich set of Pareto-optimal CNNs for HAR, spanning more than 1 order of magnitude in terms of memory, latency and energy consumption; (ii) Thanks to adaptive inference, we can derive >20 runtime operating modes starting from a single CNN, differing by up to 10% in classification scores and by more than 3x in inference complexity, with a limited memory overhead; (iii) on three of the four benchmarks, we outperform all previous deep learning methods, reducing the memory occupation by more than 100x. The few methods that obtain better performance (both shallow and deep) are not compatible with MCU deployment. (iv) All our CNNs are compatible with real-time on-device HAR with an inference latency <16ms. Their memory occupation varies in 0.05-23.17 kB, and their energy consumption in 0.005 and 61.59 uJ, allowing years of continuous operation on a small battery supply.