# TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices

arXiv: https://arxiv.org/abs/2507.07949 · HTML: https://mlsanity.com/paper/2507.07949/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://mlsanity.com/paper/2103.10697/) (2021)
- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware](https://mlsanity.com/paper/2312.00425/) (2023)
- [Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition](https://mlsanity.com/paper/2310.14390/) (2023)
- [Non-contact, real-time eye blink detection with capacitive sensing](https://mlsanity.com/paper/2211.05541/) (2022)
- [Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets](https://mlsanity.com/paper/2412.09037/) (2024)
- [Deep Adaptive Temporal Pooling for Activity Recognition](https://mlsanity.com/paper/1808.07272/) (2018)
- [On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface](https://mlsanity.com/paper/2409.00083/) (2024)
- [Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study](https://mlsanity.com/paper/2501.15330/) (2025)
- [Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification](https://mlsanity.com/paper/2503.06311/) (2025)
- [CycloWatt: An Affordable, TinyML-enhanced IoT Device Revolutionizing Cycling Power Metrics](https://mlsanity.com/paper/2403.07915/) (2024)

## Built on by (most cited first, 8 of 8)

- [MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices](https://mlsanity.com/paper/2602.06523/) (2026)
- [BabyMamba-HAR: Lightweight Selective State Space Models for Efficient Human Activity Recognition on Resource Constrained Devices](https://mlsanity.com/paper/2602.09872/) (2026)
- [SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition](https://mlsanity.com/paper/2603.26482/) (2026)
- [BayaHAR: Lightweight Bayesian Few-Shot User Adaptation for On-Device Personalized Human Activity Recognition](https://mlsanity.com/paper/2606.04798/) (2026)
- [WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition](https://mlsanity.com/paper/2606.13194/) (2026)
- [A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset](https://mlsanity.com/paper/2606.27886/) (2026)
- [LITEWAY: LIghtweight HAR via Temporal Efficient highWAY](https://mlsanity.com/paper/2608.09421/) (2026)
- [You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition](https://mlsanity.com/paper/2609.16065/) (2026)

## Abstract (arXiv)

Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces TinierHAR, an ultra-lightweight deep learning architecture that synergizes residual depthwise separable convolutions, gated recurrent units (GRUs), and temporal aggregation to achieve SOTA efficiency without compromising performance. Evaluated across 14 public HAR datasets, TinierHAR reduces Parameters by 2.7x (vs. TinyHAR) and 43.3x (vs. DeepConvLSTM), and MACs by 6.4x and 58.6x, respectively, while maintaining the averaged F1-scores. Beyond quantitative gains, this work provides the first systematic ablation study dissecting the contributions of spatial-temporal components across proposed TinierHAR, prior SOTA TinyHAR, and the classical DeepConvLSTM, offering actionable insights for designing efficient HAR systems. We finally discussed the findings and suggested principled design guidelines for future efficient HAR. To catalyze edge-HAR research, we open-source all materials in this work for future benchmarkingfootnotehttps://github.com/zhaxidele/TinierHAR