# SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks

arXiv: https://arxiv.org/abs/1906.02399 · HTML: https://mlsanity.com/paper/1906.02399/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [SSMF: Shifting Seasonal Matrix Factorization](https://mlsanity.com/paper/2110.12763/) (2021)
- [Advances in Set Function Learning: A Survey of Techniques and Applications](https://mlsanity.com/paper/2501.14991/) (2025)
- [Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2202.12949/) (2022)
- [A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation](https://mlsanity.com/paper/2107.07331/) (2021)
- [DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness](https://mlsanity.com/paper/2209.15415/) (2022)
- [Community Learning: Understanding A Community Through NLP for Positive Impact](https://mlsanity.com/paper/2210.00590/) (2022)

## Abstract (arXiv)

Batteryless or so called passive wearables are providing new and innovative methods for human activity recognition (HAR), especially in healthcare applications for older people. Passive sensors are low cost, lightweight, unobtrusive and desirably disposable; attractive attributes for healthcare applications in hospitals and nursing homes. Despite the compelling propositions for sensing applications, the data streams from these sensors are characterised by high sparsity---the time intervals between sensor readings are irregular while the number of readings per unit time are often limited. In this paper, we rigorously explore the problem of learning activity recognition models from temporally sparse data. We describe how to learn directly from sparse data using a deep learning paradigm in an end-to-end manner. We demonstrate significant classification performance improvements on real-world passive sensor datasets from older people over the state-of-the-art deep learning human activity recognition models. Further, we provide insights into the model's behaviour through complementary experiments on a benchmark dataset and visualisation of the learned activity feature spaces.