# Learning Attribute Representation for Human Activity Recognition

arXiv: https://arxiv.org/abs/1802.00761 · HTML: https://mlsanity.com/paper/1802.00761/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2018.

## Built on by (most cited first, 5 of 5)

- [Human Activity Recognition using Attribute-Based Neural Networks and Context Information](https://mlsanity.com/paper/2111.04564/) (2021)
- [Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches](https://mlsanity.com/paper/2212.05062/) (2022)
- [Scene Recognition with Objectness, Attribute and Category Learning](https://mlsanity.com/paper/2207.10174/) (2022)
- [Multi-Channel Time-Series Person and Soft-Biometric Identification](https://mlsanity.com/paper/2304.01585/) (2023)
- ["Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living](https://mlsanity.com/paper/2306.13149/) (2023)

## Abstract (arXiv)

Attribute representations became relevant in image recognition and word spotting, providing support under the presence of unbalance and disjoint datasets. However, for human activity recognition using sequential data from on-body sensors, human-labeled attributes are lacking. This paper introduces a search for attributes that represent favorably signal segments for recognizing human activities. It presents three deep architectures, including temporal-convolutions and an IMU centered design, for predicting attributes. An empiric evaluation of random and learned attribute representations, and as well as the networks is carried out on two datasets, outperforming the state-of-the art.