# Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables

arXiv: https://arxiv.org/abs/1811.08127 · HTML: https://mlsanity.com/paper/1811.08127/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition](https://mlsanity.com/paper/2110.12163/) (2021)
- [TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation](https://mlsanity.com/paper/2209.09092/) (2022)
- [Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition](https://mlsanity.com/paper/2306.01108/) (2023)
- [Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition](https://mlsanity.com/paper/2211.06173/) (2022)
- [A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation](https://mlsanity.com/paper/2303.11100/) (2023)
- [P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer](https://mlsanity.com/paper/2403.08214/) (2024)
- [Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables](https://mlsanity.com/paper/2110.05732/) (2021)
- [Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR](https://mlsanity.com/paper/2207.08187/) (2022)
- [Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning](https://mlsanity.com/paper/2409.17392/) (2024)
- [HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data](https://mlsanity.com/paper/2203.03087/) (2022)
- [Sequential Contextual Fit Predicts Human Behavioural and Neural Dynamics Across Domains](https://mlsanity.com/paper/2609.20179/) (2026)

## Abstract (arXiv)

Automatic recognition of human activities from time-series sensor data (referred to as HAR) is a growing area of research in ubiquitous computing. Most recent research in the field adopts supervised deep learning paradigms to automate extraction of intrinsic features from raw signal inputs and addresses HAR as a multi-class classification problem where detecting a single activity class within the duration of a sensory data segment suffices. However, due to the innate diversity of human activities and their corresponding duration, no data segment is guaranteed to contain sensor recordings of a single activity type. In this paper, we express HAR more naturally as a set prediction problem where the predictions are sets of ongoing activity elements with unfixed and unknown cardinality. For the first time, we address this problem by presenting a novel HAR approach that learns to output activity sets using deep neural networks. Moreover, motivated by the limited availability of annotated HAR datasets as well as the unfortunate immaturity of existing unsupervised systems, we complement our supervised set learning scheme with a prior unsupervised feature learning process that adopts convolutional auto-encoders to exploit unlabeled data. The empirical experiments on two widely adopted HAR datasets demonstrate the substantial improvement of our proposed methodology over the baseline models.