# Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors

arXiv: https://arxiv.org/abs/1808.01358 · HTML: https://mlsanity.com/paper/1808.01358/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Sensor-Augmented Egocentric-Video Captioning with Dynamic Modal Attention](https://mlsanity.com/paper/2109.02955/) (2021)
- [Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review](https://mlsanity.com/paper/2403.15422/) (2024)
- [Leveraging Foundation Models for Zero-Shot IoT Sensing](https://mlsanity.com/paper/2407.19893/) (2024)
- [Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies](https://mlsanity.com/paper/2205.10778/) (2022)

## Abstract (arXiv)

This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventional ZSL methods have equally dealt with all the available attributes, but this sometimes causes misclassification. This is because an attribute that is effective for classifying instances of one class is not always effective for another class. In this case, a metric of classifying the latter class can be undesirably influenced by the irrelevant attribute. This paper solves this problem by taking the importance of each attribute for each class into account when calculating the metric. In addition to the proposal of this new method, this paper also contributes by providing a dataset for pose classification based on wearable sensors, named HDPoseDS. It contains 22 classes of poses performed by 10 subjects with 31 IMU sensors across full body. To the best of our knowledge, it is the richest wearable-sensor dataset especially in terms of sensor density, and thus it is suitable for studying zero-shot pose/action recognition. The presented method was evaluated on HDPoseDS and outperformed relative improvement of 5.9% in comparison to the best baseline method.