.png){kind=icon}
返回
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition
.png){kind=icon}
PRE
AI
DOI：10.1145/3517246.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is an expensive task, unsupervised and semi-supervised learning techniques have emerged that can learn good features from the data without requiring any labels. In this paper, we extend this line of research and present a novel technique called Collaborative Self-Supervised Learning (ColloSSL) which leverages unlabeled data collected from multiple devices worn by a user to learn high-quality features of the data. A key insight that underpins the design of ColloSSL is that unlabeled sensor datasets simultaneously captured by multiple devices can be viewed as natural transformations of each other, and leveraged to generate a supervisory signal for representation learning. We present three technical innovations to extend conventional self-supervised learning algorithms to a multi-device setting: a Device Selection approach which selects positive and negative devices to enable contrastive learning, a Contrastive Sampling algorithm which samples positive and negative examples in a multi-device setting, and a loss function called Multi-view Contrastive Loss which extends standard contrastive loss to a multi-device setting. Our experimental results on three multi-device datasets show that ColloSSL outperforms both fully-supervised and semi-supervised learning techniques in majority of the experiment settings, resulting in an absolute increase of upto 7.9% in F-1 score compared to the best performing baselines. We also show that ColloSSL outperforms the fully-supervised methods in a low-data regime, by just using one-tenth of the available labeled data in the best case.
Keyword:
Human Activity Recognition
Self-Supervised learning
Contrastive Learning
期刊
P
IF:
4.5
论文数:
1.1K
被引数:
7.2K
机构
引用论文
.png){kind=icon}
Applying Machine Learning for Sensor Data Analysis in Interactive Systems: Common Pitfalls of Pragmatic Use and Ways to Avoid Them将机器学习应用于交互式系统中的传感器数据分析: 实用使用的常见陷阱和避免方法
PREAI
分享收藏A Tutorial on Human Activity Recognition Using Body-Worn Inertial Sensors使用身体佩戴的惯性传感器进行人体活动识别的教程
PREAI
分享收藏
查看更多内容