# Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors

## TL;DR

This paper introduces an attention-based CNN that improves human activity recognition from weakly labeled wearable sensor data by focusing on relevant signals and identifying activity locations, reducing annotation effort.

## Contribution

The novel attention model effectively isolates labeled activities within long sensor sequences and enhances recognition accuracy with weak labels.

## Key findings

- Outperforms classical deep learning methods in accuracy
- Identifies specific activity locations in weakly labeled data
- Facilitates easier sensor data annotation

## Abstract

Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a time-consuming process. However, traditional methods of human activity recognition require a large amount of such strictly labeled data for training classifiers. In this paper, we present an attention-based convolutional neural network for human recognition from weakly labeled data. The proposed attention model can focus on labeled activity among a long sequence of sensor data, and while filter out a large amount of background noise signals. In experiment on the weakly labeled dataset, we show that our attention model outperforms classical deep learning methods in accuracy. Besides, we determine the specific locations of the labeled activity in a long sequence of weakly labeled data by converting the compatibility score which is generated from attention model to compatibility density. Our method greatly facilitates the process of sensor data annotation, and makes data collection more easy.