# Human Activity Recognition with Convolutional Neural Netowrks

arXiv: https://arxiv.org/abs/1906.01935 · HTML: https://mlsanity.com/paper/1906.01935/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark](https://mlsanity.com/paper/2101.01665/) (2021)
- [Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models](https://mlsanity.com/paper/2310.03512/) (2023)
- [DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage Temporal Convolutional Network](https://mlsanity.com/paper/2402.02910/) (2024)
- [Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques](https://mlsanity.com/paper/2103.16490/) (2021)
- [Description of Structural Biases and Associated Data in Sensor-Rich Environments](https://mlsanity.com/paper/2104.04885/) (2021)
- [Privacy in Multimodal Federated Human Activity Recognition](https://mlsanity.com/paper/2305.12134/) (2023)
- [A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition](https://mlsanity.com/paper/2405.12711/) (2024)
- [C-AAE: Compressively Anonymizing Autoencoders for Privacy-Preserving Activity Recognition in Healthcare Sensor Streams](https://mlsanity.com/paper/2507.18072/) (2025)

## Abstract (arXiv)

The problem of automatic identification of physical activities performed by human subjects is referred to as Human Activity Recognition (HAR). There exist several techniques to measure motion characteristics during these physical activities, such as Inertial Measurement Units (IMUs). IMUs have a cornerstone position in this context, and are characterized by usage flexibility, low cost, and reduced privacy impact. With the use of inertial sensors, it is possible to sample some measures such as acceleration and angular velocity of a body, and use them to learn models that are capable of correctly classifying activities to their corresponding classes. In this paper, we propose to use Convolutional Neural Networks (CNNs) to classify human activities. Our models use raw data obtained from a set of inertial sensors. We explore several combinations of activities and sensors, showing how motion signals can be adapted to be fed into CNNs by using different network architectures. We also compare the performance of different groups of sensors, investigating the classification potential of single, double and triple sensor systems. The experimental results obtained on a dataset of 16 lower-limb activities, collected from a group of participants with the use of five different sensors, are very promising.