# Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput

arXiv: https://arxiv.org/abs/1611.03607 · HTML: https://mlsanity.com/paper/1611.03607/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2016.

## Built on by (most cited first, 10 of 10)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Efficient Deep Clustering of Human Activities and How to Improve Evaluation](https://mlsanity.com/paper/2209.08335/) (2022)
- [Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition](https://mlsanity.com/paper/2309.01262/) (2023)
- [Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2202.12949/) (2022)
- [Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning](https://mlsanity.com/paper/2304.10068/) (2023)
- [From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes](https://mlsanity.com/paper/2503.06525/) (2025)
- [Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification](https://mlsanity.com/paper/2112.04572/) (2021)
- [HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data](https://mlsanity.com/paper/2203.03087/) (2022)
- [In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models against Variability](https://mlsanity.com/paper/2503.11466/) (2025)
- [BasketHAR: A Multimodal Dataset for Human Activity Recognition and Sport Analysis in Basketball Training Scenarios](https://mlsanity.com/paper/2604.17065/) (2026)

## Abstract (arXiv)

In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The "high throughput" refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition rate was 95.42% and 83.43% against the test data of 108 segmented trials each of which has single activity class and 18 multiple sequential trials, respectively. Here, the maximum recognition rates by traditional methods were 71.65% and 54.97% for each. In addition, the efficiency of the found parameters was evaluated by using additional dataset. Further, as for throughput of the recognition per unit time, the constructed DRNN was requiring only 1.347 [ms], while the best traditional method required 11.031 [ms] which includes 11.027 [ms] for feature calculation. These advantages are caused by the compact and small architecture of the constructed real time oriented DRNN.