# Personalized Step Counting Using Wearable Sensors: A Domain Adapted LSTM Network Approach

arXiv: https://arxiv.org/abs/2012.08975 · HTML: https://mlsanity.com/paper/2012.08975/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Count What You Want: Exemplar Identification and Few-shot Counting of Human Actions in the Wild](https://mlsanity.com/paper/2312.17330/) (2023)
- [Step Counting with Attention-based LSTM](https://mlsanity.com/paper/2211.13114/) (2022)
- [IMUVIE: Pickup Timeline Action Localization via Motion Movies](https://mlsanity.com/paper/2411.12689/) (2024)

## Abstract (arXiv)

Activity monitors are widely used to measure various physical activities (PA) as an indicator of mobility, fitness and general health. Similarly, real-time monitoring of longitudinal trends in step count has significant clinical potential as a personalized measure of disease related changes in daily activity. However, inconsistent step count accuracy across vendors, body locations, and individual gait differences limits clinical utility. The tri-axial accelerometer inside PA monitors can be exploited to improve step count accuracy across devices and individuals. In this study, we hypothesize: (1) raw tri-axial sensor data can be modeled to create reliable and accurate step count, and (2) a generalized step count model can then be efficiently adapted to each unique gait pattern using very little new data. Firstly, open-source raw sensor data was used to construct a long short term memory (LSTM) deep neural network to model step count. Then we generated a new, fully independent data set using a different device and different subjects. Finally, a small amount of subject-specific data was domain adapted to produce personalized models with high individualized step count accuracy. These results suggest models trained using large freely available datasets can be adapted to patient populations where large historical data sets are rare.