# iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition

arXiv: https://arxiv.org/abs/2402.09445 · HTML: https://mlsanity.com/paper/2402.09445/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Exploring Contrastive Learning in Human Activity Recognition for Healthcare](https://mlsanity.com/paper/2011.11542/) (2020)
- [Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition](https://mlsanity.com/paper/2210.01459/) (2022)
- [More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors](https://mlsanity.com/paper/2202.08267/) (2022)

## Built on by (most cited first, 9 of 9)

- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets](https://mlsanity.com/paper/2412.09037/) (2024)
- [HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2512.10807/) (2025)
- [Initial Findings on Sensor based Open Vocabulary Activity Recognition via Text Embedding Inversion](https://mlsanity.com/paper/2501.07408/) (2025)
- [CODA: A Continuous Online Evolve Framework for Deploying HAR Sensing Systems](https://mlsanity.com/paper/2403.14922/) (2024)
- [TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition](https://mlsanity.com/paper/2505.02052/) (2025)
- [ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recogntion](https://mlsanity.com/paper/2408.09527/) (2024)
- [SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models](https://mlsanity.com/paper/2507.06405/) (2025)
- [From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation](https://mlsanity.com/paper/2507.12884/) (2025)

## Abstract (arXiv)

Automatic and precise fitness activity recognition can be beneficial in aspects from promoting a healthy lifestyle to personalized preventative healthcare. While IMUs are currently the prominent fitness tracking modality, through iMove, we show bio-impedence can help improve IMU-based fitness tracking through sensor fusion and contrastive learning.To evaluate our methods, we conducted an experiment including six upper body fitness activities performed by ten subjects over five days to collect synchronized data from bio-impedance across two wrists and IMU on the left wrist.The contrastive learning framework uses the two modalities to train a better IMU-only classification model, where bio-impedance is only required at the training phase, by which the average Macro F1 score with the input of a single IMU was improved by 3.22 % reaching 84.71 % compared to the 81.49 % of the IMU baseline model. We have also shown how bio-impedance can improve human activity recognition (HAR) directly through sensor fusion, reaching an average Macro F1 score of 89.57 % (two modalities required for both training and inference) even if Bio-impedance alone has an average macro F1 score of 75.36 %, which is outperformed by IMU alone. In addition, similar results were obtained in an extended study on lower body fitness activity classification, demonstrating the generalisability of our approach.Our findings underscore the potential of sensor fusion and contrastive learning as valuable tools for advancing fitness activity recognition, with bio-impedance playing a pivotal role in augmenting the capabilities of IMU-based systems.