# Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices

arXiv: https://arxiv.org/abs/2402.03714 · HTML: https://mlsanity.com/paper/2402.03714/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015)
- [MobileOne: An Improved One millisecond Mobile Backbone](https://mlsanity.com/paper/2206.04040/) (2022)
- [Multi-task Self-Supervised Learning for Human Activity Detection](https://mlsanity.com/paper/1907.11879/) (2019)
- [Optimal Feature Transport for Cross-View Image Geo-Localization](https://mlsanity.com/paper/1907.05021/) (2019)
- [DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing](https://mlsanity.com/paper/1611.01942/) (2016)
- [Optimal Transport for Multi-source Domain Adaptation under Target Shift](https://mlsanity.com/paper/1803.04899/) (2018)
- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [Data augmentation using synthetic data for time series classification with deep residual networks](https://mlsanity.com/paper/1808.02455/) (2018)
- [Cross-domain Activity Recognition via Substructural Optimal Transport](https://mlsanity.com/paper/2102.03353/) (2021)
- [SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation](https://mlsanity.com/paper/1701.08886/) (2017)
- [Enabling hand gesture customization on wrist-worn devices](https://mlsanity.com/paper/2203.15239/) (2022)
- [Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition](https://mlsanity.com/paper/2210.01459/) (2022)
- [A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors](https://mlsanity.com/paper/2003.01874/) (2020)
- [Feature Selection for Unsupervised Domain Adaptation using Optimal Transport](https://mlsanity.com/paper/1806.10861/) (2018)

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

- [Scaling Wearable Foundation Models](https://mlsanity.com/paper/2410.13638/) (2024)
- [IMUCoCo: Enabling Flexible On-Body IMU Placement for Human Pose Estimation and Activity Recognition](https://mlsanity.com/paper/2508.01894/) (2025)
- [What Egocentric Video Embeddings Encode and How to Combine Them with Inertial Sensors for Human Activity Recognition](https://mlsanity.com/paper/2610.12926/) (2026)

## Abstract (arXiv)

Wearable sensors have permeated into people's lives, ushering impactful applications in interactive systems and activity recognition. However, practitioners face significant obstacles when dealing with sensing heterogeneities, requiring custom models for different platforms. In this paper, we conduct a comprehensive evaluation of the generalizability of motion models across sensor locations. Our analysis highlights this challenge and identifies key on-body locations for building location-invariant models that can be integrated on any device. For this, we introduce the largest multi-location activity dataset (N=50, 200 cumulative hours), which we make publicly available. We also present deployable on-device motion models reaching 91.41% frame-level F1-score from a single model irrespective of sensor placements. Lastly, we investigate cross-location data synthesis, aiming to alleviate the laborious data collection tasks by synthesizing data in one location given data from another. These contributions advance our vision of low-barrier, location-invariant activity recognition systems, catalyzing research in HCI and ubiquitous computing.