# Recognizing Detailed Human Context In-the-Wild from Smartphones and Smartwatches

arXiv: https://arxiv.org/abs/1609.06354 · HTML: https://mlsanity.com/paper/1609.06354/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2016.

## Built on by (most cited first, 30 of 42)

- [NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation](https://mlsanity.com/paper/2208.05117/) (2022)
- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [FedStack: Personalized activity monitoring using stacked federated learning](https://mlsanity.com/paper/2209.13080/) (2022)
- [SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions](https://mlsanity.com/paper/2502.02883/) (2025)
- [Stop&Hop: Early Classification of Irregular Time Series](https://mlsanity.com/paper/2208.09795/) (2022)
- [Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity](https://mlsanity.com/paper/2306.13735/) (2023)
- [TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computers](https://mlsanity.com/paper/2412.15304/) (2024)
- [Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning](https://mlsanity.com/paper/2201.08565/) (2022)
- [SensorQA: A Question Answering Benchmark for Daily-Life Monitoring](https://mlsanity.com/paper/2501.04974/) (2025)
- [CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator](https://mlsanity.com/paper/2202.10562/) (2022)
- [Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework](https://mlsanity.com/paper/2301.00489/) (2023)
- [(Un)fair devices: Moving beyond AI accuracy in personal sensing](https://mlsanity.com/paper/2303.15585/) (2023)
- [Scaling laws in wearable human activity recognition](https://mlsanity.com/paper/2502.03364/) (2025)
- [WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch](https://mlsanity.com/paper/2502.05783/) (2025)
- [SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces](https://mlsanity.com/paper/2604.14705/) (2026)
- [Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition](https://mlsanity.com/paper/2409.18481/) (2024)
- [WatchHAR: Real-time On-device Human Activity Recognition System for Smartwatches](https://mlsanity.com/paper/2509.04736/) (2025)
- [Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks](https://mlsanity.com/paper/2302.04052/) (2023)
- [Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition](https://mlsanity.com/paper/2409.17483/) (2024)
- [VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition](https://mlsanity.com/paper/2204.00130/) (2022)
- [UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventions](https://mlsanity.com/paper/2212.14731/) (2022)
- [Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia](https://mlsanity.com/paper/2302.03224/) (2023)
- [COMPASS: Unsupervised and Online Clustering of Complex Human Activities from Smartphone Sensors](https://mlsanity.com/paper/2306.15437/) (2023)
- [CRoP: Context-wise Robust Static Human-Sensing Personalization](https://mlsanity.com/paper/2409.17994/) (2024)
- [Adaptive Client Selection with Personalization for Communication Efficient Federated Learning](https://mlsanity.com/paper/2411.17833/) (2024)
- [WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition](https://mlsanity.com/paper/2508.16604/) (2025)
- [HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data](https://mlsanity.com/paper/2203.03087/) (2022)
- [On-device modeling of user's social context and familiar places from smartphone-embedded sensor data](https://mlsanity.com/paper/2205.08790/) (2022)
- [Predicting dominant hand from spatiotemporal context varying physiological data](https://mlsanity.com/paper/2212.04077/) (2022)
- [VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild](https://mlsanity.com/paper/2303.06048/) (2023)

## Abstract (arXiv)

The ability to automatically recognize a person's behavioral context can contribute to health monitoring, aging care and many other domains. Validating context recognition in-the-wild is crucial to promote practical applications that work in real-life settings. We collected over 300k minutes of sensor data with context labels from 60 subjects. Unlike previous studies, our subjects used their own personal phone, in any way that was convenient to them, and engaged in their routine in their natural environments. Unscripted behavior and unconstrained phone usage resulted in situations that are harder to recognize. We demonstrate how fusion of multi-modal sensors is important for resolving such cases. We present a baseline system, and encourage researchers to use our public dataset to compare methods and improve context recognition in-the-wild.