mlsanityData to 7 Oct 2026

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

arXiv 1609.06354 ↗First posted 20 Sep 2016cs.AI cs.CY cs.HCcited by 41

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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.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 10; 2023: 11; 2024: 6; 2025: 7; 2026: 6.

41 papers.

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  1. 2022NOTE: Robust Continual Test-time Adaptation Against Temporal Correlationcited by 150
  2. 2021Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advancescited by 54
  3. 2022FedStack: Personalized activity monitoring using stacked federated learningcited by 15
  4. 2025SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactionscited by 11
  5. 2022Stop&Hop: Early Classification of Irregular Time Seriescited by 10
  6. 2023Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcitycited by 9
  7. 2024TinyLLM: A Framework for Training and Deploying Language Models at the Edge Computerscited by 9
  8. 2022Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learningcited by 8
  9. 2025SensorQA: A Question Answering Benchmark for Daily-Life Monitoringcited by 7
  10. 2022CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulatorcited by 6
  11. 2023Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Frameworkcited by 6
  12. 2023(Un)fair devices: Moving beyond AI accuracy in personal sensingcited by 6
29 more
  1. 2025Scaling laws in wearable human activity recognitioncited by 4
  2. 2025WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatchcited by 4
  3. 2026SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Tracescited by 4
  4. 2024Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognitioncited by 3
  5. 2025WatchHAR: Real-time On-device Human Activity Recognition System for Smartwatchescited by 3
  6. 2023Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networkscited by 2
  7. 2024Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognitioncited by 2
  8. 2022VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognitioncited by 1
  9. 2022UBIWEAR: An end-to-end, data-driven framework for intelligent physical activity prediction to empower mHealth interventionscited by 1
  10. 2023Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementiacited by 1
  11. 2023COMPASS: Unsupervised and Online Clustering of Complex Human Activities from Smartphone Sensorscited by 1
  12. 2024CRoP: Context-wise Robust Static Human-Sensing Personalizationcited by 1
  13. 2024Adaptive Client Selection with Personalization for Communication Efficient Federated Learningcited by 1
  14. 2025WHAR Datasets: An Open Source Library for Wearable Human Activity Recognitioncited by 1
  15. 2022HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Datacited by 0
  16. 2022On-device modeling of user's social context and familiar places from smartphone-embedded sensor datacited by 0
  17. 2022Predicting dominant hand from spatiotemporal context varying physiological datacited by 0
  18. 2023VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wildcited by 0
  19. 2023Neuro-Symbolic Approaches for Context-Aware Human Activity Recognitioncited by 0
  20. 2023DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervisioncited by 0
  21. 2023MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edgecited by 0
  22. 2023CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protectioncited by 0
  23. 2024Contrastive Learning with Auxiliary User Detection for Identifying Activitiescited by 0
  24. 2025Semantically Encoding Activity Labels for Context-Aware Human Activity Recognitioncited by 0
  25. 2026FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognitioncited by 0
  26. 2026WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognitioncited by 0
  27. 2026Multimodal Federated Learning under Dual-Axis Modality Missingnesscited by 0
  28. 2026PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generationcited by 0
  29. 2026Domain Generalization for Smartphone-Based Human Activity Recognition: A Systematic Analysis of Components and Interactionscited by 0