# Context-Aware Activity Recognition Systems

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/context-aware-activity-recognition-systems/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on activity recognition in pervasive computing environments, utilizing wearable sensors and accelerometer data. It explores the application of context-aware systems for health monitoring, smart homes, and ambient intelligence. The papers also delve into the use of deep learning algorithms for accurate activity recognition and fall detection. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10444 |
| Works | 190 |

## Topic papers all

Showing 15 of 190.

- [A survey of mobile phone sensing](https://scholariq.org/papers/a-survey-of-mobile-phone-sensing/)
- [Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis](https://scholariq.org/papers/dose-response-associations-between-accelerometry-measured-physical-activity-and/)
- [The design space of wireless sensor networks](https://scholariq.org/papers/the-design-space-of-wireless-sensor-networks/)
- [A Survey on Ambient-Assisted Living Tools for Older Adults](https://scholariq.org/papers/a-survey-on-ambient-assisted-living-tools-for-older-adults/)
- [Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly](https://scholariq.org/papers/ambulatory-system-for-human-motion-analysis-using-a-kinematic-sensor-monitoring/)
- [Collecting complex activity datasets in highly rich networked sensor environments](https://scholariq.org/papers/collecting-complex-activity-datasets-in-highly-rich-networked-sensor/)
- [Unobtrusive Sensing and Wearable Devices for Health Informatics](https://scholariq.org/papers/unobtrusive-sensing-and-wearable-devices-for-health-informatics/)
- [Sensor-based and vision-based human activity recognition: A comprehensive survey](https://scholariq.org/papers/sensor-based-and-vision-based-human-activity-recognition-a-comprehensive-survey/)
- [A robust human activity recognition system using smartphone sensors and deep learning](https://scholariq.org/papers/a-robust-human-activity-recognition-system-using-smartphone-sensors-and-deep/)
- [Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information](https://scholariq.org/papers/accurate-fast-fall-detection-using-gyroscopes-and-accelerometer-derived-posture/)
- [SoundSense](https://scholariq.org/papers/soundsense/)
- [The Jigsaw continuous sensing engine for mobile phone applications](https://scholariq.org/papers/the-jigsaw-continuous-sensing-engine-for-mobile-phone-applications/)
- [A Practical Approach to Recognizing Physical Activities](https://scholariq.org/papers/a-practical-approach-to-recognizing-physical-activities/)
- [Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls](https://scholariq.org/papers/evaluation-of-accelerometer-based-fall-detection-algorithms-on-real-world-falls/)
- [Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review](https://scholariq.org/papers/toward-pervasive-gait-analysis-with-wearable-sensors-a-systematic-review/)

## Topic primary papers

Showing 15 of 87.

- [A Survey on Ambient-Assisted Living Tools for Older Adults](https://scholariq.org/papers/a-survey-on-ambient-assisted-living-tools-for-older-adults/)
- [Collecting complex activity datasets in highly rich networked sensor environments](https://scholariq.org/papers/collecting-complex-activity-datasets-in-highly-rich-networked-sensor/)
- [Sensor-based and vision-based human activity recognition: A comprehensive survey](https://scholariq.org/papers/sensor-based-and-vision-based-human-activity-recognition-a-comprehensive-survey/)
- [A robust human activity recognition system using smartphone sensors and deep learning](https://scholariq.org/papers/a-robust-human-activity-recognition-system-using-smartphone-sensors-and-deep/)
- [Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information](https://scholariq.org/papers/accurate-fast-fall-detection-using-gyroscopes-and-accelerometer-derived-posture/)
- [The Jigsaw continuous sensing engine for mobile phone applications](https://scholariq.org/papers/the-jigsaw-continuous-sensing-engine-for-mobile-phone-applications/)
- [A Practical Approach to Recognizing Physical Activities](https://scholariq.org/papers/a-practical-approach-to-recognizing-physical-activities/)
- [Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls](https://scholariq.org/papers/evaluation-of-accelerometer-based-fall-detection-algorithms-on-real-world-falls/)
- [A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices](https://scholariq.org/papers/a-deep-learning-approach-to-on-node-sensor-data-analytics-for-mobile-or-wearable/)
- [A hybrid discriminative/generative approach for modeling human activities](https://scholariq.org/papers/a-hybrid-discriminative-generative-approach-for-modeling-human-activities/)
- [Intelligent Assistive Technology Applications to Dementia Care: Current Capabilities, Limitations, and Future Challenges](https://scholariq.org/papers/intelligent-assistive-technology-applications-to-dementia-care-current/)
- [Sensor Positioning for Activity Recognition Using Wearable Accelerometers](https://scholariq.org/papers/sensor-positioning-for-activity-recognition-using-wearable-accelerometers/)
- [A Scalable Approach to Activity Recognition based on Object Use](https://scholariq.org/papers/a-scalable-approach-to-activity-recognition-based-on-object-use/)
- [Human activity recognition in artificial intelligence framework: a narrative review](https://scholariq.org/papers/human-activity-recognition-in-artificial-intelligence-framework-a-narrative/)
- [Mobility Detection Using Everyday GSM Traces](https://scholariq.org/papers/mobility-detection-using-everyday-gsm-traces/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.