Roozbeh Jafari
Researcher / author (OpenAlex)
Facts
2 properties- Affiliation
- Biotex (United States), MIT Lincoln Laboratory, Decision Systems (United States), Texas A&M Universityas of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
🔖 Identifiers · 2
- ORCID iD
- 0000-0002-6358-0458
- OpenAlex ID
- A5011769350as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 38
Action Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns,
A Decision Level Fusion and Signal Analysis Technique for Activity Segmentation and Recognition on Smart Phones,
A dynamically reconfigurable ECG analog front-end with a 2.5 × data-dependent power reduction,
A Hardware-Assisted Energy-Efficient Processing Model for Activity Recognition Using Wearables,
A human-centered wearable sensing platform with intelligent automated data annotation capabilities,
A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing,
An ECG dataset representing real-world signal characteristics for wearable computers,
A particle filter framework for the estimation of heart rate from ECG signals corrupted by motion artifacts
+30 more
← Authored by 38
Action Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns,
A Decision Level Fusion and Signal Analysis Technique for Activity Segmentation and Recognition on Smart Phones,
A dynamically reconfigurable ECG analog front-end with a 2.5 × data-dependent power reduction,
A Hardware-Assisted Energy-Efficient Processing Model for Activity Recognition Using Wearables,
A human-centered wearable sensing platform with intelligent automated data annotation capabilities,
A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing,
An ECG dataset representing real-world signal characteristics for wearable computers,
A particle filter framework for the estimation of heart rate from ECG signals corrupted by motion artifacts
+30 more
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Summary
Roozbeh Jafari is a human[1].