# Deep learning for human activity recognition: A resource efficient implementation on low-power devices

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/deep-learning-for-human-activity-recognition-a-resource-efficient-implementation/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Daniele Ravì,Charence Wong,Benny Lo,Guang‐Zhong Yang |
| Citations | 257 |
| DOI | 10.1109/bsn.2016.7516235 |
| Fields | Computer Science,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2495920728 |
| Type | conference-paper |
| Year | 2016 |

## Paper authors

- [Benny Lo](https://scholariq.org/researchers/benny-lo/)

## Paper primary topic

- [Context-Aware Activity Recognition Systems](https://scholariq.org/topics/context-aware-activity-recognition-systems/)

## Paper topics

- [Context-Aware Activity Recognition Systems](https://scholariq.org/topics/context-aware-activity-recognition-systems/)
- [Non-Invasive Vital Sign Monitoring](https://scholariq.org/topics/non-invasive-vital-sign-monitoring/)
- [IoT and Edge/Fog Computing](https://scholariq.org/topics/iot-and-edge-fog-computing/)

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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.