# A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-hybrid-deep-learning-model-for-human-activity-recognition-using-multimodal/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Abdu Gumaei,Mohammad Mehedi Hassan,Abdulhameed Alelaiwi,Hussain AlSalman |
| Citations | 154 |
| DOI | 10.1109/access.2019.2927134 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | gold |
| OA URL | https://ieeexplore.ieee.org/ielx7/6287639/8600701/08786773.pdf |
| OpenAlex ID | https://openalex.org/W2966450377 |
| Type | article |
| Year | 2019 |

## Paper authors

- [Abdu Gumaei](https://scholariq.org/researchers/abdu-gumaei/)

## Paper journal

- [IEEE Access](https://scholariq.org/journals/ieee-access/)

## 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/)
- [IoT and Edge/Fog Computing](https://scholariq.org/topics/iot-and-edge-fog-computing/)
- [Non-Invasive Vital Sign Monitoring](https://scholariq.org/topics/non-invasive-vital-sign-monitoring/)

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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.