| dc.contributor.author | Byaruhanga, Jerry | |
| dc.date.accessioned | 2023-01-20T06:56:28Z | |
| dc.date.available | 2023-01-20T06:56:28Z | |
| dc.date.issued | 2022-09-09 | |
| dc.identifier.citation | Byaruhanga, Jerry. (2022). Data Compression for an Edge-based Machine Learning Model in IoT Devices. (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/14547 | |
| dc.description | A final project report submitted to Makerere University in partial fulfilment of the requirements for the award of BSc in Electrical Engineering. | en_US |
| dc.description.abstract | In this project, an IoT device, comprising an AD8232 ECG sensor kit and ESP32 microcontroller, was designed and developed to collect ECG data. Compression using a wavelet transform was then applied on the collected data prior to transmission giving a compression ratio of 12.8. The transmitted data was then rebuilt on the edge and processed using a deep learning sequential model. The model had training and validation accuracies of 97.1% and 96.5% respectively. In the aforementioned approach, we considered the context of driving behavior monitoring in where ECG signals data are collected from the driver using the developed IoT device and sent to an edge node for stress level detection. The results showed that the amount of transmitted data was reduced and this did not affect the driver stress level prediction accuracy. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Data Compression | en_US |
| dc.subject | Machine Learning Model | en_US |
| dc.subject | An IoT Device | en_US |
| dc.title | Data Compression for an Edge-based Machine Learning Model in IoT Devices | en_US |
| dc.type | Thesis | en_US |
