## 8 Conclusions  
In this article, a novel dataset for human activity recognition called UCA-EHAR has been presented. This dataset gathers data collected from the accelerometer, the gyroscope and the barometer of smart glasses. UCA-EHAR is the first publicly available dataset dedicated to human activity recognition on activities of daily living using smart glasses. To provide a comparison baseline for a classification task, we evaluated the performance of a residual neural network on our dataset and we provided accuracy results as well as a confusion matrix. The accuracy for this dataset using a floating-point ResNetv1-6 with 80 filters per convolution is 80.2%. However, such a floating-point implementation does not respect embedded constraints of the smart glasses. Therefore, the neural network has been quantized using 8-bit and 16-bit fixed-point inference in order to optimize the memory footprint and the inference time, thus the energy consumption. Obtained results show that the 16-bit quantization provides the best accuracy vs. memory efficiency. To illustrate the energy that can be saved by quantization, we deployed a deep neural network onto the smart glasses using our MicroAI framework. We then measured the current and voltage during a human activity recognition task running on the smart glasses. Using the 16-bit quantized network with 48 filters per convolution we have shown that we can run human activity recognition for up to 24 h on the smart glasses. In the future, we will build a dataset including more classes such as transitions (SIT_TO_STAND, STAND_TO_SIT, SIT_TO_LIE, LIE_TO_SIT) or other activities (DRIVING). We would also like to explore unsupervised online learning using this dataset. To do so, collecting data for some subjects over a longer period of time will be required. Preliminary results were already presented in [26] using the UCI-HAR dataset. Unsupervised online learning will be implemented in our MicroAI framework to automatically train, quantize and deploy a network composed of convolutional layers and unsupervised layers onto the smart glasses.  
Investigation, P.-E.N.; methodology, P.-E.N.; software, P.-E.N.; data curation, C.C.; supervision, A.P. and B.M.; writing--original draft preparation, P.-E.N.; writing--review and editing, A.P., B.M. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research is funded by \"Universite Cote d'Azur\", \"CNRS\", \"Region Sud Provence-Alpes-Cote d'Azur\" and \"Ellcie Healthy\".  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of CER (Comite d'Ethique de la Recherche) (protocol code n\({}^{\circ}\) 2022-033 and date 8 th of April of the Ethical Approval).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are openly available in Zenodo at [[https://doi.org/10.5281/zenodo.5659336](https://doi.org/10.5281/zenodo.5659336)]([https://doi.org/10.5281/zenodo.5659336](https://doi.org/10.5281/zenodo.5659336)) (accessed on 24 November 2021).  
The authors declare that Ellcie Healthy, one of the funders, has organized and managed the data collection of this study. However, the authors declare that there is no conflict of interest in the context of this study that has only been conducted by the researchers from LEAT lab.  
Figure 20 s extracted from RUNNING session of subject T1.  
Figure 20 s extracted from SITTING session of subject T1.  
Figure 20 s extracted from LYING session of subject T1.