## 1 Introduction  
With the growth of the senior population, elderly care becomes an important topic in the society. One aspect of elderly care is fall prevention, which is still challenging to tackle depending on the subject's health condition. In this context, artificial intelligence can be leveraged to notify about an increased risk. To achieve this goal, a solution consists in monitoring the subject's behaviour to detect some changes that could indicate a degradation of their mobility.  
Human activity recognition (HAR) can be used for that purpose. In this article, HAR is solved as a machine learning problem that predicts activities of daily living performed by a subject using sensors data that can be of different modalities. Two sensor categories are mainly used for human activity recognition: vision-based and body-worn sensors. Vision-based sensing relies on cameras placed in the environment to capture a video stream of a subject performing activities of daily living [1]. Body-worn sensors rely on inertial measurement units (IMU), including an accelerometer, a gyroscope and sometimes additional sensors (magnetometer, barometer, etc.) to measure the subject movements. Various devices such as smartphones [2], wearables [3] or application-specific devices [4] can be used to collect data, some being more invasive than others. Body-worn sensors generate fewer data than cameras and do not require a specific environment setup. It is therefore easier to embed on autonomous devices.  
Our approach is based on an inertial measurement unit embedded in smart glasses. Smart glasses are less invasive than some other devices such as dedicated IMU devices or even smartphones, especially for elderly for whom wearing glasses is common. However, and to the best of our knowledge, there is no available and usable dataset for human activity recognition based on smart glasses. Moreover, data would vary from one device to another due to sensors having different orientations, ranges, accuracy and sampling rates.  
In this article, we present a new dataset [5] called UCA-EHAR with data collected from Ellcie Healthy's smart glasses [6]. Our dataset provides raw data collected from an accelerometer, a gyroscope and a barometer for 8 classes of activity performed by 20 subjects.  
Additionally, for privacy, connectivity and latency reasons, all the data processing related to human activity recognition is performed directly on the smart glasses. Therefore, the machine learning algorithm performing the classification task is executed on the smart glasses' microcontroller. In previous works, we presented our MicroAI framework for end-to-end training, quantization and deployment of deep neural networks on microcontrollers [7]. This framework is now available as open-source [8]. In this work, the MicroAI framework is used to deploy a deep neural network model performing human activity recognition on the smart glasses. Quantization with 8-bit and 16-bit fixed-point representations is used to optimize the memory footprint and the inference time, thus reducing the power consumption as well.  
Section 2 gives an overview of some of the available datasets and approaches for human activity recognition. Section 3 presents the smart glasses used for collecting data and performing live inference. Section 4 details the dataset and the protocol used to collect the data. Section 5 describes the deep neural network architecture used to classify activities from our dataset as well as the training phase. Section 6 summarizes the key characteristics of our MicroAI framework, such as its quantization and deployment process. In Section 7, classification results using our dataset are given and power consumption on the smart glasses is analysed. Finally, Section 8 concludes this work and discusses future perspectives.