## 2 State of the Art  
Datasets for human activity recognition using various modalities have been flourishing for the past decade [9]. In this article, we mainly focus on body-worn sensors since vision-based or other environmental sensor approaches are significantly different compared to the smart glasses approach. The most iconic dataset for human activity recognition using an inertial measurement unit is likely the Human Activity Recognition dataset hosted by the University of California Irvine, commonly dubbed UCI-HAR [2]. This dataset is built from a 3-dimensional accelerometer and a 3-dimensional gyroscope sampled at 50 Hz, embedded into a smartphone attached to the subject's waist. The acceleration signal is filtered to create an additional signal without gravity. Therefore, there is a total of nine channels of sensor data. The data are windowed over 2.56 s with 50% overlap to create windows of 128 samples. The data are provided in two forms: vectors of 128 samples for each of the nine sensor channels, and vectors of 561 features computed from the \(128\times 9\) values. A total of 30 subjects participated in the experiments, performing 6 activities: WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LYING. A total of 21 subjects are used for training while the 9 others are used for testing, representing 7352 and 2947 vectors, respectively. As it will be seen further, some aspects of our dataset are inspired by UCI-HAR such as some classes and the window duration. The UCI-HAR dataset was extended in [10] to provide the transitions between static activities: STAND_TO_SIT, SIT_TO_STAND, SIT_TO_LIE, LIE_TO_SIT, STAND_TO_LIE, LIE_TO_STAND. This SBHAR dataset was used to evaluate the Transition-Aware Human Activity Recognition [11] system along with two other datasets: PAMAP2 and REALDISP. Instead of using a single smartphone with an accelerometer and a gyroscope, the PAMAP2 dataset [4] rather uses dedicated IMU devices called Colibri Wireless from Trivisio. One device is placed on the wrist, another one on the chest and a last one on the ankle. Each device contains a 3-dimensional accelerometer, a 3-dimensional gyroscope and a 3-dimensional magnetometer, along with a temperature sensor, all sampled at 100 Hz. Additionally, one heart-rate monitoring device is sampled at 9 Hz. In this dataset, nine subjects performed 12 to 18 activities. This setup is much more intrusive than UCI-HAR as multiple dedicated devices are used at specific location, making this approach harder to use in real conditions for live human activity recognition. The REALDISP [12] dataset has an even more complex setup, using 9 IMU devices from Xsens sampled at 50 Hz, each with a 3-dimensional accelerometer, a 3-dimensional gyroscope, a 3-dimensional magnetometer. The IMU devices also provide orientation estimates in quaternion format (4D) [13]. This dataset contains more classes performed by more subjects than PAMAP2, 33 classes and 17 subjects, respectively. Its purpose was to study the impact of sensor placement. Other popular human activity recognition datasets include UniMiB SHAR [14] containing accelerometer samples captured from a smartphone, Real-Life HAR [15] also collected from a smartphone but focusing on real-life situations (for example inactive, active or driving) rather than a laboratory setting, and OPPORTUNITY [16] that uses many sensors of different modalities. Apart from these datasets using data collected from smartphones or specific devices, there are few other datasets based on wearables available from the market. We can cite WISDM [3] using a combination of a smartphone and a smartwatch (LG G Watch) to collect data from 51 subjects performing 18 activities. Other datasets for human activity recognition, such as [17] relying on a Microsoft Band 2, have been created from consumer smartwatches. However, these datasets have not been released so far. More specifically, smart glasses are still not a popular device to use for human activity recognition.