The wearable devices market has been growing exponentially in the last few years and it is expected to count up to 930 million devices by the end of 2021. A common application of wearable devices is Human Activity Recognition (HAR), i.e., the ability of using the sensing capabilities of these devices to monitor and infer human activities. However, Animal Activity Recognition (AAR) has received significantly less attention, and most works on AAR are generally based on invasive specialized devices carried by or implanted on animals. Conversely, in this work we exploit the potential of portable and unobtrusive devices, namely smartwatches, for AAR and specifically for horse gaits recognition. This application finds natural use in horse riding, to improve the structure and balance of the horse work and training. We develop a framework that can be used in a fog computing system composed by a smartphone and a smartwatch for the recognition of horse gaits. The framework performs classification by means of a machine learning approach trained on ad-hoc features based on accelerometer data. The framework allows an offline and an online modes of operation. In the offline mode, the smartwatch is used to collect the accelerometer data and transfer it to the smartphone at the end of the riding session. The feature extraction and classification can be processed directly on the smartphone or offloaded to the cloud. Conversely, in the online mode, the smartwatch is responsible to collect and process the data, thus being able to provide real-time feedback to the rider. This modality also allows to reduce computation, storage, and energy burden on the smartwatch through an adaptive setting of the sampling frequency. We implement our approach on a system composed by a Fitbit Ionic smartwatch and a Samsung Galaxy S10. We use two horses to evaluate the performance, versus recently proposed AAR approaches. Results show that our framework achieves significantly higher classification accuracy. Furthermore, the online scheme enables flexible real-time feedback, at the expense of a small loss in the classification accuracy.

可穿戴设备市场在过去几年中呈指数增长，预计到2021年底将达到9.3亿个设备。可穿戴设备的常见应用是人类活动识别（HAR），即使用能力这些设备的监视和监视人类活动的感知能力。但是，动物活动识别（AAR）受到的关注明显较少，并且大多数有关AAR的工作通常基于动物携带或植入的侵入性专用设备。相反，在这项工作中，我们充分利用了便携式和不干扰设备（即智能手表）在AAR（特别是骑马步态）中的潜力承认。此应用程序可以在骑马中找到天然用途，以改善骑马和训练的结构和平衡。我们开发了可在由智能手机和智能手表组成的雾计算系统中使用的框架，用于识别马步态。该框架通过基于加速度计数据对临时特征进行训练的机器学习方法来执行分类。该框架允许离线和在线操作模式。在离线模式下，智能手表用于收集加速度计数据，并在骑行结束时将其传输到智能手机。可以直接在智能手机上处理特征提取和分类，也可以将其卸载到云中。相反，在在线模式下，智能手表负责收集和处理数据，从而能够向骑手提供实时反馈。这种方式还可以通过自适应设置采样频率来减少智能手表的计算，存储和能源负担。我们在由Fitbit Ionic智能手表和三星Galaxy S10组成的系统上实施我们的方法。与最近提出的AAR方法相比，我们使用两匹​​马来评估性能。结果表明，我们的框架实现了明显更高的分类准确性。