## 1 Introduction  
Activity recognition (AR) has become an active research topic [1] focused on detecting human behaviours in smart environments [2]. Sensing human activity has been adopted in smart homes [3] with the aim of improving quality of life, allowing people to stay independent in their own homes for as long as possible [4]. In initial approaches, there was a predominance of binary sensors used to describe daily human activities within smart environments in a noninvasive manner. Next, a new generation of devices emerged to integrate a richer perspective in sensing smart objects and people's activities. Among them, the following types of sensors stand out: (i) wearable devices, which have been used to analyse activities and gestures[5]; (ii) location devices, which at present reach extremely high accuracy in indoor contexts [6]; (iii) vision sensors (visible-light or thermal-infrared sensors) in video and image sequences [7]; (iv) audio sensors [8] that recognise events based on audio information. This has been followed by a new trend of multimodal sensors that has enabled the use of general-purpose sensing technologies to monitor activities. AR approaches are mainly grouped into two categories: knowledge-driven approaches [9] and data-driven approaches [10]. A number of previous AR studies have focused on classifying activities where the beginning and end of the activities, and therefore, the key features are known beforehand, which is referred to as explicit segmentation [11] or offline evaluation, as they do not provide real-time capabilities in AR. However, including real-time capabilities is a key requirement in AR in order to provide responses to real-world conditions [12], enabling _adequate assistance services_. In real-time AR, where the beginning and end of the events are unknown, approaches based on sliding windows to segment the data stream are required [11]. In addition, in the context of multimodal sensors, the use of deep learning models has shown promising performance in processing multimedia data [12]. In this work, we focus on the recognition of daily events by means of ambient sound devices and using deep learning models integrated with smart boards. The contribution of this work can be summarised as follows:  
* Collecting a dataset of audio samples of events related to activities of daily living which are generated within indoor spaces;
* Integrating a fog-edge architecture with the IoT boards where the audio samples are collected to provide real-time recognition of audio events;
* Evaluating the performance of deep learning models for offline and real-time recognition of ambient daily living events in naturalistic conditions;
* A straightforward fuzzy processing of audio event streams is described by means of temporal restrictions which are modeled on linguistic protoforms to improve the audio recognition. The remainder of the paper is organised as follows: In Section 1, we review related works and methodologies; in Section 2, we describe the devices, architecture, and methodology of the approach; in Section 3, we present the results of a case study of event recognition; in Section 4, we detail our conclusions and ongoing work. Related WorksThe integration of technology into smart environments to support our daily lives in an immersive and ubiquitous way was introduced by ubiquitous computing as _the age of calm technology, when technology recedes into the background of our lives_[13]. From this visionary perspective at the beginning of the 1990s to our present Internet of Things, two key characteristics have been exploited over the last 30 years: (i) immersiveness or low invasiveness of integrated devices (both on our bodies and in our environment) and (ii) smart connected devices which provide interpretable outcomes from the information collected by sensors.