# Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices
## 1 Introduction  
The concept of a smart house or smart living environment is more current than ever, as more and more everyday devices are equipped with smart capabilities. The importance of activity recognition research lies in the advantages of being able to monitor and assist a person who uses smart sensors [1]. Internet connectivity, which is, in most cases omnipresent, has even increased the range of these smart devices as they can now communicate with any internet node and can even be controlled or supervised remotely. The security aspect of this emerging connectivity is still a very important one, as personal data are sent around via the internet, so any device or platform that is exposed over the internet needs to be properly secured to make sure that the personal data are only available to the correct system and are stored only according to the user's agreement. Human Activity Recognition (HAR) is one area that greatly benefits from this emerging trend of having smart capable devices around the living environment. The amount of available data for human activity recognition greatly increases as we are surrounded by smart capable devices, thus we can detect human activity more precisely. This increase in HAR precision can be especially helpful in environments that are inhabited by multiple persons. In most cases, smart devices can pinpoint the current user, providing additional context when trying to resolve the multiple inhabit HAR issue, more precisely, to identify a certain person and link them to a specific complex activity in a multi-person space. Internet of Things (IoT) technology is applied in multiple domains such as medicine, manufacturing, emergency response, logistics, and daily life activity recognition. Furthermore, emerging IoT technologies allow us to not only supervise HAR but also to actively react, based on these and other data, by controlling smart-enabled devices or contacting other persons. This kind of complex supervision and environment control, together with all the smart-enabled devices and systems, make up the building blocks of the smart home, a home that can help its occupants in day-to-day tasks and especially during emergencies or life-threatening situations. Activity recognition can be performed using three main sensor categories: ambient, vision, and body-worn, as also shown in [2; 3; 4]. Ambient sensors are a great way to supervise relatively small areas and require changes across the living environment to function. The main disadvantage of using ambient sensors is the fact that they require a specially prepared living area for HAR analysis. This kind of ambient sensor implementation can help when dealing with multiple inhabitant spaces where we need to identify and pinpoint each user, as the entire living space is supervised at any moment and acts as a single unit. As the supervised living environment increases, the required number of nodes increases as well. A particular type of ambient sensor is vision-based, which can offer great user recognition in multi-inhabitant spaces. This type of ambient sensor requires multiple vision devices installed across the living space (at least one vision-based sensor for every room) and raises some potential privacy issues, especially if the data are analyzed outside the local living environment network. Body-worn sensors, also known as wearable devices, can directly gather and even process the sensor data closest to the user's body. Since the main data-gathering device is worn on the user's body, this approach will work fully or partially even in non-controlled environments. For this body-worn sensor type, we have only network or minimum infrastructure requirements for the system to work, even if there are multiple types of devices that send data via many types of communication technologies [5]. A person usually performs many types of activities, based on simple and complex movements [6], and this physical information is easily collected through wearable devices like commercial mobile phones and portable devices [7].