The Ambient Intelligence Lab (AMI) was founded in year 2016; It is a cutting-edge research lab at the Arab Open University, Lebanon Branch. AMI Lab is dedicated to develop innovative ubiquitous systems that strive toward establishing a smart environment, thus enhancing the university campus life and its interaction with the surrounding environment.

About Ambient Intelligence

Ambient Intelligence aims at enhancing the interaction between people with their environment to promote safety and to enhance their life standards. However, deploying Ambient Intelligence largely depends on the technology adopted, for instance smart sensors that are interconnected through networks, as well as on the Artificial Intelligence (AI) used for decision-making.

Research Scope

Nowadays vast amount of data are generated from different sources, whether using handheld devices or using implanted smart sensors. These data are either stored/replicated on local devices or on the cloud. By mining, analyzing these data using AI, one can create a smart environment. This can be achieved by creating smart campuses, smart cities and smart homes.

AMI Lab Current Projects:

Traffic Alerting System using drivers smart Phones (TALES):
Developing a traffic monitoring system using (M2M)/"Internet of Things” technologies requires the implantation of sensors on roads, but nowadays almost all drivers carry mobile phones. Hence, the integrated sensors within the mobile phones can be used as replacement of the required smart sensors. We propose a traffic alert system that uses the drivers’ Mobile phone sensors and the lightweight MQTT protocol instead of HTTP to exchange messages between the Mobile and the cloud.

Heart Rate Test Reports Combined With Detailed User Contextual Information Using (Handheld Devices) Smartphone:
Technology is intended to serve many aspects, especially in healthcare. In this work we investigated how providing detailed contextual information records of users testing for heart rate using their smartphones might enhance diagnostics of heart rate results. The HRM sensor was used in a smartphone device to perform personal heart rate tests and build test reports combined with specific determinants that affect heart rate. Using the smartphone embedded sensors, we customized algorithms for user activity recognition to auto detect user activities and to gather environmental factors that affect heart rate performance.

Never mind, we know who you are (KNOWYOU):
People are becoming more and more dependent on wide spectrum of new powerful computing devices such as ubiquitous, pervasive, embedded and handled devices. Whether you are the owner of these devices or are privileged to use them, you must first successfully pass some of their authentication mechanisms. Unfortunately, these systems have different interfaces and hence provide different authentication techniques, whether implicit or explicit. Thus, providing a unified dynamic cloud based engine that is able to implicitly authenticate you, and transfer this authentication to all systems surrounding you, will afford you to deploy and to benefit more from these computing systems and their services. We propose a cloud based engine that tracks the user behavior on end user devices and decide whether he/she is an adversary or not.