InSyS Technology leverages the power of Machine Learning (ML) to understand human emotional states, intentions, and sentiments. Our state-of-the-art technology powered by biosensors and ML algorithms detect nuanced changes in mood and behavior via analysis of different physiological variables such as heart rate variability, blood pressure, skin conductance level, finger temperature, and more.
The system creates actionable insights that improve security, consumer satisfaction, and user experience. InSyS Technology can be seamlessly integrated into the public surveillance systems, computer hardware, infrastructure to instantly turn biometric variables into data for government agencies, enterprises, public agencies, and individuals.
InSyS Technology rests on the vision that better interaction between humans and machines can dramatically improve the life of citizens, public security and boost business innovation.
WHAT WE DO:
For too long, governments and companies have struggled to open the ‘block box’ of human emotions to improve public safety, economic well-being, and consumer experience. InSyS breaks the mold by turning human biometric data into actionable insights that improve decision-making and understanding of human behavior. At the core of InSyS Technology is the revolutionary Machine Learning system powered by biosensors that read critical physiological information in a broad range of environments. Thanks to its powerful self-learning features, non-invasiveness, and seamless integration into numerous environments, the InSyS technology is able to understand hidden patterns of the human emotional state that would otherwise have gone unnoticed.
MSc, Electrical Engineering, Co-Founder at InSyS
Abeer is a passionate researcher with a focus on innovative methods in Artificial Intelligence, Machine Learning, and Neuro-Fuzzy Systems. Her key contribution to the InSyS Technology is implementing a theoretical model to define the security threat levels by measuring human emotional state and building the physical Neural/Fuzzy Logic System of the security products. Abeer has participated in several projects such as detecting human emotions by measuring EEG waves for different human categories, tele-traffic control system to detect drivers’ sleepiness status using adaptive neuro-fuzzy inference system, and analyzing EEG wavelets to enable mouse control for people with special needs.
PhD, Computer Engineering, Co-Founder at InSyS
Mohammad is an experienced Computer Scientist and network architect who specializes in the design of High-Throughput Broadband Wireless Systems. He has developed a Large Scale Radio Frequency Service Management Data and Large Scale Per-Call Management Data Systems that integrate ML and pattern recognition functionality into the network interfaces. His key contribution to the InSyS Technology is the exploration of emotion detectability through external physiological variables and the development of the Wireless Sensor Neural/Fuzzy Logic System that underlies InSyS security products. Dr. Malkawi supervised three master theses and several publication in this domain.