A Real-Time Face Motion Based Approach towards Modeling Socially Assistive Wireless Robot Control with Voice Recognition

Abstract

Abstract-The robotics domain has a couple of specific general design requirements which requires the close integration of planning, sensing, control and modeling and for sure the robot must take into account the interactions between itself, its task and its environment surrounding it. Thus considering the fundamental configurations, the main motive is to design a system with user-friendly interfaces that possess the ability to control embedded robotic systems by natural means. While earlier works have focused primarily on issues such as manipulation and navigation only, this proposal presents a conceptual and intuitive approach towards man-machine interaction in order to provide a secured live biometric logical authorization to the user access, while making an intelligent interaction with the control station to navigate advanced gesture controlled wireless Robotic prototypes or mobile surveillance systems along desired directions through required displacements. The intuitions are based on tracking real-time 3-Dimensional Face Motions using skin tone segmentation and maximum area considerations of segmented face-like blobs, Or directing the system with voice commands using real-time speech recognition. The system implementation requires designing a user interface to communicate between the Control station and prototypes wirelessly, either by accessing the internet over an encrypted WiFi Protected Access (WPA) via a HTML web page for communicating with face motions or with the help of natural voice commands like "Trace 5 squares", "Trace 10 triangles", "Move 10 meters", etc. evaluated on an iRobot Create over Bluetooth connectivity using a Bluetooth Access Module (BAM). Such an implementation can prove to be highly effective for designing systems of elderly aid and maneuvering the physically challenged.

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

“…In particular, 43% of them are above 90% and, in some cases, close to 100%. For example, tracking the face with the HSV & RGB algorithm [ 18 ] offers performance of 99%; monitoring the exercise routine of a person with the CNN & LSTM algorithms [ 57 ] yields performance of 99.87%.…”

“…For face tracking, they calculated the centroids of the eyes and mouth by implementing the k-means algorithm to determine the position of the face and transform it into a command to move the same wheelchair. Moreover, Bhattacharjee et al [ 18 ] proposed an algorithm for face detection and tracking based on segmentation in two color spaces, HSV (hue, saturation, value) and RGB (red, green, blue). First, the algorithm finds the regions that belong to the skin color to locate the face’s position within a frame; second, it converts this position into instructions and sends them to a robot, which uses them to move its wheels and track a person in a room in order to maintain his face in the center of the image.…”