A horseback riding simulator is a robotic machine that simulates the motion of horseback riding. In this paper, we present an interaction control system for a horseback riding simulator. The proposed system provides a postural correction function suited to the user based on their historical log and specialized posture coaching data. The system has adopted certain schemes for posture detection and recognition of the identified user as a way to recommend customized exercise modes. Our experiments show that including these techniques will help users maintain good posture while riding.

IT technologies have started to be applied to many fields such as sports science, robotics or BT. Especially, object detection and recognition using vision sensor has intensively studied due to diverse application fields. But, various illumination condition, pose or time progress have put object detection and recognition to challenge task in the real world. In this paper, face detection and recognition using vision sensor applied to sports simulator which has been proposed. Face detection has been processed to identify straight in the face that has used to detect head pose of riders. Face recognition has used to identify user, who has tried to take horse riding simulator. Gabor wavelet and face graph has used to recognize low quality face image, which has acquired under poor illumination environment and movement of simulator. We have simulated on FERET and ETRI database. ETRI database has acquired on horse riding simulator under poor illumination condition. The accuracy of 91% face recognition rate on FERET and encouraging result of 82% recognition rate on ETRI DB have been obtained.

This paper proposes user segmentation algorithm using moving edge detection to provide customized coaching service to users in advanced sports simulator like horse riding. To give this service in the simulator, exact user segmentation is very important. If the segmenting user from its background became better, we will get a better pose recognition results. In this paper, some of the well-known object segmentation methods to be applied to sports simulator have been investigated. Also, a moving object detection algorithm for recognizing user's poses which is a novel innovation to assure high recognition reliability in sports simulator has been proposed.

Ubiquitous location-based services have become a popular trend these days as a next-generation paradigm. Various research studies are underway to provide the location-based services through wireless sensor networks in an indoor environment. In this paper, we present a user's location recognition system based on environmental sensor data in an indoor living space. The system gathers the environmental data and extracts the feature data from them. It also recognizes and classifies the current location information of the user. We describe the configuration of the sensor module used for location recognition, and present a recognition system that is designed and implemented to recognize the location based on data from the sensor signals, and describe the pertinent aspects of our experimental results.

When a receiver of a ciphertext message can not decrypt the message due to the fact that he has lost his private-key, the private-key of the receiver and session-key of the message need to be recovered. We demonstrate how we have modeled and analyzed a new type of multiple agent based key recovery protocol. It is characterized by key encapsulation approach, protocol generalization, secret choice of key recovery agents and fork/join of session-keys by random-keys. The proposed protocol is formally modeled by a new pictorial model, an extended cryptographic timed Petri net (ECTPN). Recoverability of a session-key as well as performance of the protocol is verified by using a reachability graph of the ECTPN.