Impaired articulation, chewing, or swallowing can have a strong impact on Quality of Life. These behaviors can be affected by decreased oral muscle function caused by aging and/or certain medical conditions. Aspiration pneumonitis is a major cause of death among elderly people, and is triggered by impaired swallowing, mainly due to decreased oral muscle strength. To reduce the risk of aspiration pneumonitis, elderly people are often advised to exercise and train their oral muscles. Unfortunately, such training tends to be monotonous and it is often difficult for trainees to detect the effects of the training, i.e., improved oral function. To address this issue, we developed an oral muscle training game called Squachu. Squachu is composed of a non-contact tongue-mouth-motion detection system and a game engine. As motion is tracked using a depth camera, players play the game via mouth and tongue motion and do not have to wear any specialized equipment. Thus, the game is hygienic and easily accessible for daily play.

Bounded Model Checking (BMC) converts a verification problem within a user-specified bound into satisfiability checks of propositional formulas. As the bound deepens, the formulas become larger in size and harder to solve. In this paper, we propose a hybrid approach in which stateless explicit-state exploration (SESE) is integrated into the BMC process to improve the scalability and performance of BMC for the verification of properties expressed in Linear Temporal Logic (LTL). Specifically, SESE is utilized to traverse, under the constraints of Bounded-Context Switching (BCS), the state space of a system design and memorize legal execution paths. These paths are classified according to heuristic state predicates into path clusters, which are then encoded into propositional formulas representing, together with the encoded formula for an LTL property, independent BMC instances. Such BMC instances are solved with SMT solvers running on mutilcores in parallel. Once a counterexample is found for one of the instances, the entire model checking (SESE as well as BMC) terminates. This hybrid checking procedure progresses in an incremental fashion until either a counterexample is found or the user-specified bound is reached. We have implemented this proposed hybrid approach in a tool called Garakabu2 with Yices 2 as its back-end solver. The experimental results show that Garakabu2 outperforms significantly the state-of-the-art BMC methods implemented in SAL for both safety and liveness properties.

We propose a hybrid approach to improving the verification performance of SMT-based bounded model checking for LTL properties. In this approach, stateless explicit-state exploration is utilized to traverse, under the constraints of bounded context switches, the state space of a system design and memorize legal execution paths. These paths are classified according to certain predicates into path clusters, which are then encoded into propositional formulas representing, together with the encoded formula for an LTL property, independent BMC instances. Such BMC instances are solved with SMT solvers running on mutilcores in parallel. Once a counterexample is found for one of the instances, the entire model checking terminates. This hybrid checking procedure progresses in an incremental fashion until either a counterexample is found or the user-specified bound is reached. We have implemented this proposed hybrid approach in a tool called Garakabu2 with CVC4 as its backend solver. The experimental results show that Garakabu2 often outperforms the state-of-the-art pure BMC methods implemented in SAL infinite bounded model checker for both safety and liveness properties.