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This paper focuses on finding the shortest path in a controlled indoor environment, called "smart lab". This study proposes a faster pathfinding model based on optimizing the decision-making process and fitting the hyper-parameters. At the same time, the study includes a comparison of performing the path planning on the robot through fog computing. The solution components are designed around the four main subcomponents of the reinforcement learning systems [1]: policy, reward signal, value function, and

Fall detection for elder people is a very challenging task that has not been solved yet. In this population, fall detection devices must not introduce extra constraints, like carrying with a belt fixed device or a mobile phone. This paper describes one fall detection approach accomplishing with these constraints. Afterwards, a discussion on some model decisions concerning the computational restrictions according to where the data processing and classification are performed is also included.