An integral part of human-robot collaboration is the ability ofthe robot to understand and predict human motion. Predicting what the human collaborator will do next is very useful in planning the robot's response. In this paper, an algorithm for early detection and prediction of human activities is presented. For a given sequential task composed of many steps, a long short-term memory (LSTM) recurrent neural network (RNN) model is trained to learn the underlying sequence of steps. The trained network is then used to make predictions about the subsequent steps the human is about to carry out.

Over 72.5 million connected car units are estimated to be sold by 2023, enabling nearly 70% of all passenger vehicles to actively exchange data with external sources. The amount of data resulting from these smart vehicles will be overwhelming for traditional data processing solutions to gather and analyze, as well as the associated latency of processing this data-- leading to potential life-or-death scenarios, according to Ramya Ravichandar, from Foghorn. We speak with Ravichandar, about how connected car manufacturers are implementing edge AI solutions for real-time video recognition, multi-factor authentication, and other innovative capabilities to decrease network latency and optimize data gathering, analyzing and security. Digital Journal: What are the current trends with autonomous and connected cars? Ramya Ravichandar: Automotive companies are looking to improve real-time functionalities and accelerate autonomous operations of passenger vehicles. Connected vehicle technology is introducing a new dimension of transportation by extending vehicle operations and controls beyond the driver to include internal networks and systems.