In this paper, we present our latest research engagement with a railroad company to apply machine vision technologies to automate the inspection and condition monitoring of railroad tracks. Specifically, we have proposed a complete architecture including imaging setup for capturing multiple video streams, important rail component detection such as tie plate, spike, anchor and joint bar bolt, defect identification such as raised spikes, defect severity analysis and temporal condition analysis, and long-term predictive assessment. This paper will particularly present various video analytics that we have developed to detect rail components, which form the building block of the entire framework. Our preliminary performance study has achieved an average of 98.2% detection rate, 1.57% false positive rate and 1.78% false negative rate on the component detection. Finally, with the lack of sufficient representative data and annotations to evaluate system performance on exception detection at both sequence and compliance levels, we proposed a mathematical modeling approach to calculate the probabilities of detecting such exceptions. Such analysis shows that there is still big room for us to improve our approaches in order to achieve desired false positive rate and miss detection rate at the sequence level.

Unlike machines, we humans are prone to boredom when we perform routine activities for long periods of time. Workers' mental engagement in boring tasks diminishes, which eventually, compromises their performance. The result is a double-whammy because the workers do not get job satisfaction and their employers do not receive optimal return on investment. This paper proposes a novel way for improving workers' mental engagement and hence, enjoyment, in routine activities. Specifically, we propose to blend in routine tasks mild mental/physical challenges. To test our hypothesis, we chose to experiment on a monitoring task typical of security guard operations. We combined this routine task with an iPhone-based game to make it more enjoyable. The results from 10 participants show that their mental engagement and enjoyment were significantly higher during the combined task.

The proposed demonstration is based on the work performed as part of the NEAT-o-Games project. NEAT-o-Games is a suite of games that runs on mobile terminals such as cell phones. Unlike other games, NEAT-o-Games' primary goal is to become part of people's everyday routines and attack the behavioral aspect of the sedentary lifestyle. Their main characteristic is that they are not carried out in short bouts, but are being played continuously and are interwoven in the daily routine of the players. Data from wearable accelerometers are logged wirelessly to a cell phone and control the animation of the player in a virtual race game (avatar) with other players over the cellular network. Players can use their excess of activity points earned from the race game to get hints in mental games of the suite, like Sudoku. Initial studies indicate that NEAT-o-Games may bring a positive physical, psychological, and social impact on players.