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Over the last few years, researchers have proposed several WiFi based gesture recognition systems that can recognize predefined gestures performed by users at runtime. As most environments are inhabited by multiple users, the true potential of WiFi based gesture recognition can be unleashed only when each user can independently define the actions that the system should take when the user performs a certain predefined gesture. To enable this, a gesture recognition system should not only be able to recognize any given predefined gesture, but should also be able to identify the user that performed it. Unfortunately, none of the prior WiFi based gesture recognition systems can identify the user performing the gesture. In this paper, we propose WiID, a WiFi and gesture based user identification system that can identify the user as soon as he/she performs a predefined gesture at runtime. WiID integrates with the WiFi based gesture recognition systems as an add-on module whose sole objective is to identify the users that perform the predefined gestures. The design of WiID is based on our novel result which states that the timeseries of the frequencies that appear in WiFi channel's frequency response while performing a given gesture are different in the samples of that gesture performed by different users, and are similar in the samples of that gesture performed by the same user. We implemented and extensively evaluated WiID in a variety of environments using a comprehensive data set comprising over 25,000 gesture samples.

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Perception-reaction time (PRT) and deceleration rate are two key components in geometric design of highways and streets. Combined with a design speed, they determine the minimum required stopping sight distance (SSD). Current American Association of Highway Transportation Officials (AASHTO) SSD guidance is based on 90th percentile PRT and 10th percentile deceleration rate values from experiments completed in the mid-1990s. These experiments lacked real-world distractions, and so forth. Thus, the values from these experiments may not be applicable in real-world scenarios. This research evaluated (1) differences in PRTs and deceleration rates between crash and near-crash events and (2) developed predictive models for PRT and deceleration rate that could be used for roadway design. This was accomplished using (1) genetic matching (with Rosenbaum’s sensitivity analysis) and (2) quantile regression. These methods were applied to the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) data. The analysis results indicated that there were differences in PRT and deceleration rates for crash and near-crash events. The specific estimates were that, on average, drivers involved in crash events took 0.487 s longer to react and decelerated at 0.018 g’s (0.58 ft/s2) slower than drivers in equivalent near-crashes. Prediction models were developed for use in roadway design. These models were used to develop tables comparing existing SSD design criteria with SSD criteria based on the results of the predictive models. These predicted values indicated that minimum design SSD values would increase by 10.5–129.2 ft, dependent on the design speed and SSD model used.

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