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Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best.

Running robots often have their center-of-mass (CoM) of the torso located on the hip, to allow for simple control schemes. However, an offset between the CoM and the hip might increase a robot's ability to recover from disturbances. In this simulation study, we investigated the effect of the CoM-location on the largest disturbance that can be corrected within one or two steps. We found that, for one-step recovery strategies, the optimal CoM-location is above the hip for a step-down disturbance and below the hip for a push disturbance. For two-step recovery strategies, we found that the performance increases for increasing offset of the CoM. An offset of the CoM-location can increase the disturbance rejection up to a factor of 10 compared to the CoM at the hip.

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