Sensors embedded in smartphones are an essential component for activity recognition. Even though the accelerometer is the most widely used sensor, the highest recognition accuracies are obtained when using data collected from multiple sensors. However, the use of multiple sensors has an adverse impact on the energy consumption of power-limited devices such as smartphones. In this paper, we present a new method to improve the recognition accuracy of physical activities by using only the accelerometer. We utilize a low-pass filter to split the acceleration data into a low- and a high-frequency component. These components provide a new set of features, which can be used as a complement to the raw acceleration to reduce the number of sensors needed to recognize physical activities. After evaluating our method for a public dataset, we found that our approach represents an average of up to 16% increase in the recognition accuracy over the raw acceleration data, outperforming even widely used combinations such as the raw acceleration plus the gyroscope. The highest accuracies are obtained when using a cut-off frequency in the interval [0:001--0:05] Hz as well as a combination of the acceleration with its low-frequency component.

Unfortunately, every year many vulnerable road users (VRUs), such as pedestrians or bicyclists are killed or seriously injured in traffic accidents. Several research groups are working on different solutions including radar or laser based approaches to reduce the number of traffic accidents VRUs are involved. The approach presented in this paper is based on wireless communications such as WLAN and 5G/LTE and a "context filter". The "context filter" identifies vulnerable road users in potentially dangerous situations based on several contexts (VRU position, movement direction, accelerations). An identified dangerous situation is communicated between the VRU and the cars using wireless communication such as ad hoc or cellular systems. As one specifically interesting context for identifying dangerous situations this paper investigates on detecting pedestrians stepping onto the road. We examine smartphone sensor data and several reasoning classifiers to investigate whether "stepping onto the road" by detecting a pedestrian "crossing a curb" is possible. To the best of our knowledge detecting pedestrians crossing a curb by using smartphone sensors, has not been investigated so far.

A person's context data can be used for a multitude of applications, such as energy management or health care. Common context recognition approaches rely on several factors, such as the sensor set, features, or the context modeling algorithm. Discovering the recognition performances of different parameter setting combinations is a complex, time-consuming, and error-prone task. To support the context recognition research, we present the Context Recognition Assistance Tool (CRAT). The Context Recognition Assistance Tool assist by automatically conducting the evaluation for a multitude combination of parameter settings and clearly presents the findings. Using the CRAT, we investigate to what degree five parameters influence the recognition accuracy. To support the research in the field of context recognition, the CRAT is publicly available.