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Ahstract-The electronic horizon of a vehicle is limited by the range of the different sensor technologies. In contrast, infrastructure sensor data can track the traffic flow and even single vehicles from a bird's perspective, and this data can be used to post-validate driving maneuvers by comparing a vehicle's on-board sensor data with the infrastructure data. In the future, this data could even be used to enhance autonomous vehicles' perception. This paper highlights the achievements in data fusion of state-of-the-art and novel infrastructure sensor data and their planned use for the vehicle-to-infrastructure communication.

Sensor-based gait analysis is a valuable tool in diagnosis and assessment of Parkinson's disease. Especially for large data sets, efficient analysis pipelines are required. Pre-segmentation of long time series into chunks of interest is a possible approach to increase efficiency. Therefore, we developed an unsupervised algorithm for the detection of gait sequences from continuous sensor signals. In the proposed method, gyroscope signals representing the angular rate of the feet are analyzed in the frequency domain using moving windows. A gait sequence was detected, if the frequency spectrum of a given window contained harmonic frequencies. The approach was tested on two data sets that differed in the ratio of clinical gait and cyclic movement tests. Sensitivity in both data sets was higher than 99% in a stride-to-stride comparison with ground truth. The specificity was measured with 76.1% (data set 1) and 94.5% (data set 2) for tests against sequences of other cyclic movements. In conclusion, the algorithm offers a reliable and efficient approach for the detection of gait sequences in time series data and is also promising for the application in long-term home-monitoring scenarios.

We present a system capable of computing two major kinematic parameters that are necessary for a running shoe recommendation. The system consists of one inertial measurement unit located in each sole of a pair of running shoes. This unobtrusive integration allows for a long-term and objective assessment of the strike type and the pronation of the foot, which can be characterized by the sole angle and the range of motion respectively. An algorithm for computing these parameters is presented, which includes a sensor to shoe alignment, a step segmentation and a quaternion based angle calculation. A study including 5112 ground contacts from 27 subjects was conducted to evaluate the accuracy of the presented algorithm. The best results compared to a motion capture system can be achieved with a subject dependent sensor to shoe alignment with a mean absolute error of 2.8° ± 2.5° for the sole angle and 2.0° ± 1.9° for the range of motion.

Digital gait measures derived from wearable inertial sensors have been shown to support the treatment of patients with motor impairments. From a technical perspective, the detection of left and right initial foot contacts (ICs) is essential for the computation of stride-by-stride outcome measures including gait asymmetry. However, in a majority of studies only one sensor close to the center of mass is used, complicating the assignment of detected ICs to the respective foot. Therefore, we developed an algorithm including supervised machine learning (ML) models for the robust classification of left and right ICs using multiple features from the gyroscope located at the lower back. The approach was tested on a data set including 40 participants (ten healthy controls, ten hemiparetic, ten Parkinson’s disease, and ten Huntington’s disease patients) and reached an accuracy of 96.3% for the overall data set and up to 100.0% for the Parkinson’s sub data set. These results were compared to a state-of-the-art algorithm. The ML approaches outperformed this traditional algorithm in all subgroups. Our study contributes to an improved classification of left and right ICs in inertial sensor signals recorded at the lower back and thus enables a reliable computation of clinically relevant mobility measures. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.