More specifically, we consider single sensor-based multi-modal biometrics from gait, head, and height modalities and focus on the sensor-based qualities of spatial resolution (SR), temporal resolution (TR) and sample-based quality of view, which all significantly affect the scores of the individual biometric modalities.
In this paper, we introduce a new approach for the integration of several sensor modalities and present a multi-modal, probability-based people detection and tracking system and its application using the different sensory systems of our mobile interaction robot Horos.
Higher penetration level, ease in testing and deployment, multiple connectivity modalities, and several built-in sensors motivated Park et al. to develop VoCell [3], a smartphone-based vehicular networking application framework.
We develop a sensor for each modality and organize the sensors as a parallel binary decision fusion architecture.
The sensor manager is not only responsible for recording data from different sensor modalities and for forwarding this data to the upload manager.
We describe the theoretical basis of the approach and present extensive experimental results from a variety of sensor modalities and environments.
Key works by O'Brien and Heneghan [11], de Chazal et al. [12], Ishijima [13], Park et al. [14], and Tarassenko et al. [1] highlight the importance of modeling noise and combining information from multiple ECG leads and sensor modalities to compensate for noisy measurements.
While these modalities rely on sensors and devices situated in the environment, there is a need for new modalities that enhance the mobile experience.
In many real world target monitoring applications like hostile territorial target monitoring [2], because of the diversity in target characteristics, different types (modalities) of sensors like video, audio, temperature, etc., are required.
"And sensors need to be tweaked regularly".
