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Automatic depression assessment based on visual cues is a rapidly growing research domain. The present exhaustive review of existing approaches as reported in over sixty publications during the last ten years focuses on image processing and machine learning algorithms. Visual manifestations of depression, various procedures used for data collection, and existing datasets are summarized. The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies. A quantitative meta-analysis of reported results, relying on performance metrics robust to chance, is included, identifying general trends and key unresolved issues to be considered in future studies of automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.

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Heart Beat Rate calculation has traditionally been conducted using specialized hardware most commonly in the form of pulse oximeters or Electrocardiogram devices. Even though these methods offer high reliability, they require the users to have special sensor to measure their heart rate. In this paper we propose a system capable of estimating the heart beat rate using just a camera from a commercially available mobile phone. The advantage of this method is that the user does not need specialized hardware and s/he can take a measurement in virtually any place under almost any circumstances. Moreover the measurement provided can be used as a tool for health coaching applications or effective telecare services aimed in enhancing the user's well being.