Advanced mood tracking using waveform statistical signal processing techniques

Tracking one's mood over time is useful for assessing one's response to life events and improving self-awareness and emotional regulation. It is also useful for diagnostic purposes for practitioners to determine the emotional stability of their patients using empirical techniques. Most mood trackers however tend to be extremely simplistic and are subject to the subjective analysis of whoever is assessing the results, leading to bias and inaccurate interpretations. Moreover, a manual human analysis is incapable of accurately detecting all patterns and trends in mood given enough data points, which may lead to critical insights being overlooked. Recent advancements in mood tracking mobile apps help alleviate this problem, but they vary widely in their data analysis techniques and tracking scale methodologies. The contents of this paper propose a more sophisticated theoretical framework, which treats mood data points over time as a time series waveform similar to what might be found from a signal in electronic systems. This framework allows standard and robust statistical signal processing techniques to be utilized and can be adapted to any particular numerical mood tracking scale. The calculations of the assessment data provide unbiased empirical analysis metrics which one can simply calculate from the data. When the tracker scale is mapped appropriately, this “mood signal” can also be analyzed in the frequency domain via a Fourier transform, which sheds light onto possible mood periodicities to better determine the source of undesirable behavioral patterns. All of these metrics together allow for a detailed, unbiased, entirely empirical analysis of mood as a function of time.