One of the main reasons for the high prevalence of mental disorders is that there is no technology to aid diagnosis or to report on recovery factors like effect of therapeutic interventions and medicines. To enable faster access to screening and to measure recovery, we propose a wearables-based framework for the automatic prediction of the states of anxiety, depression and calmness in individuals. The framework called H2SEC is based on the integrated measurements of Habituation, Hypoactivity, Synchronization, Experience and Calmness (H2SEC) through a unified framework. The framework also enables the tracking of these states in realtime, showing transition from one state of consciousness to another. To build and validate the H2SEC framework, we collected Electrodermal Activity (EDA) and Blood Volume Pulse (BVP) data, sampled at 100 Hz using a wearable device from 61 Neutral, 60 Depressed and 110 subjects with Anxiety, while they performed the experimental task. By tracking the timeseries data in centiseconds, we identified segments of time in the subjects’ data where their per-minute state of Sympathetic Nervous System (SNS) was lower compared to the previous minutes. Within these segments called Sympathetic Transition Points (STPs), we calculated scores related to the H2SEC parameters of Habituation, Hypoactivity, Synchronization, Experience and Calmness and compared them against similar scores from non-STP segments. We thus arrived at the physiological coordinates of the Neutral, Anxiety, and Depression groups, apart from identifying the intrinsic nature of calmness in each group. We implemented multi-output, multi-label machine learning (ML) algorithms to predict mental states(Neutral, Anxiety, and Depression) along with the nature of calmness (Calm, Approaching Calmness, and Not Calm) in each subject. We report 100% F1, along with 100% Precision and Recall in identifying both the states. Our methodology is the state-of-the-art in terms of mental health monitoring, and to the best of our knowledge we are the first to report on mental health disorders (anxiety and depression) and recovery mechanism (calmness) using an integrated methodology.