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# Earkick Mental Health AI

##### Earkick's technology has [proven efficacy](https://blog.earkick.com/continued-use-of-earkick-for-mental-health/) and is taking a unique and novel approach that enables fine grained and quantifiable mental health monitoring and assessment to improve the user's mental health and quality of life.

##### To monitor mental health, we combine various sources of data, including insights from [AI online chat](https://chat.earkick.com/), to better understand the context in which a user is situated and how it potentially influences their mental health.

## The data that we're considering for mental health prediction is:

## Audio, video and text memo's and typing behavior when users describe their day, their mood and anxiety levels.

## Tasks performed by the user which go toward achieving their goals (e.g. sleeping more than 7 hrs).

## Sleep duration

## Exercises

## Meditations / Mindfulness minutes

## Heart Rate measurements

## Post-processed features such as voice pitch, jitter, speech rate, and in the visual domain, facial action units like the movement of eyebrows, eye gaze and eye blink rate

## Context information such as work, friends, exercise that led to how users are feeling.

## Symptoms that are of clinical use in diagnosis of mental health issues. For example a user may record a symptom like “Nervousness” or “Constantly worrying” which correspond to clinical assessment questions in forms like Generalized Anxiety Disorder 7.

## Menstrual cycle

## Location data, if the user permits us to use it.

## Weather data such as sunlight hours and cloud coverage if the user permits us to use location

## We extract features from timestamps of when people type.

## Panic attack time, location, duration, intensity and symptoms

## The novelty of our approach lies in:

### 01. Smart mental health assessment

The integration of numerous channels of information in an innovative way, where each channel provides a different perspective and additional value to solve the puzzle that is mental health assessment.

(Status: App based measurements completed).

### 02. Decode a user's emotional state

Use of latest machine learning models to decode a user’s emotional state, create features to predict outcomes such as anxiety levels, mood disorders, panic attacks and other irregularities in behavior.

(Status: First models are prepared).

### 03.Health exercises based on you

Recommending in-the-moment cognitive behavior therapy based mental health exercises informed by our emotion recognition and mood/anxiety prediction models.

(Status: upcoming feature).

### 04.Privatized learning

Built-in privatized learning that protects the users’ identity, because we do not collect email addresses or demographic data such as name, age, gender or location.

## Research

[\[1\]](https://openaccess.thecvf.com/content/CVPR2023W/ABAW/papers/Palotti_Analysis_of_Emotion_Annotation_Strength_Improves_Generalization_in_Speech_Emotion_CVPRW_2023_paper.pdf) J. Palotti, G. Narula, L. Raheem, H. Bay, 'Analysis of Emotion Annotation Strength Improves Generalization in Speech Emotion Recognition Models'. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR2023W

\[2\] D. A. Kalmbach, J. R. Anderson, and C. L. Drake, 'The impact of stress on sleep: Pathogenic sleep reactivity as a vulnerability to insomnia and circadian disorders', J. Sleep Res., vol. 27, no. 6, p. e12710, Dec. 2018, doi: 10.1111/jsr.12710.

\[3\] C. N. Kaufmann, R. Susukida, and C. A. Depp, 'Sleep apnea, psychopathology, and mental health care', Sleep Health, vol. 3, no. 4, pp. 244–249, Aug. 2017, doi: 10.1016/j.sleh.2017.04.003.

\[4\] J. C. Mundt, A. P. Vogel, D. E. Feltner, and W. R. Lenderking, 'Vocal acoustic biomarkers of depression severity and treatment response', Biol. Psychiatry, vol. 72, no. 7, pp. 580-587, Oct. 2012, doi: 10.1016/j.biopsych. 2012.03.015.

\[5\] L. Albuquerque, A. R. S. Valente, A. Teixeira, D. Figueiredo, P. Sa-Couto, and C. Oliveira, 'Association between acoustic speech features and non-severe levels of anxiety and depression symptoms across lifespan', PloS One, vol. 16, no. 4, p. e0248842, 2021, doi:10.1371/journal.pone.0248842.

\[6\] C. Solomon, M. F. Valstar, R. K. Morriss, and J. Crowe, 'Objective Methods for Reliable Detection of Concealed Depression', Front. ICT, vol. 2, 2015, Accessed: May 04, 2022. \[Online\].Available: https:/www.frontiersin.org/article/10.3389/fict.2015.00005

\[7\] 'Crema-D'. https://paperswithcode.com/dataset/crema-d

\[8\] S. R. Livingstone and F. A. Russo, 'The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English', PLOS ONE, vol. 13, no. 5, p. e0196391, May 2018, doi: 10.1371/journal.pone.0196391.

\[9\] A. Zadeh et al., 'Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph', in ACL 2018-56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 2018, vol. 1. doi: 10.18653/v1/p18-1208.

## Try Earkick today!

Track and improve your mental health in real time