A multi-modal human robot interaction framework based on cognitive behavioral therapy model

Rastogi, Neelesh; Keshtkar, Fazel; Miah, Suruz

doi:10.1145/3279963.3279968

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…There are several papers that use multimodal data to study signs of depression e.g., through multimodal communication with a social robot [33]. In [34], a survey of ML in mental health reveals that out of 54 research papers, few describe the conduct of empirical studies of an end-to-end ML system [35,36] or assess the quality of ML predictions.…”

Section: Related Workmentioning

confidence: 99%

An End-to-End Machine Learning Solution for Anxiety and Depressive Disorder Symptom Occurrence During COVID-19: A New York Case Study

Yadav¹,

Singh²,

Srivastava³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Anxiety and depression during the COVID-19 pandemic have heightened as evidenced by the rapidly growing corpus of research articles suggesting a link between the pandemic and mental health. This paper proposes a unique end-to-end user-centric machine learning (ML) architecture, capable of assessing the quality of ML predictions about the occurrence of anxiety and/or depression symptoms. A case study is presented using official New York State COVID-19 data, highlighting the plug-and-play capabilities of this architecture for both external features, and newer ML models. This is demonstrated through the formal design of a custom weighted clustering algorithm which outperforms conventional unsupervised techniques in grouping symptomatic cases. The ability to augment external sentiment data mined from social media platforms like Twitter, increases the predictive power of this architecture. This work serves as a blueprint to build a practical ML solution to better gauge the effect of future pandemic waves on mental health.

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Section: Related Workmentioning

confidence: 99%

An End-to-End Machine Learning Solution for Anxiety and Depressive Disorder Symptom Occurrence During COVID-19: A New York Case Study

Yadav¹,

Singh²,

Srivastava³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Papers that analyzed structured data (n = 10) included the evaluation of questionnaires (n = 7) and health records (n = 3). Several papers (n = 7) described complex multi-modal systems, or frameworks that built on everyday technology [ 82 , 140 , 193 , 222 ], robot [ 154 ], or human/virtual agent [ 155 , 184 ] interactions.…”

Section: Main Data Domains For ML In Mental Healthmentioning

confidence: 99%

“…Other examples include the detection of mood states from mobile sensing data [ 128 , 176 ], or phone typing dynamics [ 27 ] as well as stress assessments from location [ 218 ], biometrical and accelerometer data [ 67 ]. This is complemented by recent trends in analyzing human-robot [ 154 ] or agent interactions [ 155 ] to help assess peoples' mental health status. Furthermore, text analysis was performed to detect and automatically extract diagnostic information from written narratives or psychiatric records [ 45 ], while questionnaire data was studied to help differentiate between mental health states or diagnosis such as patients who experience bipolar I or bipolar II [ 61 ].…”

Section: Understanding Detecting and Diagnosis Of Mental Health Statusmentioning

confidence: 99%

“…Nevertheless, there may be a need for more explicit dialogue and efforts to nurture a clearer understanding for those from whom data is being collected and analyzed as to what constitutes the purpose of the data analysis, and what risks and benefits sharing their data may entail for the [ 4 ]; Alam et al [ 6 ]; Chang et al [ 31 ]; Chen et al [ 33 ]; DeMasi and Recht [ 44 ]; Diederich et al [ 45 ]; Fatima et al [ 57 ]; Frogner et al [ 62 ]; Galiatsatos et al [ 63 ]; Gjoreski et al [ 67 ]; Joshi et al [ 86 ]; Kavuluru et al [ 89 ]; Mallol-Ragolta et al [ 112 ]; Nguyen et al [ 133 ]; Nosakhare and Picard [ 135 ]; Panagiotakopoulos et al [ 139 ]; Patterson and Cloud [ 144 ]; Pestian et al [ 145 ]; Rastogi et al [ 154 ]; Ray et al [ 155 ]; Spathis et al [ 177 ]; Tavabi [ 184 ]; Tran et al [ 192 ]; Tsiakas et al [ 193 ]; Wilbourne et al [ 205 ] Reports of ethical approval/review exemption Institutional/Regional IRB Doryab et al [ 50 ]; Ernala et al [ 53 ]; Nobles et al [ 134 ]; Paredes et al [ 140 ]; Salekin et al [ 168 ]; Wang et al [ 201 ]; Yazdevar et al [ 211 ]; Zakaria et al [ 217 ] Re-use of data (e.g . , that previously received or was exempt from ethical approval)…”

Section: Autonomy (Including User Consent and Human Agency In Ml-infomentioning

confidence: 99%

“…Modeling multi-modal data of different signals, durations, densities; data fusion challenges Cao et al [ 27 ]; Mitra et al [ 122 ]; Morshed et al [ 128 ], Panagiotakopoulos et al [ 139 ]; Ray et al [ 155 ]; Rastogi et al [ 154 ]; Tran et al [ 192 ]; Zhou et al [ 222 ] Complex mapping of multi-label classification Ojeme and Mbogho [ 134 ] Limitations of ML modeling/ implications…”

Section: A Appendix A1 Examples Of Common ML Models or Techniques Inmentioning

confidence: 99%

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Machine Learning in Mental Health

Thieme

Belgrave

Doherty

2020

ACM Trans. Comput.-Hum. Interact.

183

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High prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression; and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. Aiming to guide future research and identify new directions for advancing development in this important domain, this article presents an introduction to, and a systematic review of, current ML work regarding psycho-socially based mental health conditions from the computing and HCI literature. A quantitative synthesis and qualitative narrative review of 54 papers that were included in the analysis surfaced common trends, gaps, and challenges in this space. Discussing our findings, we (i) reflect on the current state-of-the-art of ML work for mental health, (ii) provide concrete suggestions for a stronger integration of human-centered and multidisciplinary approaches in research and development, and (iii) invite more consideration of the potentially far-reaching personal, social, and ethical implications that ML models and interventions can have, if they are to find widespread, successful adoption in real-world mental health contexts.

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