Motion mapping in humans as a biomarker for psychiatric disorders

Measuring movement has been a cornerstone of studying behavior in animals in controlled studies.

Movement data have served as a primary window into behavior, and by proxy, social function, mood, and cognition [1]. In humans, variations in locomotor activity have served as markers of a range of psychiatric syndromes including major depression, bipolar disorder, anxiety, catatonia, and substance use disorders [2]. Reports of sensor-based motion measurement in humans date back to the 1950s [2], and motion-based models of human psychopathology have been key to developing animal models of psychopathology [1]. A vast literature has documented how these animal models have facilitated development of motion-based signatures of antidepressant, anxiolytic, and other drug effects and guided drug development [3]. However, the limitations of technology thus far have meant that motion-mapping in humans has remained largely restricted to experimental settings.

Phenotyping of naturalistic human behavior continues to depend on self-report or observer-report measures, with sampling often at intervals of hours to days. Newer technologies, supported by advances in wireless connectivity, more compact and reliable sensors and devices, and higher computing power can now sample movement at intervals of seconds, and can facilitate measurement of human motion in the natural living environment in ways previously not possible [4]. Such technologies are setting the stage for movement to become a major new phenotypic biomarker in neuropsychiatry.

A wide array of validated technologies can quantify motion. These range from active sensors that move along with the body (e.g., accelerometers, gyroscopes), passive “line of sight” technologies (e.g., cameras, thermal sensors, and lasers), passive technologies that can detect motion through walls (e.g., radio wave sensors) and positioning technologies (e.g., GPS, satellite imaging) [5]. It is now possible to track individual contractions of facial muscles (used in computer vision and analysis of micro emotions), complex motions such as smoking or drinking coffee [6] or even the movement of an individual across a global geographic field. Work in this new emerging field necessitates collaborative teams with a range of expertise that includes hardware development, signal processing, big data processing and analytics, machine learning, data visualization, and clinical implementation.

Early work in this field has demonstrated feasibility of translating this intensive sensor-based approach to clinical research. As an example, our group has demonstrated that by measuring how low frequency radio signals bounce off the human body and objects in a defined space (up to 800 sq.ft.), it is possible to elicit gait speed, gait patterns and spatial location. Using clinical correlation, we have established that these data can serve as markers of apathy, pacing, and disrupted circadian rhythms in patients with dementia [5].

A marker of the maturing of this field is recent NIH investment into major collaborative initiatives such as the MD2K initiative [7], which supports the acceleration of translating motion sensor data into validated biomarkers of behavior. While the need for privacy and security around sensor data is well recognized, innovative solutions around secure data storage and transfer will be crucial to this approach gaining widespread acceptance and use [4].

References

  1. 1.

    Henry BL, Minassian A, Young JW, Paulus MP, Geyer MA, Perry W. Cross-species assessments of motor and exploratory behavior related to bipolar disorder. Neurosci Biobehav Rev. 2010;34:1296–306. https://doi.org/10.1016/j.neubiorev.2010.04.002

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Teicher MH. Actigraphy and motion analysis: new tools for psychiatry. Harv Rev Psychiatry. 1995;3:18–35.

    CAS  Article  Google Scholar 

  3. 3.

    Tecott LH, Nestler EJ. Neurobehavioral assessment in the information age. Nat Neurosci. 2004;7:462–6. https://doi.org/10.1038/nn1225

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang GZ. Big data for health. IEEE J Biomed Health Inform. 2015;19:1193–208. https://doi.org/10.1109/JBHI.2015.2450362

    Article  PubMed  Google Scholar 

  5. 5.

    Collier S, Monette P, Hobbs K, Tabasky E, Forester BP, Vahia IV. Mapping movement: applying motion measurement technologies to the psychiatric care of older adults. Curr Psychiatry Rep. 2018;20:64 https://doi.org/10.1007/s11920-018-0921-z

    Article  PubMed  Google Scholar 

  6. 6.

    Shoaib M, Bosch S, Incel OD, Scholten H, Havinga PJ. Complex human activity recognition using smartphone and wrist-worn motion sensors. Sensors. 2016;16:426 https://doi.org/10.3390/s16040426

    Article  PubMed  Google Scholar 

  7. 7.

    Kumar S, Abowd G, Abraham WT, al’Absi M, Chau DHP, Ertin E, et al. Center of excellence for mobile sensor data-to-knowledge (MD2K). IEEE Pervasive Comput. 2017;16:18–22. https://doi.org/10.1109/MPRV.2017.29

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the contributions of Patrick Monette to the preparation of this manuscript.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ipsit V. Vahia.

Ethics declarations

Competing interests

Dr. Forester has received Research Grant Support from Eli Lilly, Biogen, Rogers Family Foundation and the National Institutes of Health; Dr. Vahia has received Research Grant Support from the Once Upon a Time Foundation, the Massachusetts Institute of Technology and the National Institutes of Health

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vahia, I.V., Forester, B.P. Motion mapping in humans as a biomarker for psychiatric disorders. Neuropsychopharmacol 44, 231–232 (2019). https://doi.org/10.1038/s41386-018-0205-7

Download citation

Further reading

Search