Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash 99 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 40 tok/s
GPT-5 High 38 tok/s Pro
GPT-4o 101 tok/s
GPT OSS 120B 470 tok/s Pro
Kimi K2 161 tok/s Pro
2000 character limit reached

Towards More Efficient Depression Risk Recognition via Gait (2310.06283v1)

Published 10 Oct 2023 in cs.CV

Abstract: Depression, a highly prevalent mental illness, affects over 280 million individuals worldwide. Early detection and timely intervention are crucial for promoting remission, preventing relapse, and alleviating the emotional and financial burdens associated with depression. However, patients with depression often go undiagnosed in the primary care setting. Unlike many physiological illnesses, depression lacks objective indicators for recognizing depression risk, and existing methods for depression risk recognition are time-consuming and often encounter a shortage of trained medical professionals. The correlation between gait and depression risk has been empirically established. Gait can serve as a promising objective biomarker, offering the advantage of efficient and convenient data collection. However, current methods for recognizing depression risk based on gait have only been validated on small, private datasets, lacking large-scale publicly available datasets for research purposes. Additionally, these methods are primarily limited to hand-crafted approaches. Gait is a complex form of motion, and hand-crafted gait features often only capture a fraction of the intricate associations between gait and depression risk. Therefore, this study first constructs a large-scale gait database, encompassing over 1,200 individuals, 40,000 gait sequences, and covering six perspectives and three types of attire. Two commonly used psychological scales are provided as depression risk annotations. Subsequently, a deep learning-based depression risk recognition model is proposed, overcoming the limitations of hand-crafted approaches. Through experiments conducted on the constructed large-scale database, the effectiveness of the proposed method is validated, and numerous instructive insights are presented in the paper, highlighting the significant potential of gait-based depression risk recognition.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (67)
  1. W. H. Organization, “Mental health,” https://www.who.int/health-topics/mental-health#tab=tab_1, Mar. 2023.
  2. I. of Health Metrics and Evaluation, “Global health data exchange (ghdx),” https://vizhub.healthdata.org/gbd-results/, Mar. 2023.
  3. S. H. Kennedy, B. S. Eisfeld, and R. G. Cooke, “Quality of life: an important dimension in assessing the treatment of depression?” Journal of psychiatry & neuroscience: JPN, vol. 26, no. Suppl, p. S23, 2001.
  4. B. N. Gaynes, B. J. Burns, D. L. Tweed, and P. Erickson, “Depression and health-related quality of life,” The Journal of nervous and mental disease, vol. 190, no. 12, pp. 799–806, 2002.
  5. R. Hirschfeld, S. A. Montgomery, M. B. Keller, S. Kasper, A. F. Schatzberg, M. Hans-Jurgen, D. Healy, D. Baldwin, M. Humble, and M. Versiani, “Social functioning in depression: a review,” Journal of Clinical Psychiatry, vol. 61, no. 4, pp. 268–275, 2000.
  6. L. Hansson, “Quality of life in depression and anxiety,” International Review of Psychiatry, vol. 14, no. 3, pp. 185–189, 2002.
  7. K. J. Bourassa, M. Memel, C. Woolverton, and D. A. Sbarra, “Social participation predicts cognitive functioning in aging adults over time: comparisons with physical health, depression, and physical activity,” Aging & mental health, vol. 21, no. 2, pp. 133–146, 2017.
  8. B. Roshanaei-Moghaddam, W. J. Katon, and J. Russo, “The longitudinal effects of depression on physical activity,” General hospital psychiatry, vol. 31, no. 4, pp. 306–315, 2009.
  9. K. Hawton, C. C. i Comabella, C. Haw, and K. Saunders, “Risk factors for suicide in individuals with depression: a systematic review,” Journal of affective disorders, vol. 147, no. 1-3, pp. 17–28, 2013.
  10. C. Center, M. Davis, T. Detre, D. E. Ford, W. Hansbrough, H. Hendin, J. Laszlo, D. A. Litts, J. Mann, P. A. Mansky et al., “Confronting depression and suicide in physicians: a consensus statement,” Jama, vol. 289, no. 23, pp. 3161–3166, 2003.
  11. J. L. Coulehan, H. C. Schulberg, M. R. Block, M. J. Madonia, and E. Rodriguez, “Treating depressed primary care patients improves their physical, mental, and social functioning,” Archives of Internal Medicine, vol. 157, no. 10, pp. 1113–1120, 1997.
  12. J. C. Coyne, S. Fechner-Bates, and T. L. Schwenk, “Prevalence, nature, and comorbidity of depressive disorders in primary care,” General hospital psychiatry, vol. 16, no. 4, pp. 267–276, 1994.
  13. A. Halfin, “Depression: the benefits of early and appropriate treatment,” American Journal of Managed Care, vol. 13, no. 4, p. S92, 2007.
  14. S. Weich, L. Morgan, M. King, and I. Nazareth, “Attitudes to depression and its treatment in primary care,” Psychological medicine, vol. 37, no. 9, pp. 1239–1248, 2007.
  15. D. Souery, P. Oswald, I. Massat, U. Bailer, J. Bollen, K. Demyttenaere, S. Kasper, Y. Lecrubier, S. Montgomery, A. Serretti et al., “Clinical factors associated with treatment resistance in major depressive disorder: results from a european multicenter study.” Journal of Clinical Psychiatry, vol. 68, no. 7, pp. 1062–1070, 2007.
  16. A. Akincigil and E. B. Matthews, “National rates and patterns of depression screening in primary care: results from 2012 and 2013,” Psychiatric services, vol. 68, no. 7, pp. 660–666, 2017.
  17. C. Barbui and M. Tansella, “Identification and management of depression in primary care settings. a meta-review of evidence,” Epidemiology and Psychiatric Sciences, vol. 15, no. 4, pp. 276–283, 2006.
  18. A. S. Young, R. Klap, C. D. Sherbourne, and K. B. Wells, “The quality of care for depressive and anxiety disorders in the united states,” Archives of general psychiatry, vol. 58, no. 1, pp. 55–61, 2001.
  19. R. C. Kessler, O. Demler, R. G. Frank, M. Olfson, H. A. Pincus, E. E. Walters, P. Wang, K. B. Wells, and A. M. Zaslavsky, “Prevalence and treatment of mental disorders, 1990 to 2003,” New England Journal of Medicine, vol. 352, no. 24, pp. 2515–2523, 2005.
  20. R. H. Belmaker and G. Agam, “Major depressive disorder,” New England Journal of Medicine, vol. 358, no. 1, pp. 55–68, 2008.
  21. J. F. Greden, “The burden of recurrent depression: causes, consequences, and future prospects,” Journal of Clinical Psychiatry, vol. 62, pp. 5–9, 2001.
  22. D. C. Mohr, S. L. Hart, I. Howard, L. Julian, L. Vella, C. Catledge, and M. D. Feldman, “Barriers to psychotherapy among depressed and nondepressed primary care patients,” Annals of Behavioral Medicine, vol. 32, no. 3, pp. 254–258, 2006.
  23. R. L. Kravitz, D. A. Paterniti, R. M. Epstein, A. B. Rochlen, R. A. Bell, C. Cipri, E. F. y Garcia, M. D. Feldman, and P. Duberstein, “Relational barriers to depression help-seeking in primary care,” Patient education and counseling, vol. 82, no. 2, pp. 207–213, 2011.
  24. P. Cassano and M. Fava, “Depression and public health: an overview,” Journal of psychosomatic research, vol. 53, no. 4, pp. 849–857, 2002.
  25. R. A. Bell, P. Franks, P. R. Duberstein, R. M. Epstein, M. D. Feldman, E. F. y Garcia, and R. L. Kravitz, “Suffering in silence: reasons for not disclosing depression in primary care,” The Annals of Family Medicine, vol. 9, no. 5, pp. 439–446, 2011.
  26. M.-J. Johnstone, “Stigma, social justice and the rights of the mentally ill: Challenging the status quo,” Australian and New Zealand Journal of Mental Health Nursing, vol. 10, no. 4, pp. 200–209, 2001.
  27. W. Moyle, “Unstructured interviews: challenges when participants have a major depressive illness,” Journal of advanced nursing, vol. 39, no. 3, pp. 266–273, 2002.
  28. K. Delbaere, J. C. Close, H. Brodaty, P. Sachdev, and S. R. Lord, “Determinants of disparities between perceived and physiological risk of falling among elderly people: cohort study,” Bmj, vol. 341, 2010.
  29. K. Rost, J. L. Smith, and M. Dickinson, “The effect of improving primary care depression management on employee absenteeism and productivity a randomized trial,” Medical care, vol. 42, no. 12, p. 1202, 2004.
  30. J. P. Docherty, “Barriers to the diagnosis of depression in primary care,” Journal of clinical psychiatry, vol. 58, no. 1, pp. 5–10, 1997.
  31. C. Dowrick and A. Frances, “Medicalising unhappiness: new classification of depression risks more patients being put on drug treatment from which they will not benefit,” bmj, vol. 347, 2013.
  32. N. Cheng and S. Mohiuddin, “Addressing the nationwide shortage of child and adolescent psychiatrists: determining factors that influence the decision for psychiatry residents to pursue child and adolescent psychiatry training,” Academic psychiatry, pp. 1–7, 2021.
  33. T. Butryn, L. Bryant, C. Marchionni, and F. Sholevar, “The shortage of psychiatrists and other mental health providers: causes, current state, and potential solutions,” International Journal of Academic Medicine, vol. 3, no. 1, pp. 5–9, 2017.
  34. T. Kanai, H. Takeuchi, T. A. Furukawa, R. Yoshimura, T. Imaizumi, T. Kitamura, and K. Takahashi, “Time to recurrence after recovery from major depressive episodes and its predictors,” Psychological Medicine, vol. 33, no. 5, pp. 839–845, 2003.
  35. C. Sobin and H. A. Sackeim, “Psychomotor symptoms of depression,” American Journal of Psychiatry, vol. 154, no. 1, pp. 4–17, 1997.
  36. D. Schrijvers, W. Hulstijn, and B. G. Sabbe, “Psychomotor symptoms in depression: a diagnostic, pathophysiological and therapeutic tool,” Journal of affective disorders, vol. 109, no. 1-2, pp. 1–20, 2008.
  37. E. I. Fried and R. M. Nesse, “The impact of individual depressive symptoms on impairment of psychosocial functioning,” PloS one, vol. 9, no. 2, p. e90311, 2014.
  38. K. Takakusaki, “Functional neuroanatomy for posture and gait control,” Journal of movement disorders, vol. 10, no. 1, p. 1, 2017.
  39. S. Walther, J. Bernard, V. Mittal, and S. Shankman, “The utility of an rdoc motor domain to understand psychomotor symptoms in depression,” Psychological medicine, vol. 49, no. 2, pp. 212–216, 2019.
  40. J. Michalak, N. F. Troje, J. Fischer, P. Vollmar, T. Heidenreich, and D. Schulte, “Embodiment of sadness and depression—gait patterns associated with dysphoric mood,” Psychosomatic medicine, vol. 71, no. 5, pp. 580–587, 2009.
  41. M. R. Lemke, T. Wendorff, B. Mieth, K. Buhl, and M. Linnemann, “Spatiotemporal gait patterns during over ground locomotion in major depression compared with healthy controls,” Journal of psychiatric research, vol. 34, no. 4-5, pp. 277–283, 2000.
  42. G. Nilsonne and F. E. Harrell Jr, “Eeg-based model and antidepressant response,” Nature Biotechnology, vol. 39, no. 1, pp. 27–27, 2021.
  43. D. M. Schnyer, P. C. Clasen, C. Gonzalez, and C. G. Beevers, “Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor mri measures in individuals with major depressive disorder,” Psychiatry Research: Neuroimaging, vol. 264, pp. 1–9, 2017.
  44. R. Ramasubbu, M. R. Brown, F. Cortese, I. Gaxiola, B. Goodyear, A. J. Greenshaw, S. M. Dursun, and R. Greiner, “Accuracy of automated classification of major depressive disorder as a function of symptom severity,” NeuroImage: Clinical, vol. 12, pp. 320–331, 2016.
  45. B. Vai, L. Parenti, I. Bollettini, C. Cara, C. Verga, E. Melloni, E. Mazza, S. Poletti, C. Colombo, and F. Benedetti, “Predicting differential diagnosis between bipolar and unipolar depression with multiple kernel learning on multimodal structural neuroimaging,” European Neuropsychopharmacology, vol. 34, pp. 28–38, 2020.
  46. M. Shim, M. J. Jin, C.-H. Im, and S.-H. Lee, “Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using p300 features,” NeuroImage: Clinical, vol. 24, p. 102001, 2019.
  47. H. Jiang, T. Popov, P. Jylänki, K. Bi, Z. Yao, Q. Lu, O. Jensen, and M. Van Gerven, “Predictability of depression severity based on posterior alpha oscillations,” Clinical Neurophysiology, vol. 127, no. 4, pp. 2108–2114, 2016.
  48. J. R. Sato, J. Moll, S. Green, J. F. Deakin, C. E. Thomaz, and R. Zahn, “Machine learning algorithm accurately detects fmri signature of vulnerability to major depression,” Psychiatry Research: Neuroimaging, vol. 233, no. 2, pp. 289–291, 2015.
  49. Y. Wei, Q. Chen, A. Curtin, L. Tu, X. Tang, Y. Tang, L. Xu, Z. Qian, J. Zhou, C. Zhu et al., “Functional near-infrared spectroscopy (fnirs) as a tool to assist the diagnosis of major psychiatric disorders in a chinese population,” European archives of psychiatry and clinical neuroscience, vol. 271, pp. 745–757, 2021.
  50. X. Zhou, K. Jin, Y. Shang, and G. Guo, “Visually interpretable representation learning for depression recognition from facial images,” IEEE transactions on affective computing, vol. 11, no. 3, pp. 542–552, 2018.
  51. X. Ma, H. Yang, Q. Chen, D. Huang, and Y. Wang, “Depaudionet: An efficient deep model for audio based depression classification,” in Proceedings of the 6th international workshop on audio/visual emotion challenge, 2016, pp. 35–42.
  52. L. Wang, H. Ning, T. Tan, and W. Hu, “Fusion of static and dynamic body biometrics for gait recognition,” IEEE Transactions on circuits and systems for video technology, vol. 14, no. 2, pp. 149–158, 2004.
  53. X. Li, Y. Makihara, C. Xu, Y. Yagi, S. Yu, and M. Ren, “End-to-end model-based gait recognition,” in Proceedings of the Asian conference on computer vision, 2020.
  54. W. Kusakunniran, Q. Wu, J. Zhang, and H. Li, “Support vector regression for multi-view gait recognition based on local motion feature selection,” in 2010 IEEE Computer society conference on computer vision and pattern recognition.   IEEE, 2010, pp. 974–981.
  55. K. Bashir, T. Xiang, and S. Gong, “Cross view gait recognition using correlation strength.” in Bmvc, 2010, pp. 1–11.
  56. Z. Wu, Y. Huang, L. Wang, X. Wang, and T. Tan, “A comprehensive study on cross-view gait based human identification with deep cnns,” IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 2, pp. 209–226, 2016.
  57. S. Yu, H. Chen, E. B. Garcia Reyes, and N. Poh, “Gaitgan: Invariant gait feature extraction using generative adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 30–37.
  58. H. Chao, Y. He, J. Zhang, and J. Feng, “Gaitset: Regarding gait as a set for cross-view gait recognition,” in Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 8126–8133.
  59. C. Song, Y. Huang, Y. Huang, N. Jia, and L. Wang, “Gaitnet: An end-to-end network for gait based human identification,” Pattern recognition, vol. 96, p. 106988, 2019.
  60. C. Fan, Y. Peng, C. Cao, X. Liu, S. Hou, J. Chi, Y. Huang, Q. Li, and Z. He, “Gaitpart: Temporal part-based model for gait recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 14 225–14 233.
  61. S. Hou, C. Cao, X. Liu, and Y. Huang, “Gait lateral network: Learning discriminative and compact representations for gait recognition,” in European conference on computer vision.   Springer, 2020, pp. 382–398.
  62. B. Lin, S. Zhang, and X. Yu, “Gait recognition via effective global-local feature representation and local temporal aggregation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 14 648–14 656.
  63. L. Sloman, M. Berridge, S. Homatidis, D. Hunter, and T. Duck, “Gait patterns of depressed patients and normal subjects.” The American journal of psychiatry, vol. 139, no. 1, pp. 94–97, 1982.
  64. T. Wang, C. Li, C. Wu, C. Zhao, J. Sun, H. Peng, X. Hu, and B. Hu, “A gait assessment framework for depression detection using kinect sensors,” IEEE Sensors Journal, vol. 21, no. 3, pp. 3260–3270, 2020.
  65. H. Lu, W. Shao, E. Ngai, X. Hu, and B. Hu, “A new skeletal representation based on gait for depression detection,” in 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM).   IEEE, 2021, pp. 1–6.
  66. J. Fang, T. Wang, C. Li, X. Hu, E. Ngai, B.-C. Seet, J. Cheng, Y. Guo, and X. Jiang, “Depression prevalence in postgraduate students and its association with gait abnormality,” IEEE Access, vol. 7, pp. 174 425–174 437, 2019.
  67. Y. Yuan, B. Li, N. Wang, Q. Ye, Y. Liu, and T. Zhu, “Depression identification from gait spectrum features based on hilbert-huang transform,” in Human Centered Computing: 4th International Conference, HCC 2018, Mérida, Mexico, December, 5–7, 2018, Revised Selected Papers 4.   Springer, 2019, pp. 503–515.
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Ai Generate Text Spark Streamline Icon: https://streamlinehq.com

Paper Prompts

Sign up for free to create and run prompts on this paper using GPT-5.

List Streamline Icon: https://streamlinehq.com Explore 10 Community Prompts
Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now