journal article·2022-06-17Open access

Smart Phone-Based Motion Capture and Analysis: Importance of Operating Envelope Definition and Application to Clinical Use

Ashley Chey Vincent, Haley Furman, Rebecca C. Slepian, Kaitlyn R. Ammann, Carson Di Maria, Jung Hung Chien, Ka-Chun Siu, Marvin J. Slepian

Applied Sciences· Volume 12· Issue 12

Abstract

Human movement is vital for life, with active engagement affording function, limiting disease, and improving quality; with loss resulting in disability; and the treatment and training leading to restoration and enhancement. To foster these endeavors a need exists for a simple and reliable method for the quantitation of movement, favorable for widespread user availability. We developed a Mobile Motion Capture system (MO2CA) employing a smart-phone and colored markers (2, 5, 10 mm) and here define its operating envelope in terms of: (1) the functional distance of marker detection (range), (2) the inter-target resolution and discrimination, (3) the mobile target detection, and (4) the impact of ambient illumination intensity. MO2CA was able to detect and discriminate: (1) single targets over a range of 1 to 18 ft, (2) multiple targets from 1 ft to 11 ft, with inter-target discrimination improving with an increasing target size, (3) moving targets, with minimal errors from 2 ft to 8 ft, and (4) targets within 1 to 18 ft, with an illumination of 100–300 lux. We then evaluated the utility of motion capture in quantitating regional-finger abduction/adduction and whole body–lateral flex motion, demonstrating a quantitative discrimination between normal and abnormal motion. Overall, our results demonstrate that MO2CA has a wide operating envelope with utility for the detection of human movements large and small, encompassing the whole body, body region, and extremity and digit movements. The definition of the effective operating envelope and utility of smart phone-based motion capture as described herein will afford accuracy and appropriate use for future application studies and serve as a general approach for defining the operational bounds of future video capture technologies that arise for potential clinical use.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Balance, Gait, and Falls Prevention · Stroke Rehabilitation and Recovery · Cerebral Palsy and Movement Disorders

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Medicine, Sarver Heart Center University of Arizona, Tucson, AZ 85724, USA

Department of Biomedical Engineering, College of Engineering, University of Arizona, Tucson, AZ 85724, USA

Arizona Center for Accelerated Biomedical Innovation, University of Arizona, Tucson, AZ 85724, USA

Department of Health & Rehabilitation Sciences, University of Nebraska Medical Center, Omaha, NE 68198, USA

Sarver Heart Center, Departments of Medicine and Biomedical Engineering, 1501 North Campbell Avenue, Bldg. 201E, Rm 5146, Tucson, AZ 85724, USA

Funding

ACABI—the Arizona Center for Accelerated Biomedical Innovation of the University of Arizona · NASA Nebraska Space Grant · UNeTECH Fund of the University of Nebraska Medical Center

References (47)

Show source-provided references
1. Midlife Physical Activity and Mobility in Older Age. The InCHIANTI Study10.1016/j.amepre.2006.05.005 ↗
2. A Review of the Effects of Physical Activity and Exercise on Cognitive and Brain Functions in Older Adults
3. Physical Activity and Quality of Life10.3961/jpmph.2013.46.S.S28 ↗
4. Pillay, S. (2021, August 05). How Simply Moving Benefits Your Mental Health. Available online: https://www.health.harvard.edu/blog/how-simply-moving-benefits-your-mental-health-201603289350.
5. Effects of Dance/Movement Training vs. Aerobic Exercise Training on Cognition, Physical Fitness and Quality of Life in Older Adults: A Randomized Controlled Trial10.1016/j.jbmt.2019.05.004 ↗
6. Bouchard, C., Shephard, R.J., and Stephens, T. (1994). Physical Activity, Fitness, and Health: The Model and Key Concepts. Physical Activity, Fitness, and Health: International Proceedings and Consensus Statement, Human Kinetic Publishers.
7. Goreczny, A.J. (1995). Exercise and Physical Activity. Handbook of Health and Rehabilitation Psychology, Spinger.10.1007/978-1-4899-1028-8 ↗
8. Physical Activity for Health: What Kind? How Much? How Intense? On Top of What?10.1146/annurev-publhealth-031210-101151 ↗
9. Kelley, G.A., and Kelley, K.S. (2015). Meditative Movement Therapies and Health-Related Quality-of-Life in Adults: A Systematic Review of Meta-Analyses. PLoS ONE, 10.10.1371/journal.pone.0129181 ↗
10. Physical Activity Guidelines for Americans From the US Department of Health and Human Services10.1161/CIRCOUTCOMES.118.005263 ↗
11. Lower-Extremity Function in Persons over the Age of 70 Years as a Predictor of Subsequent Disability10.1056/NEJM199503023320902 ↗
12. Lower Extremity Function and Subsequent Disability: Consistency across Studies, Predictive Models, and Value of Gait Speed Alone Compared with the Short Physical Performance Battery10.1093/gerona/55.4.M221 ↗
13. Evaluation of Kinect 3D Sensor for Healthcare Imaging10.1007/s40846-016-0184-2 ↗
14. Accuracy of Human Motion Capture Systems for Sport Applications; State-of-the-Art Review10.1080/17461391.2018.1463397 ↗
15. Current Low-Cost Video-Based Motion Analysis Options for Clinical Rehabilitation: A Systematic Review10.1093/ptj/pzz097 ↗
16. Development of a Mobile Motion Capture (MO 2 CA) System for Future Military Application10.1093/milmed/usy312 ↗
17. A Review of Vision-Based Motion Analysis in Sport10.2165/00007256-200838120-00006 ↗
18. Colyer, S.L., Evans, M., Cosker, D.P., and Salo, A.I.T. (2018). A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System. Sports Medicine—Open, Springer.10.1186/s40798-018-0139-y ↗
19. Kinematic and Kinetic Validation of an Improved Depth Camera Motion Assessment System Using Rigid Bodies10.1109/JBHI.2018.2872834 ↗
20. Comparative Abilities of Microsoft Kinect and Vicon 3D Motion Capture for Gait Analysis10.3109/03091902.2014.909540 ↗
21. Concurrent Validity and Reliability of 2D Kinematic Analysis of Frontal Plane Motion During Running
22. Bell, K., Onyeukwu, C., McClincy, M., Allen, M., Bechard, L., Mukherjee, A., Hartman, R., Smith, C., Lynch, A., and Irrgang, J. (2019). Verification of a Portable Motion Tracking System for Remote Management of Physical Rehabilitation of the Knee. Sensors, 19.10.3390/s19051021 ↗
23. Evaluation of a Two Dimensional Analysis Method as a Screening and Evaluation Tool for Anterior Cruciate Ligament Injury10.1136/bjsm.2005.018598 ↗
24. 3D Skeleton Model Derived from Kinect Depth Sensor Camera and Its Application to Walking Style Quality Evaluations
25. Accurate Realtime Full-Body Motion Capture Using a Single Depth Camera10.1145/2366145.2366207 ↗
26. Three-Dimensional Cameras and Skeleton Pose Tracking for Physcial Function Assessment: A Review of Uses, Validity, Current Developments and Kinect Alternatives10.1016/j.gaitpost.2018.11.029 ↗
27. Ceseracciu, E., Sawacha, Z., and Cobelli, C. (2014). Comparison of Markerless and Marker-Based Motion Capture Technologies through Simultaneous Data Collection during Gait: Proof of Concept. PLoS ONE, 9.10.1371/journal.pone.0087640 ↗
28. Moving Outside the Lab: Markerless Motion Capture Accurately Quantifies Sagittal Plane Kinematics during the Vertical Jump10.1016/j.jbiomech.2021.110547 ↗
29. Concurrent Assessment of Gait Kinematics Using Marker-Based and Markerless Motion Capture10.1016/j.jbiomech.2021.110665 ↗
30. Three-Dimensional Kinematics of the Lumbar Spine during Gait Using Marker-Based Systems: A Systematic Review10.3109/03091902.2016.1154616 ↗
31. Ammann, K.R., Ahamed, T., Sweedo, A.L., Ghaffari, R., Weiner, Y.E., Slepian, R.C., Jo, H., and Slepian, M.J. (2020). Human Motion Component and Envelope Characterization via Wireless Wearable Sensors. BMC Biomed. Eng., 2.10.1186/s42490-020-0038-4 ↗
32. Predicting Lower Limb Joint Kinematics Using Wearable Motion Sensors10.1016/j.gaitpost.2007.11.001 ↗
33. A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring10.3390/s100807772 ↗
34. Using Accelerometers to Measure Physical Activity in Large-Scale Epidemiological Studies: Issues and Challenges10.1136/bjsports-2013-093154 ↗
35. Application of Smart Bracelet to Monitor Frailty-Related Gait Parameters of Older Chinese Adults: A Preliminary Study10.1111/ggi.13492 ↗
36. Poitras, I., Dupuis, F., Bielmann, M., Campeau-Lecours, A., Mercier, C., Bouyer, L.J., and Roy, J.S. (2019). Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review. Sensors, 19.10.3390/s19071555 ↗
37. Glowinski, S., Obst, M., Majdanik, S., and Potocka-Banaś, B. (2021). Dynamic Model of a Humanoid Exoskeleton of a Lower Limb with Hydraulic Actuators. Sensors, 21.10.3390/s21103432 ↗
38. Accuracy of Computer-Assisted Navigation: Significant Augmentation by Facial Recognition Software10.1002/alr.21982 ↗
39. Detection and Identification Techniques for Markers Used in Computer Vision
40. Ojha, S., and Sakhare, S. (2015, January 8–10). Image Processing Techniques for Object Tracking in Video Surveillance—A Survey. Proceedings of the 2015 International Conference on Pervasive Computing (ICPC), Pune, India.10.1109/PERVASIVE.2015.7087180 ↗
41. Zhang, X., Fronz, S., and Navab, N. (2002, January 1). Visual Marker Detection and Decoding in AR Systems: A Comparative Study. Proceedings of the International Symposium on Mixed and Augmented Reality, Darmstadt, Germany.
42. (2021, March 20). iOS Device Compatibility Reference. Available online: https://developer.apple.com/library/archive/documentation/DeviceInformation/Reference/iOSDeviceCompatibility/Cameras/Cameras.html.
43. Mansurov, N. (2021, March 20). Camera Resolution Explained. Available online: https://photographylife.com/camera-resolution-explained.
44. Tominaga, S., Nishi, S., and Ohtera, R. (2021). Measurement and Estimation of Spectral Sensitivity Functions for Mobile Phone Cameras. Sensors, 21.10.3390/s21154985 ↗
45. Lubek, T. (2021, March 20). Video Depth of Field with iPhone: A Simple Beginners Guide. Available online: https://www.diyvideostudio.com/depth-of-field-with-iphone/.
46. (2021, March 20). How to Evaluate Camera Sensitivity. Available online: https://www.flir.com/discover/iis/machine-vision/how-to-evaluate-camera-sensitivity/.
47. Song, M.H., and Godøy, R.I. (2016). How Fast Is Your Body Motion? Determining a Sufficient Frame Rate for an Optical Motion Tracking System Using Passive Markers. PLoS ONE, 11.10.1371/journal.pone.0150993 ↗