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Healing through motion: medicine and instructional design.

Summary of Literature

Motion Capture (MC) provides doctors and researchers with a new tool in gaining a better understanding of how the body works and maintaining its health. The program's most common role is a tool for the research and study into the limitations and potential of the body. But as engineers and developers work more with this system, new opportunities have emerged in the development of technologies and techniques that provide improvements in patient rehabilitation. As the process becomes more wide spread within the field, new areas of research and application are finding a use for it.

Understanding the Body Through Motion

MC has recently become a tool in medicine for its natural ability to study the body. Through this process, the complex movements of the fingers can be examined and studied for their importance. When applied to the study of the knee, the process helps to reveal its range of motion and aids in the development of more effective surgical replacements. By examining how humans walk, new opportunities emerge in a variety of areas including therapy and surgery.

The hand is a complex component of the human body, utilizing MC to break down each motion from the joints, bones, and muscles will provide researchers with a greater understanding of how it works. Using a system of markers placed on each major joint allows researchers to closely study what is required to form complex signs, positions, and exercises (Braido & Zhang 2004). This approach can be applied to measuring the importance of the sense of touch and how it affects the accuracy of task performances, such as writing or playing a piano (Goebl, W. and C. Palmer 2008). A further understanding of the importance of muscle groups and their influence on the motions of the hand can be obtained by utilizing MC and surgically manipulated cadaver arms (Nimbarte, Kaz, & Li, 2008).

In order to ensure that the range of motion and movements of the human knee are replicated, MC is often required to examine the factors that allow it to function. Using the process to closely examine the workings of the knee can help to ensure that the replacement can mimic the performance of the original (Victor et al., 2009).

Similarly, the use of the system can aid in the customization of the new prosthetic so that it will be tailored to fit the limitations of the patient without causing strain (Chen, Wechsler, Zhu, MacMahon, & Carmines). By using it as an alternative to surgery, doctors can monitor patients in search for the leading factors in Patellofemoral pain in the knee (Wilson, Press, Koh, Hendrix, & Li-Qun, 2009).

Walking is an important function of the body that is made up of numerous patterns and factors, but by careful examination of these patterns the trends behind the steps become clearer. Monitoring a basic walk through motion capture can be used to identify patterns and differences between men and women in their walking styles in different settings and situations (Troje, N. 2002). Additional data can be gathered by studying running and the effects of different environments and terrain on the hips, joints, knees and spine (Riley et. al., 2008). When this data is collected and compiled it can be used to generate better hip replacements that will be able to handle the different conditions that the body can place on it (Otake et al., 2008).

Perfecting Medical Technologies

When the body has been pushed past its limits, it sometimes requires the aid of technologies such as MC, robotics and kinetics to help keep it moving. Utilizing the system can ensure that the resources designed to aid the body are fulfilling their purpose without providing new problems in the process. Similarly it can be applied in the development of more advanced tools for the disabled, providing them with greater levels of freedom and confidence. Steps may also be taken to monitor common trends of movement to help identify the factors that have been the causes of reoccurring injuries.

The role of a wheelchair is to provide mobility to those with disabilities, however through the use of MC researchers can ensure that this device will not cause more harm than good to its users. With upwards of 65 percent of active wheelchair users reporting shoulder or joint related pain, doctors are recording users' movements to search for answers (Kotajarvi et. al., 2004). To help find answers for these injuries, sensors and markers are placed on the arms and shoulder to study the positions and methods used to propel the device (Lin, Su, Wu, & An, 2004). Further work to prevent such conditions looks at the posture of the user on different terrains and angles to test for possible signs of strain (Richter, Rodriguez, Woods, & Axelson, 2007).

The complex range of the body's motions makes the design of artificial limbs difficult, but by applying recording technologies, designers gain new insights in developing more effective and comfortable limbs. To create a more effective replacement limb, MC is applied to the developmental stages as a method of recording and measuring the ranges for the future prosthetic (Hauschild, Davoodi, & Loeb 2007). Taking additional data allows researchers to further design prosthetics, such as feet, to adjust and adapt to changes in weight to make walking easier when lifting heavier objects (Hansen & Childress, 2005). By examining the position of the ankle and foot as a person walks, new changes can be applied to replacement feet that will allow the user to wear a variety of different footwear comfortably (Hansen & Childress, 2004).

Injuries are a common occurrence within humans, particularly athletes; because of this trend it is important to understand the causes in order to prevent and aid in the recovery from such injuries. To help understand why hamstring pain is so commonplace in runners, MC is being used as one of several tools to closely examine how the body is moving and if those motions could be the source of the problem (Thelen et al., 2005). Similar methods are also being applied in the search for the reasons behind why stress fractures occur more in female runners than their male counterparts (Milner, Ferber, Pollard, Hamill, & Davis, 2006). Using joint positions and the cycle of movement made during rehabilitation sessions, physical therapists can construct a pain free and effective workout that provides better results in the recovery of the patient (Trumbower & Faghri, 2005).

Adaptive Medical Uses

Medical uses of MC should not be limited to just the operating room or laboratories, with proper application this program leaves space for growth in other medical avenues. Taking a closer look at the physical signs the mind can leave on the body can provide doctors with a valuable tool in combating and treating mental illnesses. While placing the system in sports medicine can help to ensure the safety and health of the athletes through preventive treatments. MC is a technology that is continually adapting, trying to develop new methods of recording to provide doctors and researchers with as much accurate information as possible.

Understanding the mindset and moods of a patient requires a well trained eye and experience, but through advances in motion capture the creation of a new system of psychological examination is possible. Researchers are currently looking into trends of psychomotor activity and how the study of these motions can provide psychologists with new methods of diagnosing patients (Alessi, 2001). By placing subjects in motion recording devices or suits, doctors could monitor the trends and physical behaviors to look for psychical signs of depression, agitation, and even suicide (Van Laerhoven, Gellersen, & Malliaris, 2006). The process can also be applied in aiding the rehabilitation process by allowing the patients a greater sense of interaction and focus on their recovery (Weiss, Rand, Katz, & Kizony, 2004).

Keeping athletes healthy can be a deciding factor between a winning and losing season, using MC in sports medicine can allow coaches and trainers to ensure that their players will stay well throughout the season. This system has the ability to break down every move made by an athlete for a closer look at trends that may be the cause of common sports injuries (Hewett, Ford, Myer, Wanstrath, & Scheper, 2006). From this information they can monitor the effects of fatigue on the body and what it does to increase the chances of injury or a poorer performance on the field (Augustsson et. al., 2006). And because a large number of sports injuries are caused through improper technique, a close examination of their form will allow coaches and trainers to spot any flaws that could lead to injury (Aylward & Paradiso, 2007).

In order to make MC more accessible to the medical field, new adaptations are required to bring the process out of the studios and into the field. Taking the information acquired with the system and applying it with additional measurement technologies such as accelerometers, force plates, and gyroscopes can provide researchers with multiple levels of data on the body (Brodie, Walmsley, & Page, 2008). Efforts are also being made to combine the effects of both MC and accelerometers into one system, that would allow for a less complex and more data driven method of recording information without the need for cameras and tracking sensors (Barbieri, Farella, Benini, Ricco, & Acquaviva, 2004). Taking both these techniques one step further is the development of smart fabrics, that are capable of recording a person's vitals, position and movements (De Rossi et al., 2003).

Critical Evaluation

Critique of the Literature

The majority of the literature in this review provides a great variety in the multiple uses and applications that motion capture can play in medicine. The majority of the readings go into detail on how MC is applied to the patients, including the positions of sensors, type of equipment used, number of subjects, etc. (Braido & Zhang, 2004). But soon a pattern emerges that while the articles go into great detail about what equipment is used and where it is placed, seldom does the research go into detail on how the MC data is used by itself. This leads into another major flaw, in the fact that MC is often just one set of many tools used, often times the data is being compiled together without knowing the individual results (Goldberg et. al., 2009). Probably the most difficult challenge to overcome in the articles is that the majority of the readings are all research and medical journals. Many of the articles refer to specific medical conditions and studies. The design of these articles is oriented more towards medical professionals or specialists to understand and not those lacking advanced medical backgrounds (Nakamura, Yamane, Fujita, & Suzuki, 2005). Overall the literature while providing its own series of challenges in the reading, performs its role in providing the reader with an wide background in the variety and uses of MC in medicine.

Possible Ties to Instructional Design

Instructional Design is an evolving field currently investigating issues relating to the learning sciences and advanced emergent technologies fields, e.g., games and simulations design (Almeida, 2010). Motion capture can certainly be seen as a tool for research and as an independent variable in the field of Instructional design in the 21st century. It has the potential to be used as a problem based learning tool, and for experimental studies in education reform (CarrChellman ,2006). Perhaps, scholars and practitioners of instructional design should be investigating the impact that motion capture labs have on individual's well-being (Ely, 1991), due to its potential impacts on subject's lives. Although the field has had serious definitions problems and ambiguity to a point of undergoing an identity crisis (Reigeluth, 1999), the ties to medical terminology and the use of motion capture will probably not affect, negatively, the field in terms of definitions and scope. It has, however, the potential to advance the field in the sense that it can be seen as a system (Almeida, 2010) and possible avenue for funding and research.

Future Research Questions

One of the most common areas of medical MC use that was addressed was the use of motion capture to create replacement limbs or joints. One area that should be explored further is testing of these replacement limbs in comparison to those without prosthetics. A study could be performed to discover whether these new customized parts are allowing patients the full range of motion that a normal body should have (Otake et al., 2008). Another avenue for research would be the development of additional methods and equipment to allow MC to become more easily accessible to hospitals. Because of the flaws related to the size and expense of MC, creating a more effective system would allow researchers more opportunities to study patients while in the hospital (Mundermann, Corazza, & Andriacchi, 2006). A final area that needs to be addressed is education's roles in medical training and MC. A look at how doctors and nurses are being trained or the lack of training they receive while in school in applying MC as treatment options.

--What are the differences in mobility between customized prosthetic replacements and generic prosthetic replacements?

--Are their significant changes in the patient's mobility when using motion capture to research customized prosthetic replacements?

--What steps are being taken to ensure that motion capture is more accessible to doctors and researchers?

--What current research in motion capture is focused in the production of medical motion capture systems, fabrics and recording devices ?

References

Alessi, N. (2001). "Is there a future for depression digital motion constructs in psychiatry?" Cyberpsychology & Behavior: The Impact Of The Internet, Multimedia And Virtual Reality On Behavior And Society 4(4): 457-463.

Almeida, L. (2010). [TEXT NOT REPRODUCIBLE IN ASCII] (Empowering gifted students to learn through game design). Journal of Digital Games Research, 4(2), 33-38.

Augustsson, J., ThomeAe, R., LindAen, R., Folkesson, M., Tranberg, R., & Karlsson, J. (2006). Single-leg hop testing following fatiguing exercise: reliability and biomechanical analysis. Scandinavian Journal of Medicine & Science in Sports. 16: 111-120.

Aylward, R., & Paradiso, J. (2007). A compact, high-speed, wearable sensor network for biomotion capture and interactive media, ACM.

R. Barbieri, E. Farella, L. Benini, B. Ricco, and A. Acquaviva. A low-power motion capture system with integrate accelerometers (gesture recognition applications). In Proc. of the IEEE consumer communications and networking conference, pages 418{423. IEEE, January 2004.

Braido, P., & Zhang, X. (2004). "Quantitative analysis of finger motion coordination in hand manipulative and gestic acts." Human movement science 22(6): 661-678.

Brodie, M., Walmsley, A., & Page, W. (2008). "Fusion motion capture: a prototype system using inertial measurement units and GPS for the biomechanical analysis of ski racing." Sports Technology 1(1): 17-28.

Busso, C., Bulut, M., Lee, C-C., Kazemzadeh, A., Mower, E., Kim, S., Chang, J., Lee, S., & Narayanan, S. (2008). IEMOCAP: interactive emotional dyadic motion capture database. Language Resources & Evaluation. 42: 335-359.

Carr-Chellman, A. (2006). User-Design book. Sage Publications. New York, NY.

Chen, J., Wechsler, H., Zhu, Y., MacMahon, E.B., & Carmines, D.V. "Knee Surgery Assistance: Patient Model Reconstruction, Motion Simulation, and Biomechanical Visualization." submitted to IEEE Transactions on Biomedical Engineering. MG 3.

De Rossi, D., Carpi, F., Lorussi, F., Mazzoldi, A., Paradiso, R., Scilingo, EP., & Tognetti, A. (2003). "Electroactive fabrics and wearable biomonitoring devices." AUTEX Research Journal 3(4): 180-185.

Geroch, M. (2004). "Motion capture for the rest of us." Journal of Computing Sciences in Colleges 19(3): 157-164.

Goebl, W., & Palmer, C. (2008). "Tactile feedback and timing accuracy in piano performance." Experimental Brain Research 186(3): 471-479.

Goldberg, S. R., Kepple, T. M., Stanhope, S. J. (2009). "In situ calibration and motion capture transformation optimization improve instrumented treadmill measurements." Journal Of Applied Biomechanics 25(4): 401-406.

Hansen, A., & Childress, D. (2004). "Effects of shoe heel height on biologic rollover characteristics during walking." Journal of rehabilitation research and development 41(4): 547-554.

Hansen, A., & Childress, D. (2005). "Effects of adding weight to the torso on roll-over characteristics of walking." Journal of rehabilitation research and development 42(3): 381.

Hauschild, M., Davoodi, R., & Loeb, GE. (2007). "A virtual reality environment for designing and fitting neural prosthetic limbs." IEEE Transactions on Neural Systems and Rehabilitation Engineering 15(1): 9.

Hewett, T., Ford, K. Myer, G.D., Wanstrath, K., & Scheper, M. (2006). "Gender differences in hip adduction motion and torque during a single-leg agility maneuver." Journal of orthopaedic research 24(3): 416-421.

Kotajarvi, B., Sabick, M., An, K.N., Zhao, K.D., Kaufman, K.R., & Basford, J.R. (2004). "The effect of seat position on wheelchair propulsion biomechanics." Journal of rehabilitation research and development 41(3B): 403-414.

Lin, H.-T., F.-C. Su, Wu, H.-W., & An, K.-N. (2004). "Muscle forces analysis in the shoulder mechanism during wheelchair propulsion." Proceedings Of The Institution Of Mechanical Engineers. Part H, Journal Of Engineering In Medicine 218(4): 213-221.

Ma, Y., Paterson, H. M., & Pollick, F. E. (2006). "A motion capture library for the study of identity, gender, and emotion perception from biological motion." Behavior Research Methods 38(1): 134-141.

Milner, C., Ferber, R., Pollard, C.D., Hamill, J., & Davis, I.S. (2006). "Biomechanical factors associated with tibial stress fracture in female runners." Medicine & Science in Sports & Exercise 38(2): 323.

Mundermann, L., Corazza, S., & Andriacchi, T.P. (2006). "The evolution of methods for the capture of human movement leading to marker less motion capture for biomechanical applications." Journal of NeuroEngineering and Rehabilitation 3: 6-6.

Nakamura, Y., Yamane, K., Fujita, Y., Suzuki, I. (2005). "Somatosensory computation for man-machine interface from motion-capture data and musculoskeletal human model." IEEE Transactions on Robotics 21(1): 58-66.

Nimbarte, A. D., Kaz, R., & Li, Z-M. (2008). "Finger joint motion generated by individual extrinsic muscles: A cadaveric study." Journal Of Orthopedic Surgery And Research 3: 27-27.

Otake, Y., Suzuki, N., Hattori, A., Miki, H., Yamamura, M., Yonenobu, K., Ochi, T., & Sugano, N. (2008). "Hip motion analysis using multi-phase (virtual and physical) simulation of the patient-specific hip joint dynamics." Studies In Health Technology And Informatics 132: 339-344.

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Richter, W., Rodriguez, R., Woods, K.R., & Axelson, P.W. (2007). "Stroke pattern and handrim biomechanics for level and uphill wheelchair propulsion at self-selected speeds." Archives of physical medicine and rehabilitation 88(1): 81-87.

Riley, P., Dicharry, J., Franz, J.,Croce, U., Wilder, R.P., & Kerrigan, D.C. (2008). "A kinematics and kinetic comparison of overground and treadmill running." Medicine & Science in Sports & Exercise 40(6): 1093.

Thelen, D., Chumanov, E., Hoerth, D.M., Best, T.M., Swanson, S.C., Li, L., Young, M., & Heiderscheit, B.C. (2005). "Hamstring muscle kinematics during treadmill sprinting." Medicine & Science in Sports & Exercise 37(1): 108.

Troje, N. (2002). "Decomposing biological motion: A framework for analysis and synthesis of human gait patterns." Journal of Vision 2(5): 371-387.

Trumbower, R. D. & Faghri, P.D. (2005). Kinematic analyses of semireclined leg cycling in able-bodied and spinal cord injured individuals. Spinal Cord, Nature Publishing Group. 43: 543-549.

Van Laerhoven, K., Gellersen, H., & Malliaris, YG. (2006). Long-term activity monitoring with a wearable sensor node, Citeseer.

Victor, J., F. Van Glabbeek, F., Vander Sloten, J., Parizel, P. M., Somville, J., & Bellemans, J (2009). "An Experimental Model for Kinematic Analysis of the Knee." Journal of Bone & Joint Surgery, American Volume 91-A: 150-163.

Weiss, P., Rand, D., Katz, N., & Kizony, R. (2004). "Video capture virtual reality as a flexible and effective rehabilitation tool." Journal of NeuroEngineering and Rehabilitation 1(1): 12.

Wilson, N. A., Press, J. M., Koh, J. L., Hendrix, R. W., & Li-Qun, Z. (2009). In Vivo Noninvasive Evaluation of Abnormal Patellar Tracking During Squatting in Patients with Patellofemoral Pain. Journal of Bone & Joint Surgery, American Volume. 91-A: 558-566.

Luis C. Almeida. Ph.D.

Department of Communications Media

Indiana University of Pennsylvania

Chris Juengel

Department of Communications Media

Indiana University of Pennsylvania
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Author:Almeida, Luis C.; Juengel, Chris
Publication:The Proceedings of the Laurel Highlands Communications Conference
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2011
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