Journal of Neuroscience Methods 231 (2014) 1–2
Contents lists available at ScienceDirect
Journal of Neuroscience Methods journal homepage: www.elsevier.com/locate/jneumeth
Editorial
Motion capture and associated novel measurement devices for movement function in humans and animal models
Aging leads to decreased function in movement, motivation, emotions, and memory as well as an increased amount of falls and syncope episodes. Although individuals with age-related neurological disorders often exhibit movement deficits, analytical, objectively quantifiable, methods for measurement of movement have been lacking. This was the rationale for developing this special issue for the Journal of Neuroscience Methods dedicated to novel methods to quantify movement, including 3-dimensional capture and analysis models or other technologically advanced computerized approaches to obtain quantifiable outcome measurements. With novel methods now available, but performed by very few research groups, a special issue on this topic seems timely. This special issue, therefore, focuses on motion capture, both in animal models and in humans, with special emphasis on analysis of movement function with aging and age-related neurodegenerative disorders. Despite the apparent clinical significance of movement dysfunction occurring with normal aging as well as neurological disorders or injuries, the techniques utilized to objectively measure such movement have remained fairly undeveloped until recently. For example, the most common neurological rating scales for assessment of motor dysfunction in Parkinson’s disease are still employing relatively primitive measures such as “finger tapping” and observation of walking pattern and speed without any sophisticated measurement apparatus (see e.g. Maetzler et al., 2009). Utilizing these traditional rating scales has several limitations, the most common being: (1) high variability of results between multisite locations due to differences in how the tests are carried out, (2) inability to design similar tests for animal models and humans, and, (3) inability to use these methods for prediction of disease or treatment progression, since improvement or deterioration cannot be modeled using current measuring techniques. Objective quantification of motor function becomes especially important in view of recent studies examining the effects of training on progression of Parkinson’s disease and other neurological disorders (Schenkman et al., 2012). A more detailed unbiased assessment of movement function can reveal more in depth the effects of training intervention studies. Indeed, quantitative and objective measurement of disease associated tremor, as an example, has been unobtainable with traditional techniques or devices. However, over the last ten years, several research groups have begun to revolutionize the movement disorder field by developing novel and more unbiased measurements for motor function. Some of this work we aspire to present collectively with this special issue. http://dx.doi.org/10.1016/j.jneumeth.2014.05.028 0165-0270/© 2014 Published by Elsevier B.V.
During the last few years, several devices have become available, or have been developed by scientists, in the movement disorder field. For example, a recent manuscript described the utilization of a wireless, wearable ultrasonic transmitter and several receivers that were fixed in known positions in order to measure 3-dimensional foot trajectory (Qi et al., 2014). A Radio Frequency (RF) module was used for wireless data transmission so that movement of the subjects studied was non-restricted. In the sports medicine world, devices that measure muscle performance have become more readily available than for medical conditions. An example of performance measurement was provided by Sinclair et al. (2014), who utilized a 10-camera motion capture system sampling at 500 Hz to explore ball velocity in high-performing soccer players who were attempting to score a goal. Three-dimensional kinematics of the lower extremity segments were assessed and regression analysis was used to identify the kinematic parameters associated with the development of ball velocity. The goal of this study was to identify movement parameters leading to the highest ball velocity, and the authors were able to demonstrate that knee extension was the highest predictor of ball velocity. These devices could easily be adapted to be used for assessment of disease progression and/or treatment benefits in neurological disorders or trauma associated with motor dysfunction. In this special issue, we have collected manuscripts focusing on a range of different devices and techniques for objective measurement of movement, ranging from more traditional motion capture systems adapted to be used in mouse models of movement disorders (Karakostas et al., 2013), to small devices for gait analyses modified to be carried around by humans (Gregory et al., 2014) or applications integrated in smart phones that allow registration and monitoring of physical activities (Antos et al., 2013). In Zhang et al. (2014, pp. 3–8), the investigators focused on delineating the nature of spasticity, which presents itself as one of the primary problems in diverse neurological conditions such as stroke, traumatic brain injury and multiple sclerosis (MS). The investigators utilized a joint driving device to apply digitally controlled perturbations to the knee in order to manifest the reflex and non-reflex properties of the joint in control subjects as well as those with MS. With the use of a nonlinear delay differential equation model they characterized spasticity (the increasing resistance to external motion) by investigating the reflexive and intrinsic properties of the knee in terms of its phasic stretch reflex and tonic stretch reflex properties, as well as the joint elastic stiffness and viscous components. The joint
2
Editorial / Journal of Neuroscience Methods 231 (2014) 1–2
coefficient of viscosity and tonic stretch reflex gain of the spastic MS patients were lower than those of normal controls, and from these findings the investigators concluded that joint elastic stiffness and viscosity in neurological disorders may be utilized to gain insight into the mechanisms underlying spasticity and could potentially be used for development of impairment-specific treatments in individuals with MS or other conditions involving spasticity. Another group of contributing to this special issue authors, Kang et al. (2014) have also worked on developing novel methods for the measurement of knee joint moments in real time. They modified a commercial elliptical by instrumenting it with potentiometermonitored foot plates which were also instrumented with load cells. Combined with the kinematic output from electrogoniometers, the load cell output allowed them to focus on the moments applied to the knee joint as a function of the dynamics, kinematics and kinetics, at the ankle joint while exercising on the elliptical. They implemented their approach to investigate the external knee joint varus moments in real time which have been found to be very damaging for the progression of osteoarthritis. This technology can be used for the investigation, or rehabilitation, of other neurological disorders such as cerebral palsy, traumatic brain injury, stroke, spinal cord injury or amyotrophic lateral sclerosis, because the instrumented foot plates of the elliptical can be used to provide controlled perturbations. Therefore, the system can be used to assess the ability of a patient to respond to perturbations as a function of the disease type and status, as well as train the patients’ ability to respond to perturbations. Gregory et al. (2014) focused their study on recent technological advancements that could allow researchers and clinicians to measure certain elements of human movement outside of the laboratory. These tools can then be used by researchers to integrate the “real world” based data with laboratory based findings (e.g. reverse translation) as well as with clinic-based exam findings, thus, facilitating clinical decision-making during treatment. Further, current application of these technologies seeks to investigate their utility for use outside of the lab and clinic to assess behaviors within real-world settings (e.g. home, work, community). Gregory and colleagues have focused specifically in gait analysis devices, and performed a market analysis of current existing methods employed in the laboratory setting, as well as how hand-held smaller devices used in the clinic or at home measure up in terms of reliably evaluating gait quantity and quality. Along similar lines, Antos et al. (2013) have focused on developing an application that can be integrated with the smart phones and can, subsequently convert a smart phone to an activity tracking device. Their application can track when a person walks, sits or stands, irrespective of where the phone is carried (pocket, bag, belt) as long as it carrying position does not change. This work is very important during the rehabilitation or during the post-rehabilitation stage that a patients is either sent home, with home exercise programs, or the focus is on functional re-integration in the community. Finally, Karakostas and Granholm et al. developed a threedimensional mathematical kinematic model in experimental rodent models to identify etiologies of age-related motor loss and accompanying pathologies secondary to neurodegeneration. The model also involved constructing a three-dimensional rodent motion capture model that was used by the kinematic model to analyze three-dimensional rotations and translations between the lower, upper and head body segments of the rodent using a set of segment coordinate systems. This approach allows a user friendly recording and analysis process of the rodent’s body motion because the respective segment rotations, and translations, are independent of the order they occur. The investigators demonstrated that
quantiative movement alterations that occur with normal aging in mice can be easily translated into clinical parameters for aging or neurodegenerative disease using this three-dimentional motion capture and analysis process. In conclusion, utilizing unbiased motor assessment instruments can not only enhance the outcome assessments of studies involving motor improvement or degeneration. These novel devices can also be directly involved in recovery of motor function in disease as well as injury-related damage. A recent manuscript by Possover (2014) demonstrated a surprising recovery of function in movement in patients with paraplegia, using a combination of functional electrical stimulation (FES)-assisted locomotor training and continuous low-frequency pelvic-lumbosacral neuromodulation. Others have utilized robotic locomotor training and found significant improvement in a patient with incomplete spinal cord lesion (Spiess et al., 2012). Collectively, the findings reported in this special issue are encouraging and suggest that devices designed for motor stimulation or measurement of motoric function, both in humans and animal models, have improved significantly and hold promise for future development of novel techniques designed for both intervention and progression studies. References Antos SA, Albert MV, Kording KP. Hand, belt, pocket, or bag; practical activity tracking with mobile phones. J Neurosci Methods 2014;231:22–30. Gregory CM, Perry LA, Embry AE, Hinson V, Bowden MG. Quantifying human movement across the continuum of care: from lab to clinic to community. J Neurosci Methods 2014;231:18–21. Kang SH, Ren Y, Zhang L-Q. Real-time tracking of knee adduction moment in patients with knee osteoarthritis. J Neurosci Methods 2014;231:9–17. Karakostas T, Hsiang S, Boger H, Middaugh L, Granholm AC. Three-dimensional rodent motion analysis and neurodegenerative disorders. J Neurosci Methods 2014;231:31–7. Maetzler W, Liepelt I, Berg D. Progression of Parkinson’s disease in the clinical phase: potential markers. Lancet Neurol 2009;8(December (12)):1158–71, http://dx.doi.org/10.1016/S1474-4422(09)70291-1, PMID: 19909914 [Review]. Possover M. Recovery of sensory and supraspinal control of leg movement in people with chronic paraplegia: a case series. Arch Phys Med Rehabil 2014;95(April (4)):610–4, http://dx.doi.org/10.1016/j.apmr.2013.10.030, PMID: 24269993 [Epub 2013 Nov 19]. Qi Y, Soh CB, Gunawan E, Low K. Ambulatory measurement of 3-dimensional foot displacement during treadmill walking using wearable wireless ultrasonic sensor network. IEEE J Biomed Health Inform 2014(April), PMID: 24759996 [Epub ahead of print]. Schenkman M, Hall DA, Barón AE, Schwartz RS, Mettler P, Kohrt WM. Exercise for people in early- or mid-stage Parkinson disease: a 16-month randomized controlled trial. Phys Ther 2012;92(November (11)):1395–410, http://dx.doi.org/10.2522/ptj.20110472, PMID: 22822237 [Epub 2012 Jul 19]. Sinclair J, Fewtrell D, Taylor PJ, Bottoms L, Atkins S, Hobbs SJ. Three-dimensional kinematic correlates of ball velocity during maximal instep soccer kicking in males. Eur J Sport Sci 2014;23(April):1–7, PMID: 24754671 [Epub ahead of print]. Spiess MR, Jaramillo JP, Behrman AL, Teraoka JK, Patten C. Unexpected recovery after robotic locomotor training at physiologic stepping speed: a single-case design. Arch Phys Med Rehabil 2012;93(August (8)):1476–84, http://dx.doi.org/10.1016/j.apmr.2012.02.030, PMID: 22446153 [Epub 2012 Mar 23]. Zhang L-Q, et al. Characterizations of reflex and nonreflex changes in spastic multiple sclerosis. J Neurosci Methods 2014;231:3–8.
a
Tasos Karakostas (MPT, PhD BEng.) a,b Motion Analysis Laboratory, Rehabilitation Institute of Chicago, Chicago, IL 60611, United States b Department of Orthopaedics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States Ann-Charlotte Granholm Department of Neurosciences and the Center on Aging, Medical University of South Carolina, Charleston, SC 29425, United States
Contents lists available at ScienceDirect
Journal of Neuroscience Methods journal homepage: www.elsevier.com/locate/jneumeth
Editorial
Motion capture and associated novel measurement devices for movement function in humans and animal models
Aging leads to decreased function in movement, motivation, emotions, and memory as well as an increased amount of falls and syncope episodes. Although individuals with age-related neurological disorders often exhibit movement deficits, analytical, objectively quantifiable, methods for measurement of movement have been lacking. This was the rationale for developing this special issue for the Journal of Neuroscience Methods dedicated to novel methods to quantify movement, including 3-dimensional capture and analysis models or other technologically advanced computerized approaches to obtain quantifiable outcome measurements. With novel methods now available, but performed by very few research groups, a special issue on this topic seems timely. This special issue, therefore, focuses on motion capture, both in animal models and in humans, with special emphasis on analysis of movement function with aging and age-related neurodegenerative disorders. Despite the apparent clinical significance of movement dysfunction occurring with normal aging as well as neurological disorders or injuries, the techniques utilized to objectively measure such movement have remained fairly undeveloped until recently. For example, the most common neurological rating scales for assessment of motor dysfunction in Parkinson’s disease are still employing relatively primitive measures such as “finger tapping” and observation of walking pattern and speed without any sophisticated measurement apparatus (see e.g. Maetzler et al., 2009). Utilizing these traditional rating scales has several limitations, the most common being: (1) high variability of results between multisite locations due to differences in how the tests are carried out, (2) inability to design similar tests for animal models and humans, and, (3) inability to use these methods for prediction of disease or treatment progression, since improvement or deterioration cannot be modeled using current measuring techniques. Objective quantification of motor function becomes especially important in view of recent studies examining the effects of training on progression of Parkinson’s disease and other neurological disorders (Schenkman et al., 2012). A more detailed unbiased assessment of movement function can reveal more in depth the effects of training intervention studies. Indeed, quantitative and objective measurement of disease associated tremor, as an example, has been unobtainable with traditional techniques or devices. However, over the last ten years, several research groups have begun to revolutionize the movement disorder field by developing novel and more unbiased measurements for motor function. Some of this work we aspire to present collectively with this special issue. http://dx.doi.org/10.1016/j.jneumeth.2014.05.028 0165-0270/© 2014 Published by Elsevier B.V.
During the last few years, several devices have become available, or have been developed by scientists, in the movement disorder field. For example, a recent manuscript described the utilization of a wireless, wearable ultrasonic transmitter and several receivers that were fixed in known positions in order to measure 3-dimensional foot trajectory (Qi et al., 2014). A Radio Frequency (RF) module was used for wireless data transmission so that movement of the subjects studied was non-restricted. In the sports medicine world, devices that measure muscle performance have become more readily available than for medical conditions. An example of performance measurement was provided by Sinclair et al. (2014), who utilized a 10-camera motion capture system sampling at 500 Hz to explore ball velocity in high-performing soccer players who were attempting to score a goal. Three-dimensional kinematics of the lower extremity segments were assessed and regression analysis was used to identify the kinematic parameters associated with the development of ball velocity. The goal of this study was to identify movement parameters leading to the highest ball velocity, and the authors were able to demonstrate that knee extension was the highest predictor of ball velocity. These devices could easily be adapted to be used for assessment of disease progression and/or treatment benefits in neurological disorders or trauma associated with motor dysfunction. In this special issue, we have collected manuscripts focusing on a range of different devices and techniques for objective measurement of movement, ranging from more traditional motion capture systems adapted to be used in mouse models of movement disorders (Karakostas et al., 2013), to small devices for gait analyses modified to be carried around by humans (Gregory et al., 2014) or applications integrated in smart phones that allow registration and monitoring of physical activities (Antos et al., 2013). In Zhang et al. (2014, pp. 3–8), the investigators focused on delineating the nature of spasticity, which presents itself as one of the primary problems in diverse neurological conditions such as stroke, traumatic brain injury and multiple sclerosis (MS). The investigators utilized a joint driving device to apply digitally controlled perturbations to the knee in order to manifest the reflex and non-reflex properties of the joint in control subjects as well as those with MS. With the use of a nonlinear delay differential equation model they characterized spasticity (the increasing resistance to external motion) by investigating the reflexive and intrinsic properties of the knee in terms of its phasic stretch reflex and tonic stretch reflex properties, as well as the joint elastic stiffness and viscous components. The joint
2
Editorial / Journal of Neuroscience Methods 231 (2014) 1–2
coefficient of viscosity and tonic stretch reflex gain of the spastic MS patients were lower than those of normal controls, and from these findings the investigators concluded that joint elastic stiffness and viscosity in neurological disorders may be utilized to gain insight into the mechanisms underlying spasticity and could potentially be used for development of impairment-specific treatments in individuals with MS or other conditions involving spasticity. Another group of contributing to this special issue authors, Kang et al. (2014) have also worked on developing novel methods for the measurement of knee joint moments in real time. They modified a commercial elliptical by instrumenting it with potentiometermonitored foot plates which were also instrumented with load cells. Combined with the kinematic output from electrogoniometers, the load cell output allowed them to focus on the moments applied to the knee joint as a function of the dynamics, kinematics and kinetics, at the ankle joint while exercising on the elliptical. They implemented their approach to investigate the external knee joint varus moments in real time which have been found to be very damaging for the progression of osteoarthritis. This technology can be used for the investigation, or rehabilitation, of other neurological disorders such as cerebral palsy, traumatic brain injury, stroke, spinal cord injury or amyotrophic lateral sclerosis, because the instrumented foot plates of the elliptical can be used to provide controlled perturbations. Therefore, the system can be used to assess the ability of a patient to respond to perturbations as a function of the disease type and status, as well as train the patients’ ability to respond to perturbations. Gregory et al. (2014) focused their study on recent technological advancements that could allow researchers and clinicians to measure certain elements of human movement outside of the laboratory. These tools can then be used by researchers to integrate the “real world” based data with laboratory based findings (e.g. reverse translation) as well as with clinic-based exam findings, thus, facilitating clinical decision-making during treatment. Further, current application of these technologies seeks to investigate their utility for use outside of the lab and clinic to assess behaviors within real-world settings (e.g. home, work, community). Gregory and colleagues have focused specifically in gait analysis devices, and performed a market analysis of current existing methods employed in the laboratory setting, as well as how hand-held smaller devices used in the clinic or at home measure up in terms of reliably evaluating gait quantity and quality. Along similar lines, Antos et al. (2013) have focused on developing an application that can be integrated with the smart phones and can, subsequently convert a smart phone to an activity tracking device. Their application can track when a person walks, sits or stands, irrespective of where the phone is carried (pocket, bag, belt) as long as it carrying position does not change. This work is very important during the rehabilitation or during the post-rehabilitation stage that a patients is either sent home, with home exercise programs, or the focus is on functional re-integration in the community. Finally, Karakostas and Granholm et al. developed a threedimensional mathematical kinematic model in experimental rodent models to identify etiologies of age-related motor loss and accompanying pathologies secondary to neurodegeneration. The model also involved constructing a three-dimensional rodent motion capture model that was used by the kinematic model to analyze three-dimensional rotations and translations between the lower, upper and head body segments of the rodent using a set of segment coordinate systems. This approach allows a user friendly recording and analysis process of the rodent’s body motion because the respective segment rotations, and translations, are independent of the order they occur. The investigators demonstrated that
quantiative movement alterations that occur with normal aging in mice can be easily translated into clinical parameters for aging or neurodegenerative disease using this three-dimentional motion capture and analysis process. In conclusion, utilizing unbiased motor assessment instruments can not only enhance the outcome assessments of studies involving motor improvement or degeneration. These novel devices can also be directly involved in recovery of motor function in disease as well as injury-related damage. A recent manuscript by Possover (2014) demonstrated a surprising recovery of function in movement in patients with paraplegia, using a combination of functional electrical stimulation (FES)-assisted locomotor training and continuous low-frequency pelvic-lumbosacral neuromodulation. Others have utilized robotic locomotor training and found significant improvement in a patient with incomplete spinal cord lesion (Spiess et al., 2012). Collectively, the findings reported in this special issue are encouraging and suggest that devices designed for motor stimulation or measurement of motoric function, both in humans and animal models, have improved significantly and hold promise for future development of novel techniques designed for both intervention and progression studies. References Antos SA, Albert MV, Kording KP. Hand, belt, pocket, or bag; practical activity tracking with mobile phones. J Neurosci Methods 2014;231:22–30. Gregory CM, Perry LA, Embry AE, Hinson V, Bowden MG. Quantifying human movement across the continuum of care: from lab to clinic to community. J Neurosci Methods 2014;231:18–21. Kang SH, Ren Y, Zhang L-Q. Real-time tracking of knee adduction moment in patients with knee osteoarthritis. J Neurosci Methods 2014;231:9–17. Karakostas T, Hsiang S, Boger H, Middaugh L, Granholm AC. Three-dimensional rodent motion analysis and neurodegenerative disorders. J Neurosci Methods 2014;231:31–7. Maetzler W, Liepelt I, Berg D. Progression of Parkinson’s disease in the clinical phase: potential markers. Lancet Neurol 2009;8(December (12)):1158–71, http://dx.doi.org/10.1016/S1474-4422(09)70291-1, PMID: 19909914 [Review]. Possover M. Recovery of sensory and supraspinal control of leg movement in people with chronic paraplegia: a case series. Arch Phys Med Rehabil 2014;95(April (4)):610–4, http://dx.doi.org/10.1016/j.apmr.2013.10.030, PMID: 24269993 [Epub 2013 Nov 19]. Qi Y, Soh CB, Gunawan E, Low K. Ambulatory measurement of 3-dimensional foot displacement during treadmill walking using wearable wireless ultrasonic sensor network. IEEE J Biomed Health Inform 2014(April), PMID: 24759996 [Epub ahead of print]. Schenkman M, Hall DA, Barón AE, Schwartz RS, Mettler P, Kohrt WM. Exercise for people in early- or mid-stage Parkinson disease: a 16-month randomized controlled trial. Phys Ther 2012;92(November (11)):1395–410, http://dx.doi.org/10.2522/ptj.20110472, PMID: 22822237 [Epub 2012 Jul 19]. Sinclair J, Fewtrell D, Taylor PJ, Bottoms L, Atkins S, Hobbs SJ. Three-dimensional kinematic correlates of ball velocity during maximal instep soccer kicking in males. Eur J Sport Sci 2014;23(April):1–7, PMID: 24754671 [Epub ahead of print]. Spiess MR, Jaramillo JP, Behrman AL, Teraoka JK, Patten C. Unexpected recovery after robotic locomotor training at physiologic stepping speed: a single-case design. Arch Phys Med Rehabil 2012;93(August (8)):1476–84, http://dx.doi.org/10.1016/j.apmr.2012.02.030, PMID: 22446153 [Epub 2012 Mar 23]. Zhang L-Q, et al. Characterizations of reflex and nonreflex changes in spastic multiple sclerosis. J Neurosci Methods 2014;231:3–8.
a
Tasos Karakostas (MPT, PhD BEng.) a,b Motion Analysis Laboratory, Rehabilitation Institute of Chicago, Chicago, IL 60611, United States b Department of Orthopaedics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States Ann-Charlotte Granholm Department of Neurosciences and the Center on Aging, Medical University of South Carolina, Charleston, SC 29425, United States
