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Motion Analysis Lab
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The Motion Analysis Lab is dedicated to researching new rehabilitative tools in the treatment of mobility-limiting conditions. We are working on more than two dozen studies focused on treating people with cerebral palsy, stroke, traumatic brain injury, spinal cord injury, Parkinson’s Disease, and other neuromuscular disorders.
For more information about these studies, please contact Chiara Mancinelli at 617-952-6336.
We are designing a large touchscreen system featuring novel interactive games to provide children with cerebral palsy an effective and entertaining format that will encourage repetition of therapeutic motions during rehabilitation. The system uses a Microsoft Surface multi-touch device mounted in a custom-built frame allowing the unit to be raised and tilted toward the user. This facilitates use by children of all ages as well as those in wheelchairs. The project is in collaboration with Chia Shen at Harvard University and Jim Niemi at the Wyss Institute. Technical support is being provided by John Campbell, Chief Information Officer at Spaulding. The project is being funded, in part, by the Alden Trust and the Peabody Foundation.
We are studying the effect of combining transcranial Direct Current Stimulation (tDCS) and motor training in a cohort of Traumatic Brain Injury (TBI) survivors. Our patients receive tDCS while undertaking motor training delivered via a robotic system with virtual reality modules. Subjects are randomly assigned to one of two treatment conditions: active tDCS and sham tDCS (control). We assess functional outcomes from the treatment via functional motor tests with the robotic system, along with questionnaires. The robotic system used in the study to deliver motor training therapy is the ARMEO robotic system (Hocoma AG, Switzerland). The robot is combined with a gaming environment (shown on a computer screen) designed to achieve functional movements, i.e. the subject has to perform tasks such as reaching for objects as part of the game. The project is being funded by the Center for Integration of Medicine and Innovative Technology.
We’re seeking to demonstrate the technical efficacy of an unobtrusive, wearable, in-shoe gait monitoring device (Active Gait, Simbex) to capture accurate data on foot mechanics in children with cerebral palsy (CP). We classify motor tasks on the basis of in-shoe data for a CP population using data mining/clustering algorithms. We also explore the development of a Toe Walking Severity (TWS) index from available biomechanical and Active Gait data for different ambulatory conditions. The device and tools we are developing are expected to help in the long-term monitoring of children with CP who undergo rehabilitative interventions. This is an NIH-sponsored project being done in collaboration with Simbex LCC, Lebanon, NH.
Contractures or the inability to perform the full range of motion of a joint, are common after a burn injury. As many as one-third of burn patients have at least one large joint contracture at the time of hospital discharge. Burn clinicians are compelled to find novel ways to treat contractures. Our team has recently turned its attention to the use of interactive gaming to implement rehabilitation interventions. The ARMEO system, in particular, allows clinicians to control a patient’s range of movement and to encourage subjects to increase the range over time. We hypothesize that a new gaming system aimed specifically at patients undergoing burn rehabilitation will be a useful rehabilitation tool that will enable clinicians to achieve clinical goals of facilitating the performance of a given set of movements. Our system is composed of an exoskeleton-based device (the Armeo by Hocoma AG) that provides mechanical support to the arm involved in the rehabilitation exercises. This study is being conducted in collaboration with Jeff Schneider, Physiatrist and Medical Director of the Musculoskeletal Program at Spaulding and was originally supported by Harvard Catalyst.
We’re working on a project that will combine home robots with wireless sensor networks to detect falls at home. Body sensors and ambient sensors may help clinicians detect a fall when it has occurred. An alert generated by the wireless sensor network would be relayed to the home robot, which would then respond to the alert by using its autonomous capabilities (e.g. processing of images, elaboration of sensors data) to assess the subject’s condition and send an alarm message to a remotely-located caregiver if necessary. The caregiver would be able to navigate the home environment using the robot and set in place the process needed to properly respond to the medical emergency. This is a collaborative study conducted with Bor-Rong Chen at the Wyss Institute. It has been funded by the Engineering for Neurologic Rehabilitation (ENR) infrastructure award from the National Institute of Health. Alessandro Puiatti, at the University of Applied Sciences of Southern Switzerland has contributed to its development.
Parkinson’s disease is the most common neuro-degenerative disease, affecting about 3% of the population over the age of 65 years. The main goal of this project is to use our wireless wearable sensor system (consisting of accelerometers and gyroscopes) to monitor motor fluctuations in patients with Parkinson’s disease. The focus is on patients with late-stage PD and motor fluctuations characterized by ON and OFF periods. Using data mining techniques we analyze data collected using wearable sensors and develop algorithms to predict clinical scores. In addition to successfully predicting clinical scores for the purpose of assessment, data mining has the potential of increasing our understanding of these disorders. This study is being done in collaboration with Ludy Shih at Beth Israel Deaconess Medical Center and Dan Tarsy, also at Beth Israel Deaconess Medical Center. The wearable sensor platform was developed with Matt Welsh previously at Harvard University and currently at Google. The platform is based on the SHIMMER sensor developed by Benjamin Kuris and Steve Ayer at Shimmer Research. This study has been supported by the Michael J. Fox Foundation and the National Institutes of Health.
The aim of this study is to investigate the effects of robotic therapy on the upper limbs of stroke patients by estimating the changes in muscle activity pre and post rehabilitation. To do that we record muscle activity and use it to estimate the muscle synergies of patients during simple reaching movements. In addition we correlate changes in muscle synergies with information about changes in the kinematics of movements obtained from data captured by the camera-based VICON system, before and after the therapy. This study is being run together with Emilio Bizzi’s lab at the McGovern Institute for Brain Research at MIT. The project has been funded by the National Institutes of Health.
Contractures or the inability to perform the full range of motion of a joint, are common after a burn injury. As many as one-third of burn patients have at least one large joint contracture at the time of hospital discharge. Burn clinicians are compelled to find novel ways to treat contractures. Our team has recently turned its attention to the use of interactive gaming to implement rehabilitation interventions. The ARMEO system, in particular, allows clinicians to control a patient’s range of movement and to encourage subjects to increase the range over time. We hypothesize that a new gaming system aimed specifically at patients undergoing burn rehabilitation will be a useful rehabilitation tool that will enable clinicians to achieve clinical goals of facilitating the performance of a given set of movements. Our system is composed of an exoskeleton-based device (the Armeo by Hocoma AG) that provides mechanical support to the arm involved in the rehabilitation exercises. This study is being conducted in collaboration with Jeff Schneider, Physiatrist and Medical Director of the Musculoskeletal Program at Spaulding and was originally supported by Harvard Catalyst.
We’re working on a project that will combine home robots with wireless sensor networks to detect falls at home. Body sensors and ambient sensors may help clinicians detect a fall when it has occurred. An alert generated by the wireless sensor network would be relayed to the home robot, which would then respond to the alert by using its autonomous capabilities (e.g. processing of images, elaboration of sensors data) to assess the subject’s condition and send an alarm message to a remotely-located caregiver if necessary. The caregiver would be able to navigate the home environment using the robot and set in place the process needed to properly respond to the medical emergency. This is a collaborative study conducted with Bor-Rong Chen at the Wyss Institute. It has been funded by the Engineering for Neurologic Rehabilitation (ENR) infrastructure award from the National Institute of Health. Alessandro Puiatti, at the University of Applied Sciences of Southern Switzerland has contributed to its development.
Parkinson’s disease is the most common neuro-degenerative disease, affecting about 3% of the population over the age of 65 years. The main goal of this project is to use our wireless wearable sensor system (consisting of accelerometers and gyroscopes) to monitor motor fluctuations in patients with Parkinson’s disease. The focus is on patients with late-stage PD and motor fluctuations characterized by ON and OFF periods. Using data mining techniques we analyze data collected using wearable sensors and develop algorithms to predict clinical scores. In addition to successfully predicting clinical scores for the purpose of assessment, data mining has the potential of increasing our understanding of these disorders. This study is being done in collaboration with Ludy Shih at Beth Israel Deaconess Medical Center and Dan Tarsy, also at Beth Israel Deaconess Medical Center. The wearable sensor platform was developed with Matt Welsh previously at Harvard University and currently at Google. The platform is based on the SHIMMER sensor developed by Benjamin Kuris and Steve Ayer at Shimmer Research. This study has been supported by the Michael J. Fox Foundation and the National Institutes of Health.
The aim of this study is to investigate the effects of robotic therapy on the upper limbs of stroke patients by estimating the changes in muscle activity pre and post rehabilitation. To do that we record muscle activity and use it to estimate the muscle synergies of patients during simple reaching movements. In addition we correlate changes in muscle synergies with information about changes in the kinematics of movements obtained from data captured by the camera-based VICON system, before and after the therapy. This study is being run together with Emilio Bizzi’s lab at the McGovern Institute for Brain Research at MIT. The project has been funded by the National Institutes of Health.