Robotic and Wearable Sensor Technologies for Measurements/Clinical Assessments
暂无分享,去创建一个
Marc Bolliger | Olivier Lambercy | Roger Gassert | Serena Maggioni | Lars Lünenburger | R. Gassert | O. Lambercy | M. Bolliger | L. Lünenburger | Serena Maggioni
[1] Günther Deuschl,et al. Prevalence of gait disorders in hospitalized neurological patients , 2005, Movement disorders : official journal of the Movement Disorder Society.
[2] M. Morris,et al. Concurrent related validity of the GAITRite walkway system for quantification of the spatial and temporal parameters of gait. , 2003, Gait & posture.
[3] Jan F. Veneman,et al. The Effects on Kinematics and Muscle Activity of Walking in a Robotic Gait Trainer During Zero-Force Control , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[4] Marko Munih,et al. Infant trunk posture and arm movement assessment using pressure mattress, inertial and magnetic measurement units (IMUs) , 2014, Journal of NeuroEngineering and Rehabilitation.
[5] W. Rymer,et al. Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study , 2006, Journal of NeuroEngineering and Rehabilitation.
[6] W. Zijlstra,et al. Detection of gait and postures using a miniaturized triaxial accelerometer-based system: accuracy in patients with mild to moderate Parkinson's disease. , 2010, Archives of physical medicine and rehabilitation.
[7] E. Burdet,et al. Robot-assisted rehabilitation of hand function. , 2010, Current opinion in neurology.
[8] Corwin Boake,et al. Normalized Movement Quality Measures for Therapeutic Robots Strongly Correlate With Clinical Motor Impairment Measures , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[9] P. Dario,et al. Assessing Mechanisms of Recovery During Robot-Aided Neurorehabilitation of the Upper Limb , 2008, Neurorehabilitation and neural repair.
[10] Stefan Hesse,et al. Innovative gait robot for the repetitive practice of floor walking and stair climbing up and down in stroke patients , 2010, Journal of NeuroEngineering and Rehabilitation.
[11] P. Silburn,et al. Wearable Sensor Use for Assessing Standing Balance and Walking Stability in People with Parkinson’s Disease: A Systematic Review , 2015, PloS one.
[12] Mark Latt,et al. Reliability of the GAITRite walkway system for the quantification of temporo-spatial parameters of gait in young and older people. , 2004, Gait & posture.
[13] Kamiar Aminian,et al. Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. , 2002, Journal of biomechanics.
[14] V. Dietz,et al. Clinical assessments performed during robotic rehabilitation by the gait training robot Lokomat , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..
[15] P. Duncan,et al. Measurement of Motor Recovery After Stroke: Outcome Assessment and Sample Size Requirements , 1992, Stroke.
[16] Linda Denehy,et al. Validity of the Microsoft Kinect for assessment of postural control. , 2012, Gait & posture.
[17] Etienne Burdet,et al. Quantization of human motions and learning of accurate movements , 1998, Biological Cybernetics.
[18] B. Wünsche,et al. Assessment of movement quality in robot- assisted upper limb rehabilitation after stroke: a review , 2014, Journal of NeuroEngineering and Rehabilitation.
[19] Audny Anke,et al. Arm use in patients with subacute stroke monitored by accelerometry: association with motor impairment and influence on self-dependence. , 2011, Journal of rehabilitation medicine.
[20] Li-Qun Zhang,et al. Intelligent stretching of ankle joints with contracture/spasticity , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[21] P. Feys,et al. An overview of systematic reviews on upper extremity outcome measures after stroke , 2015, BMC Neurology.
[22] J. Allum,et al. Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. , 2006, Gait & posture.
[23] Olivier Lambercy,et al. Assessment-driven arm therapy at home using an IMU-based virtual reality system , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).
[24] Olivier Lambercy,et al. Assessment of upper limb motor function in patients with multiple sclerosis using the Virtual Peg Insertion Test: A pilot study , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).
[25] Chris Raymaekers,et al. Arm training in Multiple Sclerosis using Phantom: Clinical relevance of robotic outcome measures , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.
[26] Daniel Hamacher,et al. Towards clinical application: repetitive sensor position re-calibration for improved reliability of gait parameters. , 2014, Gait & posture.
[27] A. Mihailidis,et al. The development of an adaptive upper-limb stroke rehabilitation robotic system , 2011, Journal of NeuroEngineering and Rehabilitation.
[28] Isometric hip and knee torque measurements as an outcome measure in robot assisted gait training. , 2014, NeuroRehabilitation.
[29] S. Moore,et al. Validation of 24-hour ambulatory gait assessment in Parkinson's disease with simultaneous video observation , 2011, Biomedical engineering online.
[30] Chee Leong Teo,et al. A Haptic Knob for Rehabilitation of Hand Function , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[31] S. Engelborghs,et al. Monitoring of physical activity after stroke: a systematic review of accelerometry-based measures. , 2010, Archives of physical medicine and rehabilitation.
[32] Jeffrey M. Hausdorff,et al. Can an accelerometer enhance the utility of the Timed Up & Go Test when evaluating patients with Parkinson's disease? , 2010, Medical engineering & physics.
[33] AD Pandyan,et al. Spasticity: Clinical perceptions, neurological realities and meaningful measurement , 2005, Disability and rehabilitation.
[34] Gitendra Uswatte,et al. Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. , 2005, Archives of physical medicine and rehabilitation.
[35] J. Konczak,et al. Robot-Aided Assessment of Wrist Proprioception , 2015, Front. Hum. Neurosci..
[36] Preeti Raghavan,et al. Patterns of impairment in digit independence after subcortical stroke. , 2006, Journal of neurophysiology.
[37] Chee Leong Teo,et al. Post-stroke training of a pick and place activity in a virtual environment , 2008, 2008 Virtual Rehabilitation.
[38] J. Kofman,et al. Review of fall risk assessment in geriatric populations using inertial sensors , 2013, Journal of NeuroEngineering and Rehabilitation.
[39] Etienne Burdet,et al. A Robust and Sensitive Metric for Quantifying Movement Smoothness , 2012, IEEE Transactions on Biomedical Engineering.
[40] Olivier Lambercy,et al. Performance comparison of interaction control strategies on a hand rehabilitation robot , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).
[41] Olivier Lambercy,et al. The Virtual Peg Insertion Test as an assessment of upper limb coordination in ARSACS patients: A pilot study , 2014, Journal of the Neurological Sciences.
[42] Domenico Campolo,et al. Embedding inertial-magnetic sensors in everyday objects: assessing spatial cognition in children. , 2012, Journal of integrative neuroscience.
[43] Grigore C. Burdea,et al. The Rutgers Master II-new design force-feedback glove , 2002 .
[44] V. Parmar,et al. Menz HB, Latt MD, Tiedemann A, Mun San Kwan M, Lord SR. Reliability of the GAITRite walkway system for the quantification of temporo-spatial parameters of gait in young and older people. Gait & Posture 2004;20(1):20–25 , 2006 .
[45] R. Lyle. A performance test for assessment of upper limb function in physical rehabilitation treatment and research , 1981, International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation.
[46] Arno H. A. Stienen,et al. A Wrist and Finger Force Sensor Module for Use During Movements of the Upper Limb in Chronic Hemiparetic Stroke , 2009, IEEE Transactions on Biomedical Engineering.
[47] J Denoth,et al. The importance of posture on the isokinetic assessment of spasticity , 2002, Spinal Cord.
[48] S. Micera,et al. Robotic techniques for upper limb evaluation and rehabilitation of stroke patients , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[49] A. Fugl-Meyer,et al. The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. , 1975, Scandinavian journal of rehabilitation medicine.
[50] Antoinette Domingo,et al. Reliability and validity of using the Lokomat to assess lower limb joint position sense in people with incomplete spinal cord injury , 2014, Journal of NeuroEngineering and Rehabilitation.
[51] L. Carey,et al. Impaired limb position sense after stroke: a quantitative test for clinical use. , 1996, Archives of physical medicine and rehabilitation.
[52] L. Der-Yeghiaian,et al. Robot-based hand motor therapy after stroke. , 2007, Brain : a journal of neurology.
[53] E. Field-Fote,et al. The Spinal Cord Injury Functional Ambulation Inventory (SCI-FAI). , 2001, Journal of rehabilitation medicine.
[54] Robert Riener,et al. Robot-aided assessment of walking function based on an adaptive algorithm , 2015, 2015 IEEE International Conference on Rehabilitation Robotics (ICORR).
[55] D. Nowak,et al. Grip force control during object manipulation in cerebral stroke , 2003, Clinical Neurophysiology.
[56] F Honegger,et al. Trunk sway measures of postural stability during clinical balance tests: effects of a unilateral vestibular deficit. , 2001, Gait & posture.
[57] Olivier Lambercy,et al. Experimental Validation of a Rapid, Adaptive Robotic Assessment of the MCP Joint Angle Difference Threshold , 2014, EuroHaptics.
[58] T. Flash,et al. The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[59] I. Deary,et al. The dynamic relationship between cognitive function and walking speed: the English Longitudinal Study of Ageing , 2014, AGE.
[60] G. ÓLaighin,et al. Direct measurement of human movement by accelerometry. , 2008, Medical engineering & physics.
[61] Sally Bennett,et al. Interventions for sensory impairment in the upper limb after stroke. , 2010, The Cochrane database of systematic reviews.
[62] Yao-Jen Chang,et al. A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. , 2011, Research in developmental disabilities.
[63] Shyamal Patel,et al. Estimating fugl-meyer clinical scores in stroke survivors using wearable sensors , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[64] Roger Gassert,et al. Classification of Stair Ascent and Descent in Stroke Patients , 2014, 2014 11th International Conference on Wearable and Implantable Body Sensor Networks Workshops.
[65] J. Mehrholz,et al. Computerized Arm Training Improves the Motor Control of the Severely Affected Arm After Stroke: A Single-Blinded Randomized Trial in Two Centers , 2005, Stroke.
[66] Ilaria Carpinella,et al. Quantitative assessment of upper limb motor function in Multiple Sclerosis using an instrumented Action Research Arm Test , 2014, Journal of NeuroEngineering and Rehabilitation.
[67] L. Louca,et al. Upper Limb Assessment of People With Multiple Sclerosis With the Use of a Haptic Nine-Hole Peg-Board Test , 2008 .
[68] M. Popovic,et al. Relationship between clinical assessments of function and measurements from an upper-limb robotic rehabilitation device in cervical spinal cord injury , 2022 .
[69] Maarten J. IJzerman,et al. Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. , 2006, Journal of rehabilitation research and development.
[70] Joachim Hermsdörfer,et al. Objective evaluation of manual performance deficits in neurological movement disorders , 2006, Brain Research Reviews.
[71] A. Catz,et al. SCIM – spinal cord independence measure: a new disability scale for patients with spinal cord lesions , 1997, Spinal Cord.
[72] Robert Riener,et al. Assessment and training of synergies with an arm rehabilitation robot , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.
[73] John W Krakauer,et al. Improvement After Constraint-Induced Movement Therapy , 2013, Neurorehabilitation and neural repair.
[74] S. Olney,et al. Hemiparetic gait following stroke. Part I: Characteristics , 1996 .
[75] Marc Bolliger,et al. Standardized voluntary force measurement in a lower extremity rehabilitation robot , 2008, Journal of NeuroEngineering and Rehabilitation.
[76] Paolo Bonato,et al. Advances in wearable technology and applications in physical medicine and rehabilitation , 2005, Journal of NeuroEngineering and Rehabilitation.
[77] J. Bussmann,et al. Quantifying nonuse in chronic stroke patients: a study into paretic, nonparetic, and bimanual upper-limb use in daily life. , 2012, Archives of physical medicine and rehabilitation.
[78] J. Dewald,et al. Progressive Shoulder Abduction Loading is a Crucial Element of Arm Rehabilitation in Chronic Stroke , 2009, Neurorehabilitation and neural repair.
[79] G. Kwakkel,et al. Presence of Finger Extension and Shoulder Abduction Within 72 Hours After Stroke Predicts Functional Recovery: Early Prediction of Functional Outcome After Stroke: The EPOS Cohort Study , 2010, Stroke.
[80] Etienne Burdet,et al. Robotic assessment of hand function with the HapticKnob , 2010 .
[81] D. Bassett,et al. The technology of accelerometry-based activity monitors: current and future. , 2005, Medicine and science in sports and exercise.
[82] E. G. Cruz,et al. Weakness is the primary contributor to finger impairment in chronic stroke. , 2006, Archives of physical medicine and rehabilitation.
[83] K R Westerterp,et al. Wrist-worn accelerometers in assessment of energy expenditure during intensive training , 2012, Physiological measurement.
[84] P. Tang,et al. Analysis of impairments influencing gait velocity and asymmetry of hemiplegic patients after mild to moderate stroke. , 2003, Archives of physical medicine and rehabilitation.
[85] V. Dietz,et al. Biofeedback in gait training with the robotic orthosis Lokomat , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[86] Albert A. Rizzo,et al. Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[87] W. Zijlstra,et al. Detection of walking periods and number of steps in older adults and patients with Parkinson's disease: accuracy of a pedometer and an accelerometry-based method. , 2008, Age and ageing.
[88] A. Curt,et al. Prediction and Stratification of Upper Limb Function and Self-Care in Acute Cervical Spinal Cord Injury With the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP) , 2014, Neurorehabilitation and neural repair.
[89] Ninja P. Oess,et al. Design and evaluation of a low-cost instrumented glove for hand function assessment , 2012, Journal of NeuroEngineering and Rehabilitation.
[90] Joseph Classen,et al. Development and evaluation of a low-cost sensor glove for assessment of human finger movements in neurophysiological settings , 2009, Journal of Neuroscience Methods.
[91] E. Finch. Physical rehabilitation outcome measures : a guide to enhanced clinical decision making , 2002 .
[92] Changho Song,et al. Concurrent Validity and Test-retest Reliability of the OPTOGait Photoelectric Cell System for the Assessment of Spatio-temporal Parameters of the Gait of Young Adults , 2014, Journal of physical therapy science.
[93] H. Dawes,et al. Assessment of spatio-temporal gait parameters using inertial measurement units in neurological populations. , 2011, Gait & posture.
[94] Jeffrey M. Hausdorff. Gait variability: methods, modeling and meaning , 2005, Journal of NeuroEngineering and Rehabilitation.
[95] Robert Riener,et al. Robot-Assisted Arm Assessments in Spinal Cord Injured Patients: A Consideration of Concept Study , 2015, PloS one.
[96] Dagmar Sternad,et al. Sensitivity of Smoothness Measures to Movement Duration, Amplitude, and Arrests , 2009, Journal of motor behavior.
[97] J. Krakauer,et al. Neurorehabilitation and Neural Repair Inter-individual Variability in the Capacity for Motor Recovery after Ischemic Stroke Neurorehabilitation and Neural Repair Additional Services and Information for Inter-individual Variability in the Capacity for Motor Recovery after Ischemic Stroke , 2022 .
[98] H. Rodgers,et al. Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies , 2014, Journal of NeuroEngineering and Rehabilitation.
[99] J S Rietman,et al. Stop using the Ashworth Scale for the assessment of spasticity , 2009, Journal of Neurology, Neurosurgery & Psychiatry.
[100] Jan Rueterbories,et al. Methods for gait event detection and analysis in ambulatory systems. , 2010, Medical engineering & physics.
[101] Steven C Cramer,et al. Robotics, motor learning, and neurologic recovery. , 2004, Annual review of biomedical engineering.
[102] V. Dietz,et al. Treadmill training of paraplegic patients using a robotic orthosis. , 2000, Journal of rehabilitation research and development.
[103] Jennifer A. Semrau,et al. Robotic Identification of Kinesthetic Deficits After Stroke , 2013, Stroke.
[104] T. Sinkjaer,et al. Spastic movement disorder: impaired reflex function and altered muscle mechanics , 2007, The Lancet Neurology.
[105] M. Levin. Interjoint coordination during pointing movements is disrupted in spastic hemiparesis. , 1996, Brain : a journal of neurology.
[106] Maja J. Mataric,et al. Automated administration of the Wolf Motor Function Test for post-stroke assessment , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.
[107] Hans Braun,et al. Assessing energy expenditure in male endurance athletes: validity of the SenseWear Armband. , 2011, Medicine and science in sports and exercise.
[108] Doo Han Yoo,et al. Effects of upper limb robot-assisted therapy in the rehabilitation of stroke patients , 2015, Journal of physical therapy science.
[109] Gordon Waddington,et al. Assessing proprioception: A critical review of methods , 2015, Journal of sport and health science.
[110] S. Scott,et al. Quantitative Assessment of Limb Position Sense Following Stroke , 2010, Neurorehabilitation and neural repair.
[111] V. Mathiowetz,et al. Adult norms for the Box and Block Test of manual dexterity. , 1985, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.
[112] Robert E. Kearney,et al. Characterizing coordination of grasp and twist in hand function of healthy and post-stroke subjects , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).
[113] S. Leonhardt,et al. A survey on robotic devices for upper limb rehabilitation , 2014, Journal of NeuroEngineering and Rehabilitation.
[114] H. Topka,et al. Deficits of predictive grip force control during object manipulation in acute stroke , 2003, Journal of Neurology.
[115] R. Gassert,et al. Upper limb assessment using a Virtual Peg Insertion Test , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.
[116] D. Wade,et al. Validity and reliability comparison of 4 mobility measures in patients presenting with neurologic impairment. , 2001, Archives of physical medicine and rehabilitation.
[117] R. Colombo,et al. Taking a Lesson From Patients' Recovery Strategies to Optimize Training During Robot-Aided Rehabilitation , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[118] J. Dewald,et al. Abnormal joint torque patterns in the paretic upper limb of subjects with hemiparesis , 2001, Muscle & nerve.
[119] C. Burgar,et al. Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. , 2002, Archives of physical medicine and rehabilitation.
[120] S Gilman,et al. Joint position sense and vibration sense: anatomical organisation and assessment , 2002, Journal of neurology, neurosurgery, and psychiatry.
[121] Ruzena Bajcsy,et al. Upper extremity 3‐dimensional reachable workspace analysis in dystrophinopathy using Kinect , 2015, Muscle & nerve.
[122] Gert Kwakkel,et al. Predicting Improvement in Gait After Stroke: A Longitudinal Prospective Study , 2005, Stroke.
[123] R. Kearney,et al. Intrinsic and reflex stiffness in normal and spastic, spinal cord injured subjects , 2001, Experimental Brain Research.
[124] V. Mathiowetz,et al. Adult Norms for the Nine Hole Peg Test of Finger Dexterity , 1985 .
[125] J. Verbunt,et al. Assessment of arm activity using triaxial accelerometry in patients with a stroke. , 2011, Archives of physical medicine and rehabilitation.
[126] David R Bassett,et al. Device-based monitoring in physical activity and public health research , 2012, Physiological measurement.
[127] A. Williams,et al. International classification of functioning, disability, and health: ICF-CY World Health Organization , 2013 .
[128] Richard R Neptune,et al. Step length asymmetry is representative of compensatory mechanisms used in post-stroke hemiparetic walking. , 2011, Gait & posture.
[129] P. Verschure,et al. Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation , 2010, Journal of NeuroEngineering and Rehabilitation.
[130] Volker Dietz,et al. Clinical algorithm for improved prediction of ambulation and patient stratification after incomplete spinal cord injury. , 2010, Journal of neurotrauma.
[131] D. Goble,et al. Proprioceptive Acuity Assessment Via Joint Position Matching: From Basic Science to General Practice , 2010, Physical Therapy.
[132] B Kirshner,et al. A methodological framework for assessing health indices. , 1985, Journal of chronic diseases.
[133] N. Hogan,et al. Movement Smoothness Changes during Stroke Recovery , 2002, The Journal of Neuroscience.
[134] F. Zajac,et al. Gait differences between individuals with post-stroke hemiparesis and non-disabled controls at matched speeds. , 2005, Gait & posture.
[135] S. K. Wee,et al. Effects of a robot-assisted training of grasp and pronation/supination in chronic stroke: a pilot study , 2011, Journal of NeuroEngineering and Rehabilitation.
[136] Kade Paterson,et al. Instrumenting gait assessment using the Kinect in people living with stroke: reliability and association with balance tests , 2015, Journal of NeuroEngineering and Rehabilitation.
[137] C. Winstein,et al. Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: evidence from the extremity constraint-induced therapy evaluation trial. , 2006, Archives of physical medicine and rehabilitation.
[138] K. Aminian,et al. Barcoding Human Physical Activity to Assess Chronic Pain Conditions , 2012, PloS one.
[139] A. Daffertshofer,et al. Generalizability of the Proportional Recovery Model for the Upper Extremity After an Ischemic Stroke , 2015, Neurorehabilitation and neural repair.
[140] P. van Vliet,et al. Reliability of measurements obtained with the modified Ashworth scale in the lower extremities of people with stroke. , 2002, Physical therapy.
[141] Maarten J. IJzerman,et al. Gait impairments in a group of patients with incomplete spinal cord injury and their relevance regarding therapeutic approaches using functional electrical stimulation. , 2005, Artificial organs.
[142] Dinesh John,et al. ActiGraph and Actical physical activity monitors: a peek under the hood. , 2012, Medicine and science in sports and exercise.
[143] Claude Ghez,et al. A robotic test of proprioception within the hemiparetic arm post-stroke , 2014, Journal of NeuroEngineering and Rehabilitation.
[144] A. Hof,et al. Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. , 2003, Gait & posture.
[145] T. Platz,et al. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. , 2015, The Cochrane database of systematic reviews.
[146] R. Riener,et al. Journal of Neuroengineering and Rehabilitation Open Access Biofeedback for Robotic Gait Rehabilitation , 2022 .
[147] S. Black,et al. The Fugl-Meyer Assessment of Motor Recovery after Stroke: A Critical Review of Its Measurement Properties , 2002, Neurorehabilitation and neural repair.
[148] B. Volpe,et al. Kinematic Robot-Based Evaluation Scales and Clinical Counterparts to Measure Upper Limb Motor Performance in Patients With Chronic Stroke , 2010, Neurorehabilitation and neural repair.
[149] Joachim Hermsdörfer,et al. Moving objects with clumsy fingers: how predictive is grip force control in patients with impaired manual sensibility? , 2003, Clinical Neurophysiology.
[150] S. Scott,et al. The independence of deficits in position sense and visually guided reaching following stroke , 2012, Journal of NeuroEngineering and Rehabilitation.
[151] P. Buckley,et al. Actigraphy Correctly Predicts Sleep Behavior in Infants Who Are Younger than Six Months, When Compared with Polysomnography , 2005, Pediatric Research.
[152] Joachim Hermsdörfer,et al. Grip force behavior during object manipulation in neurological disorders: Toward an objective evaluation of manual performance deficits , 2005, Movement disorders : official journal of the Movement Disorder Society.
[153] Michael L Boninger,et al. Outcome Measures for Gait and Ambulation in the Spinal Cord Injury Population , 2008, The journal of spinal cord medicine.
[154] Marc H Schieber,et al. Human finger independence: limitations due to passive mechanical coupling versus active neuromuscular control. , 2004, Journal of neurophysiology.
[155] T. Milner,et al. HandCARE: A Cable-Actuated Rehabilitation System to Train Hand Function After Stroke , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[156] D G Kamper,et al. Kinetic and kinematic workspaces of the index finger following stroke. , 2005, Brain : a journal of neurology.
[157] G R Johnson,et al. Outcome measures of spasticity. , 2002, European journal of neurology.
[158] R. Kearney,et al. Intrinsic and reflex contributions to human ankle stiffness: variation with activation level and position , 2000, Experimental Brain Research.
[159] Olivier Lambercy,et al. Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot , 2014, Journal of NeuroEngineering and Rehabilitation.
[160] Alessandra Mazzone,et al. Test–Retest Reliability of Robotic Assessment Measures for the Evaluation of Upper Limb Recovery , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[161] DE Wood,et al. Biomechanical approaches applied to the lower and upper limb for the measurement of spasticity: A systematic review of the literature , 2005, Disability and rehabilitation.
[162] D. Reinkensmeyer,et al. Review of control strategies for robotic movement training after neurologic injury , 2009, Journal of NeuroEngineering and Rehabilitation.
[163] Hermano I Krebs,et al. Robotic Measurement of Arm Movements After Stroke Establishes Biomarkers of Motor Recovery , 2014, Stroke.
[164] Yupeng Ren,et al. Effects of robot-guided passive stretching and active movement training of ankle and mobility impairments in stroke. , 2013, NeuroRehabilitation.
[165] M. Batavia,et al. The validity and reliability of the GAITRite system's measurements: A preliminary evaluation. , 2001, Archives of physical medicine and rehabilitation.
[166] Volker Dietz,et al. Restoration of sensorimotor functions after spinal cord injury. , 2014, Brain : a journal of neurology.
[167] H. van der Kooij,et al. Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[168] Martina Mancini,et al. Objective biomarkers of balance and gait for Parkinson's disease using body‐worn sensors , 2013, Movement disorders : official journal of the Movement Disorder Society.
[169] C. Hass,et al. Associations Between Cognitive and Gait Performance During Single- and Dual-Task Walking in People With Parkinson Disease , 2014, Physical Therapy.
[170] W. Rymer,et al. Effect of robot-assisted and unassisted exercise on functional reaching in chronic hemiparesis , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[171] Richard W. Bohannon,et al. Interrater reliability of a modified Ashworth scale of muscle spasticity. , 1987, Physical therapy.
[172] Marko Munih,et al. Upper limb motion analysis using haptic interface , 2001 .
[173] George Georgoulas,et al. Intelligent data analysis of instrumented gait data in stroke patients - A systematic review , 2014, Comput. Biol. Medicine.
[174] W D Memberg,et al. Instrumented objects for quantitative evaluation of hand grasp. , 1997, Journal of rehabilitation research and development.
[175] Olivier Lambercy,et al. Neurocognitive Robot-Assisted Therapy of Hand Function , 2014, IEEE Transactions on Haptics.
[176] C. Qiu,et al. Walking speed, processing speed, and dementia: a population-based longitudinal study. , 2014, The journals of gerontology. Series A, Biological sciences and medical sciences.
[177] N. Hogan,et al. Submovements grow larger, fewer, and more blended during stroke recovery. , 2003, Motor control.
[178] Grant D. Huang,et al. Robot-assisted therapy for long-term upper-limb impairment after stroke. , 2010, The New England journal of medicine.
[179] Farshid Amirabdollahian,et al. Analysis of the results from use of haptic peg-in-hole task for assessment in neurorehabilitation , 2011 .
[180] Erienne V. Olesh,et al. Automated Assessment of Upper Extremity Movement Impairment due to Stroke , 2014, PloS one.
[181] V. Dietz,et al. Computerized Visual Feedback: An Adjunct to Robotic-Assisted Gait Training , 2008, Physical Therapy.
[182] R. Gassert,et al. A Cable Driven Robotic System to Train Finger Function After Stroke , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.
[183] Roger Gassert,et al. Low-power sensor module for long-term activity monitoring , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[184] H. Nagaraja,et al. How accurately does wrist actigraphy identify the states of sleep and wakefulness? , 2001, Sleep.
[185] Marc H Schieber,et al. Selective activation of human finger muscles after stroke or amputation. , 2009, Advances in experimental medicine and biology.
[186] H. Krebs,et al. Effects of Robot-Assisted Therapy on Upper Limb Recovery After Stroke: A Systematic Review , 2008, Neurorehabilitation and neural repair.
[187] Marco Santello,et al. Compensatory motor control after stroke: an alternative joint strategy for object-dependent shaping of hand posture. , 2010, Journal of neurophysiology.
[188] V. Dietz,et al. Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial , 2014, The Lancet Neurology.
[189] Panos Markopoulos,et al. Us'em: The user-centered design of a device for motivating stroke patients to use their impaired arm-hand in daily life activities , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[190] Rahsaan J. Holley,et al. Robotic Approaches for Rehabilitation of Hand Function After Stroke , 2012, American journal of physical medicine & rehabilitation.
[191] David J. Reinkensmeyer,et al. Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury , 2008, IEEE Transactions on Biomedical Engineering.