Sensor Measures of Symmetry Quantify Upper Limb Movement in the Natural Environment Across the Lifespan.

Knowledge of upper limb activity in the natural environment is critical for evaluating the effectiveness of rehabilitation services. Wearable sensors allow efficient collection of these data and have the potential to be less burdensome than self-report measures of activity. Sensors can capture many different variables of activity and daily performance, many of which could be useful in identifying deviation from typical movement behavior or measuring outcomes from rehabilitation interventions. Although it has potential, sensor measurement is just emerging, and there is a lack of consensus regarding which variables of daily performance are valid, sensitive, specific, and useful. We propose that symmetry of full-day upper limb movement is a key variable. We describe here that symmetry is valid, robustly observed within a narrow range across the lifespan in typical development, and shows evidence of being different in populations with neuromotor impairment. Key next steps include the determination of sensitivity, specificity, minimal detectable change, and minimal clinically important change/difference. This information is needed to determine whether an individual belongs to the typical or atypical group, whether change has occurred, and whether that change is beneficial.

[1]  R. Shephard Limits to the measurement of habitual physical activity by questionnaires , 2003, British journal of sports medicine.

[2]  Joanne M Wagner,et al.  Recovery of Grasp versus Reach in People with Hemiparesis Poststroke , 2006, Neurorehabilitation and neural repair.

[3]  Andrea C Tricco,et al.  A comparison of indirect versus direct measures for assessing physical activity in the pediatric population: a systematic review. , 2009, International journal of pediatric obesity : IJPO : an official journal of the International Association for the Study of Obesity.

[4]  Debbie Rand,et al.  Disparity Between Functional Recovery and Daily Use of the Upper and Lower Extremities During Subacute Stroke Rehabilitation , 2012, Neurorehabilitation and neural repair.

[5]  Joseph W. Klaesner,et al.  A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers , 2017, Journal of visualized experiments : JoVE.

[6]  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.

[7]  D. Corbetta,et al.  Seeing and touching: the role of sensory-motor experience on the development of infant reaching. , 2009, Infant behavior & development.

[8]  J. Eng,et al.  Predicting daily use of the affected upper extremity 1 year after stroke. , 2015, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[9]  Catherine E Lang,et al.  Upper-limb activity in adults: referent values using accelerometry. , 2013, Journal of rehabilitation research and development.

[10]  Ryanne J. M. Lemmens,et al.  Recognizing Complex Upper Extremity Activities Using Body Worn Sensors , 2015, PloS one.

[11]  Kenneth Meijer,et al.  Activity identification using body-mounted sensors—a review of classification techniques , 2009, Physiological measurement.

[12]  J. Eng,et al.  Exploring the Role of Accelerometers in the Measurement of Real World Upper-Limb Use After Stroke , 2015, Brain Impairment.

[13]  E Thelen,et al.  Lateral biases and fluctuations in infants' spontaneous arm movements and reaching. , 1999, Developmental psychobiology.

[14]  Weiyang Deng,et al.  Development of a Wearable Sensor Algorithm to Detect the Quantity and Kinematic Characteristics of Infant Arm Movement Bouts Produced across a Full Day in the Natural Environment. , 2017, Technologies.

[15]  Catherine E Lang,et al.  Acceleration metrics are responsive to change in upper extremity function of stroke survivors. , 2015, Archives of physical medicine and rehabilitation.

[16]  C. Lang,et al.  An Accelerometry-Based Methodology for Assessment of Real-World Bilateral Upper Extremity Activity , 2014, PloS one.

[17]  Fay B. Horak,et al.  Daily Quantity of Infant Leg Movement: Wearable Sensor Algorithm and Relationship to Walking Onset , 2015, Sensors.

[18]  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.

[19]  A. Domingo,et al.  Accuracy and precision of consumer-level activity monitors for stroke detection during wheelchair propulsion and arm ergometry , 2018, PloS one.

[20]  Catherine E. Lang,et al.  Validity of Body-Worn Sensor Acceleration Metrics to Index Upper Extremity Function in Hemiparetic Stroke , 2015, Journal of neurologic physical therapy : JNPT.

[21]  Steffen Ortmann,et al.  Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist. , 2014, Physiological measurement.

[22]  S. Sahrmann,et al.  Relationships between Sensorimotor Impairments and Reaching Deficits in Acute Hemiparesis , 2006, Neurorehabilitation and neural repair.

[23]  R. Ware,et al.  Development, and construct validity and internal consistency of the Grasp and Reach Assessment of Brisbane (GRAB) for infants with asymmetric brain injury. , 2016, Infant behavior & development.

[24]  Vicky Chan,et al.  Wearable sensing for rehabilitation after stroke: Bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

[25]  E. Taub,et al.  The pediatric motor activity log-revised: assessing real-world arm use in children with cerebral palsy. , 2012, Rehabilitation psychology.

[26]  C. Lang,et al.  Quantifying Real-World Upper-Limb Activity in Nondisabled Adults and Adults With Chronic Stroke , 2015, Neurorehabilitation and neural repair.

[27]  E. Taub,et al.  Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter. , 2000, Stroke.

[28]  C. Lang,et al.  Assessment of upper extremity impairment, function, and activity after stroke: foundations for clinical decision making. , 2013, Journal of hand therapy : official journal of the American Society of Hand Therapists.

[29]  K. Adamo,et al.  Self-report Pregnancy Physical Activity Questionnaire overestimates physical activity , 2015, Canadian Journal of Public Health.

[30]  M. Tremblay,et al.  A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review , 2008, The international journal of behavioral nutrition and physical activity.

[31]  Beth A. Smith,et al.  Relationships between full-day arm movement characteristics and developmental status in infants with typical development as they learn to reach: An observational study , 2018, Gates open research.

[32]  David J. Reinkensmeyer,et al.  The Manumeter: A non-obtrusive wearable device for monitoring spontaneous use of the wrist and fingers , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[33]  C. Lang,et al.  Does Task-Specific Training Improve Upper Limb Performance in Daily Life Poststroke? , 2017, Neurorehabilitation and neural repair.

[34]  M. T. J. Buñuales,et al.  La clasificación internacional del funcionamiento de la discapacidad y de la salud (CIF) 2001 , 2002 .

[35]  Beth A Smith,et al.  Kinematic characteristics of infant leg movements produced across a full day , 2017, Journal of rehabilitation and assistive technologies engineering.

[36]  Laurence Kenney,et al.  Visualisation of upper limb activity using spirals: A new approach to the assessment of daily prosthesis usage , 2018, Prosthetics and orthotics international.

[37]  G. Uswatte,et al.  Everyday movement and use of the arms: Relationship in children with hemiparesis differs from adults. , 2015, Journal of pediatric rehabilitation medicine.

[38]  A. Eliasson,et al.  Development of the Hand Assessment for Infants: evidence of internal scale validity , 2017, Developmental medicine and child neurology.

[39]  Tao Liu,et al.  Gait Analysis Using Wearable Sensors , 2012, Sensors.

[40]  C. Lang,et al.  Comparison of Self-Report Versus Sensor-Based Methods for Measuring the Amount of Upper Limb Activity Outside the Clinic. , 2018, Archives of physical medicine and rehabilitation.

[41]  Ryanne J. M. Lemmens,et al.  Valid and reliable instruments for arm-hand assessment at ICF activity level in persons with hemiplegia: a systematic review , 2012, BMC Neurology.

[42]  H. Rodgers,et al.  Accelerometer measurement of upper extremity movement after stroke: a systematic review of clinical studies , 2014, Journal of NeuroEngineering and Rehabilitation.