HEAT: The Harmony Exoskeleton Self - Assessment Test

Exoskeletons have tremendous potential to assist and enhance human abilities and movement, and they are projected for use in rehabilitation of patients with neurological, or muscular mobility disorders. Despite the commercial availability of exoskeletons and ongoing research, there are no clear guidelines for evaluating human factors and movement for human-exoskeleton interaction (HEI). We present a novel self-assessment tool for evaluating HEI building on methods from the performing arts, art-based rehabilitation therapy, social robotics and sports science. The Harmony Exoskeleton Self-Assessment Test (HEAT) is a subjective measurement tool that attempts to assess how a person feels when using an exoskeleton suit with an estimation based on a numerical score. The reliability of HEAT was tested in an artistic context, but is adaptable for a range of exoskeletons, clinical trials and experimental setups. Active upper-body exoskeletons were worn by forty-six healthy adult participants in a live performance and danced for nearly one hour. Factor analysis, and composite reliability tested the validity and reliability of this “first-time” exploratory assessment study, and results suggest that the HEAT is a potentially valuable tool for research.

[1]  Z. Dörnyei,et al.  A New Approach to Assessing Strategic Learning: The Case of Self-Regulation in Vocabulary Acquisition , 2006 .

[2]  Ann Burkhardt,et al.  Occupational Therapy as a Means to Wellness with the Elderly , 2001 .

[3]  Frank Krause,et al.  Exoskeletons for industrial application and their potential effects on physical work load , 2016, Ergonomics.

[4]  Sébastien Lê,et al.  FactoMineR: An R Package for Multivariate Analysis , 2008 .

[5]  Bin Ma,et al.  Designing of a Passive Knee-Assisting Exoskeleton for Weight-Bearing , 2017, ICIRA.

[6]  Nozer D. Singpurwalla,et al.  The notion of "composite reliability" and its hierarchical Bayes estimation , 1996 .

[7]  Dana Kulic,et al.  Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots , 2009, Int. J. Soc. Robotics.

[8]  Virpi Roto,et al.  User experience evaluation methods: current state and development needs , 2010, NordiCHI.

[9]  Mikko Rönkkö,et al.  Matrix-Based Partial Least Squares Estimation , 2015 .

[10]  Malcolm MacLachlan,et al.  Development and psychometric evaluation of the Trinity Amputation and Prosthesis Experience Scales (TAPES). , 2000 .

[11]  Stephen H. Scott,et al.  Robotic exoskeleton assessment of transient ischemic attack , 2017, PloS one.

[12]  Lee Burton,et al.  Pre-participation screening: the use of fundamental movements as an assessment of function - part 1. , 2006, North American journal of sports physical therapy : NAJSPT.

[13]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[14]  Michael Goldfarb,et al.  Design and preliminary assessment of Vanderbilt hand exoskeleton , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

[15]  I. Jolliffe Principal Component Analysis and Factor Analysis , 1986 .

[16]  W. L. Ooi,et al.  Dance/Movement Therapy with Older Adults Who Have Sustained Neurological Insult: A Demonstration Project , 1997 .

[17]  J. M. Brault,et al.  Rhythmic auditory stimulation in gait training for Parkinson's disease patients , 1996, Movement disorders : official journal of the Movement Disorder Society.

[18]  Sheng Quan Xie,et al.  Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. , 2012, Medical engineering & physics.

[19]  Rob Saunders,et al.  Creative Machine Performance: Computational Creativity and Robotic Art , 2013, ICCC.

[20]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[21]  Lee Burton,et al.  Pre-participation screening: the use of fundamental movements as an assessment of function - part 2. , 2006, North American journal of sports physical therapy : NAJSPT.

[22]  Jason W. Osborne,et al.  Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. , 2005 .

[24]  Takashi Maeno,et al.  Multi-fingered exoskeleton haptic device using passive force feedback for dexterous teleoperation , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Amy LaViers,et al.  Style-Based Robotic Motion in Contemporary Dance Performance , 2014 .

[26]  J. Veneman Design and evaluation of the gait rehabilitation robot LOPES , 2007 .

[27]  Michael Goldfarb,et al.  Performance evaluation of a lower limb exoskeleton for stair ascent and descent with Paraplegia , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Pier Giuseppe Rossi,et al.  Art , 2000, The Lancet.

[29]  J.C. Perry,et al.  Upper-Limb Powered Exoskeleton Design , 2007, IEEE/ASME Transactions on Mechatronics.

[30]  Yoon Su Baek,et al.  Design of a Knee Exoskeleton Using Foot Pressure and Knee Torque Sensors , 2015 .

[31]  Chang-Soo Han,et al.  Human–robot cooperation control based on a dynamic model of an upper limb exoskeleton for human power amplification , 2014 .

[32]  Stephen H. Scott,et al.  Robotic Assessment of Sensorimotor Deficits After Traumatic Brain Injury , 2012, Journal of neurologic physical therapy : JNPT.

[33]  John Hulland,et al.  Use of partial least squares (PLS) in strategic management research: a review of four recent studies , 1999 .

[34]  Craig R. Carignan,et al.  Development of an exoskeleton haptic interface for virtual task training , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[35]  W. Velicer,et al.  Comparison of five rules for determining the number of components to retain. , 1986 .