A Multi-Stage Human Factors and Comfort Assessment of Instrumented Insoles Designed for Use in a Connected Health Infrastructure

Wearable electronics are gaining widespread use as enabling technologies, monitoring human physical activity and behavior as part of connected health infrastructures. Attention to human factors and comfort of these devices can greatly positively influence user experience, with a subsequently higher likelihood of user acceptance and lower levels of device rejection. Here, we employ a human factors and comfort assessment methodology grounded in the principles of human-centered design to influence and enhance the design of an instrumented insole. A use case was developed and interrogated by stakeholders, experts, and end users, capturing the context of use and user characteristics for the instrumented insole. This use case informed all stages of the design process through two full design cycles, leading to the development of an initial version 1 and a later version 2 prototype. Each version of the prototype was subjected to an expert human factors inspection and controlled comfort assessment using human volunteers. Structured feedback from the first cycle of testing was the driver of design changes implemented in the version 2 prototype. This prototype was found to have significantly improved human factors and comfort characteristics over the first version of the prototype. Expert inspection found that many of the original problems in the first prototype had been resolved in the second prototype. Furthermore, a comfort assessment of this prototype with a group of young healthy adults showed it to be indistinguishable from their normal footwear. This study demonstrates the power and effectiveness of human factors and comfort assessment methodologies in influencing and improving the design of wearable devices.

[1]  Trudy van der Weijden,et al.  The Development of a Mobile Monitoring and Feedback Tool to Stimulate Physical Activity of People With a Chronic Disease in Primary Care: A User-Centered Design , 2013, JMIR mHealth and uHealth.

[2]  Anna-Lisa Osvalder,et al.  Exploring user background settings in cognitive walkthrough evaluation of medical prototype interfaces: a case study , 2005 .

[3]  Stacy J. Morris Bamberg,et al.  Instrumented insole vs. force plate: A comparison of center of plantar pressure , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  A. Mündermann,et al.  Development of a reliable method to assess footwear comfort during running. , 2002, Gait & posture.

[5]  Mirams Ruth,et al.  Human-Centred Design , 2014 .

[6]  A Kushniruk,et al.  Usability Methods for Ensuring Health Information Technology Safety: Evidence-Based Approaches Contribution of the IMIA Working Group Health Informatics for Patient Safety , 2013, Yearbook of Medical Informatics.

[7]  Stephanie Rosenbaum,et al.  A toolkit for strategic usability: results from workshops, panels, and surveys , 2000, CHI.

[8]  M.R. Popovic,et al.  A reliable gyroscope-based gait-phase detection sensor embedded in a shoe insole , 2004, IEEE Sensors Journal.

[9]  Guy H. Walker,et al.  Human Factors Methods: A Practical Guide for Engineering and Design , 2012 .

[10]  Laura Manzari,et al.  User-Centered Design of a Web Site for Library and Information Science Students: Heuristic Evaluation and Usability Testing , 2013 .

[11]  K. Mills,et al.  Identifying clinically meaningful tools for measuring comfort perception of footwear. , 2010, Medicine and science in sports and exercise.

[12]  Silvia Mara Abrahão,et al.  Empirical validation of a usability inspection method for model-driven Web development , 2013, J. Syst. Softw..

[13]  John L. Reeves,et al.  Memory for pain: Relation between past and present pain intensity , 1985, Pain.

[14]  Vimla L. Patel,et al.  Using usability heuristics to evaluate patient safety of medical devices , 2003, J. Biomed. Informatics.

[15]  Lionel C. Briand,et al.  Facilitating the transition from use case models to analysis models: Approach and experiments , 2013, TSEM.

[16]  Christophe Kolski,et al.  State of the Art on the Cognitive Walkthrough Method, Its Variants and Evolutions , 2010, Int. J. Hum. Comput. Interact..

[17]  Jeff Sauro,et al.  Quantifying the User Experience: Practical Statistics for User Research , 2012 .

[18]  Monique W. M. Jaspers,et al.  A comparison of usability methods for testing interactive health technologies: Methodological aspects and empirical evidence , 2009, Int. J. Medical Informatics.

[19]  Klaas Postema,et al.  Long-term use of custom-made orthopedic shoes: 1.5-year follow-up study. , 2010, Journal of rehabilitation research and development.

[20]  B M Nigg,et al.  Influence of Foot, Leg and Shoe Characteristics on Subjective Comfort , 2000, Foot & ankle international.

[21]  R. Khajouei,et al.  Classification and prioritization of usability problems using an augmented classification scheme , 2011, J. Biomed. Informatics.

[22]  Leo R. Quinlan,et al.  Human Centred Design Considerations for Connected Health Devices for the Older Adult , 2014, Journal of personalized medicine.

[23]  Debora Shaw,et al.  Handbook of usability testing: How to plan, design, and conduct effective tests , 1996 .

[24]  Andrew Sears,et al.  Human-Computer Interaction: Development Process , 2009 .

[25]  Klaas Postema,et al.  Use and usability of custom-made orthopedic shoes. , 2010, Journal of rehabilitation research and development.

[26]  Dirk Thorleuchter,et al.  Usability Based Modeling for Advanced IT-Security – An Electronic Engineering Approach , 2012 .

[27]  B M Nigg,et al.  Effect of shoe insert construction on foot and leg movement. , 1998, Medicine and science in sports and exercise.