Presentation
User Interface Design for a Soft and Dexterous Service Robot To Support Healthcare of Older Adults at Home
SessionHome Use Environment (DH9)
DescriptionIntroduction |
Telehealth offers the opportunity to deliver healthcare services in the comfort of a person's own home with technology. The COVID pandemic underscored the value of exploring remote healthcare solutions. One innovative approach involves the use of telehealth robots situated in a user's home and remotely controlled by healthcare providers to capture high-quality videos and images for on-demand diagnosis, checkups, and treatment. For instance, a person recovering from surgery could be at home requiring regular post-surgical checkups. The robot could help with such a kind of task where it can be remotely operated. Telehealth robots have the potential to enhance healthcare delivery, break down barriers to healthcare access, and offer advantages that surpass other telehealth devices. These robots provide mobility and manipulation capabilities, along with non-invasive tools for collecting health data. Nevertheless, the widespread adoption of such devices has not yet been realized. Acceptability of such telehealth robots by healthcare practitioners (HCP) is critical to their successful deployment. The usability of the interface of these robots may be a major barrier to the adoption of these systems by the HCPs.
Some other challenges for the deployment and adoption of telehealth robots in the home include cost (Scott et al., 2018) and size (Aguiar et al., 2021). A huge percentage of telehealth robots are developed using hard, rigid materials that have both advantages and disadvantages relative to emerging soft dexterous robots (Uppalapati et al., 2020).
We therefore worked with a hybrid (rigid and soft aspects) leveraging on the precision of the rigid robot and flexibility of the soft dexterous robots.
We explored the potential of multiple mock-ups for an easy-to-use and comprehensive user interface (UI) with which HCPs could interact with their patients remotely for telehealth consultations.
Method |
Design Process
The current study is a phase of a larger study that took an interdisciplinary and participatory design approach to developing a novel hybrid hard-rigid and soft robotic arm for telehealth applications (see Kadylak et al., 2023in press). The collaborative design team initiated the process by creating a set of potential tasks suitable for a soft robot's application in a telehealth setting. These tasks encompassed activities such as medication checks and wound inspections. The design team facilitated this task identification process by engaging in discussions with two subject matter experts (SMEs) from the healthcare field. Through their expertise and insights, the SMEs pinpointed specific telehealth requirements encountered in their everyday practice and identified scenarios where in-home care could be enhanced, potentially reducing the necessity for in-person appointments. The Kadylak et al., 2023. study focused on engaging a diverse range of healthcare providers (HCPs) to explore possible use cases and to share their overall attitudes, and potential concerns for soft telehealth robots. The current study focused on design requirements for the user interface of the soft telehealth robot.
The Telehealth Robot
The robot has different parts including a mobile base and a manipulator. The manipulator is made up of a rigid link, a soft material at its end to facilitate flexibility, and a camera at the end, to provide real-time visuals for the HCP. The rigid arm has three motors that can move it around. This arm can be used to place the final soft link in any desired position for the telehealth applications. It can be controlled remotely through a web interface. The web interface constitutes the user interface for the HCP which this study is focused on.
Measures and Materials
The study materials were prepared and reviewed by four subject matter experts who had experience in human-robot interaction studies, nursing and healthcare delivery. The focus was to identify applicable and useful features that should be included in the user interface. Mockups were developed to spur discussions, ideas, and suggestions on the user interface from the HCPs for different telehealth scenarios. The material included a background questionnaire to gain insights on the expertise, experience, and expectations of the HCPs. Short videos of the robot’s operation in different use cases were included to provide the HCPs with some insights on the functionalities of the robot. Three different UI mockups showcased different layouts, tools, and processes that could be carried out through the interface. The interview script prompted the HCPs to consider the use of the tools on the interface and to give feedback on possible improvements.
Participants and Data Collection
Five healthcare providers (HCPs) [n = 5; 2 nurses, 3 physicians] were recruited for the study. The two nurses were both nurses from Digital Hospital with OSF Healthcare. One of the physicians was a neurologist from OSF Healthcare, and the other two physicians were occupational medicine physicians from Carle Hospital. All five HCPs had worked extensively with telehealth, having an average of 6 years of experience interfacing with patients via telehealth. The five HCPs had – on average – 20 years of experience working as HCPs, and the average age of the HCPs was 45.6 years old. Of the five HCPs, two were females and three were males. The needs assessment interviews each lasted 60-120 minutes and were conducted remotely via Zoom. Following a brief round of background questions, study participants were shown demonstration videos of the robot, asked to provide their initial impressions, given a walkthrough of the UI mockups, asked questions specific to the UI design, asked additional questions pertaining to the soft arm attachments for the robot and other relevant features, and ultimately debriefed on the study.
Results |
We were able to collect a wide array of data pertaining to first impressions; clinical use cases; barriers to use. The HCPs provided information on their safety and privacy concerns while operating the robot through the interface. Visualizations on the interface that would enhance better situation awareness such as views required for safe navigation and manipulation of tools was learned in the process. Other features that the interface should have to facilitate ease of use were shared such as battery life requirements, camera placement, display screen adaptability, automated and manual operation modes were discussed. Features to ensure data privacy were also discussed to provide security for the care recipients. We documented other features suggested by the HCP to facilitate the processing of information through the robot such as data processing needing immediate attention, or coding of information that required permission for access. Features for training clinicians to use various soft arm attachments such as stethoscopes, graspers, blood pressure cuffs, otoscopes and ophthalmoscopes were also co-designed with the HCPs. Factors that would facilitate trust in the technology such as transparency were factored into the design.
Conclusion |
All HCP study participants expressed excitement regarding the future integration of this technology into clinical practice and provided excellent feedback and suggestions for improvement of both the software and hardware features of the robot. Future iterations of the needs assessment will assess the usability and accessibility of functional prototypes which are currently being designed to reflect both the UI mockups and the data collected as a part of this needs assessment.
Reference
Kadylak, T., Uppalapati, N., Huq, A., Krishnan, G., & Rogers, W. A. (2023). Engaging Healthcare Providers to Design a Robot for Telehealth. Ergonomics in Design, 10648046231193287.
Scott Kruse, C., Karem, P., Shifflett, K., Vegi, L., Ravi, K., & Brooks, M. (2018). Evaluating barriers to adopting telemedicine worldwide: a systematic review. Journal of telemedicine and telecare, 24(1), 4-12.
Uppalapati N. K., Kadylak T. A., Rogers W. A., Krishnan G. (2020). Morphological switching robots to support independent living for older adults. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). http://ras.papercept.net/images/temp/IROS/files/3626.pdf
Telehealth offers the opportunity to deliver healthcare services in the comfort of a person's own home with technology. The COVID pandemic underscored the value of exploring remote healthcare solutions. One innovative approach involves the use of telehealth robots situated in a user's home and remotely controlled by healthcare providers to capture high-quality videos and images for on-demand diagnosis, checkups, and treatment. For instance, a person recovering from surgery could be at home requiring regular post-surgical checkups. The robot could help with such a kind of task where it can be remotely operated. Telehealth robots have the potential to enhance healthcare delivery, break down barriers to healthcare access, and offer advantages that surpass other telehealth devices. These robots provide mobility and manipulation capabilities, along with non-invasive tools for collecting health data. Nevertheless, the widespread adoption of such devices has not yet been realized. Acceptability of such telehealth robots by healthcare practitioners (HCP) is critical to their successful deployment. The usability of the interface of these robots may be a major barrier to the adoption of these systems by the HCPs.
Some other challenges for the deployment and adoption of telehealth robots in the home include cost (Scott et al., 2018) and size (Aguiar et al., 2021). A huge percentage of telehealth robots are developed using hard, rigid materials that have both advantages and disadvantages relative to emerging soft dexterous robots (Uppalapati et al., 2020).
We therefore worked with a hybrid (rigid and soft aspects) leveraging on the precision of the rigid robot and flexibility of the soft dexterous robots.
We explored the potential of multiple mock-ups for an easy-to-use and comprehensive user interface (UI) with which HCPs could interact with their patients remotely for telehealth consultations.
Method |
Design Process
The current study is a phase of a larger study that took an interdisciplinary and participatory design approach to developing a novel hybrid hard-rigid and soft robotic arm for telehealth applications (see Kadylak et al., 2023in press). The collaborative design team initiated the process by creating a set of potential tasks suitable for a soft robot's application in a telehealth setting. These tasks encompassed activities such as medication checks and wound inspections. The design team facilitated this task identification process by engaging in discussions with two subject matter experts (SMEs) from the healthcare field. Through their expertise and insights, the SMEs pinpointed specific telehealth requirements encountered in their everyday practice and identified scenarios where in-home care could be enhanced, potentially reducing the necessity for in-person appointments. The Kadylak et al., 2023. study focused on engaging a diverse range of healthcare providers (HCPs) to explore possible use cases and to share their overall attitudes, and potential concerns for soft telehealth robots. The current study focused on design requirements for the user interface of the soft telehealth robot.
The Telehealth Robot
The robot has different parts including a mobile base and a manipulator. The manipulator is made up of a rigid link, a soft material at its end to facilitate flexibility, and a camera at the end, to provide real-time visuals for the HCP. The rigid arm has three motors that can move it around. This arm can be used to place the final soft link in any desired position for the telehealth applications. It can be controlled remotely through a web interface. The web interface constitutes the user interface for the HCP which this study is focused on.
Measures and Materials
The study materials were prepared and reviewed by four subject matter experts who had experience in human-robot interaction studies, nursing and healthcare delivery. The focus was to identify applicable and useful features that should be included in the user interface. Mockups were developed to spur discussions, ideas, and suggestions on the user interface from the HCPs for different telehealth scenarios. The material included a background questionnaire to gain insights on the expertise, experience, and expectations of the HCPs. Short videos of the robot’s operation in different use cases were included to provide the HCPs with some insights on the functionalities of the robot. Three different UI mockups showcased different layouts, tools, and processes that could be carried out through the interface. The interview script prompted the HCPs to consider the use of the tools on the interface and to give feedback on possible improvements.
Participants and Data Collection
Five healthcare providers (HCPs) [n = 5; 2 nurses, 3 physicians] were recruited for the study. The two nurses were both nurses from Digital Hospital with OSF Healthcare. One of the physicians was a neurologist from OSF Healthcare, and the other two physicians were occupational medicine physicians from Carle Hospital. All five HCPs had worked extensively with telehealth, having an average of 6 years of experience interfacing with patients via telehealth. The five HCPs had – on average – 20 years of experience working as HCPs, and the average age of the HCPs was 45.6 years old. Of the five HCPs, two were females and three were males. The needs assessment interviews each lasted 60-120 minutes and were conducted remotely via Zoom. Following a brief round of background questions, study participants were shown demonstration videos of the robot, asked to provide their initial impressions, given a walkthrough of the UI mockups, asked questions specific to the UI design, asked additional questions pertaining to the soft arm attachments for the robot and other relevant features, and ultimately debriefed on the study.
Results |
We were able to collect a wide array of data pertaining to first impressions; clinical use cases; barriers to use. The HCPs provided information on their safety and privacy concerns while operating the robot through the interface. Visualizations on the interface that would enhance better situation awareness such as views required for safe navigation and manipulation of tools was learned in the process. Other features that the interface should have to facilitate ease of use were shared such as battery life requirements, camera placement, display screen adaptability, automated and manual operation modes were discussed. Features to ensure data privacy were also discussed to provide security for the care recipients. We documented other features suggested by the HCP to facilitate the processing of information through the robot such as data processing needing immediate attention, or coding of information that required permission for access. Features for training clinicians to use various soft arm attachments such as stethoscopes, graspers, blood pressure cuffs, otoscopes and ophthalmoscopes were also co-designed with the HCPs. Factors that would facilitate trust in the technology such as transparency were factored into the design.
Conclusion |
All HCP study participants expressed excitement regarding the future integration of this technology into clinical practice and provided excellent feedback and suggestions for improvement of both the software and hardware features of the robot. Future iterations of the needs assessment will assess the usability and accessibility of functional prototypes which are currently being designed to reflect both the UI mockups and the data collected as a part of this needs assessment.
Reference
Kadylak, T., Uppalapati, N., Huq, A., Krishnan, G., & Rogers, W. A. (2023). Engaging Healthcare Providers to Design a Robot for Telehealth. Ergonomics in Design, 10648046231193287.
Scott Kruse, C., Karem, P., Shifflett, K., Vegi, L., Ravi, K., & Brooks, M. (2018). Evaluating barriers to adopting telemedicine worldwide: a systematic review. Journal of telemedicine and telecare, 24(1), 4-12.
Uppalapati N. K., Kadylak T. A., Rogers W. A., Krishnan G. (2020). Morphological switching robots to support independent living for older adults. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). http://ras.papercept.net/images/temp/IROS/files/3626.pdf
Event Type
Oral Presentations
TimeWednesday, March 2710:30am - 11:00am CDT
LocationSalon A-2
Digital Health