Presentation
HE11 - Enhancing Upper Limb Rehabilitation in Hospital Occupational Therapy Using a Machine Learning Human-Robot Interaction (HRI) Platform Integrated With Haptic and Visual Feedback
SessionPoster Session 2
DescriptionTopic:
This study aims to advance Human-Robot Interaction (HRI) for upper limb rehabilitation in general hospital settings by developing and validating an innovative platform that integrates haptic robotics, visual feedback, and machine learning.
Application:
The primary goal is to enhance the hospital occupational therapy environment by improving interactions between upper limb impaired patients and rehabilitation exercises. Machine learning techniques will be employed to identify and adapt to predefined tasks recommended by occupational therapists as patients engage with the robotic system. By integrating haptic feedback and visual interventions, the platform aims to provide more efficient, personalized rehabilitation options that prioritize human-centered care.
Background:
Upper limb rehabilitation is a critical component of recovery for patients with impairments resulting from conditions such as stroke or injury. Traditionally, occupational therapy in hospital environments relies on manual exercises and therapist-guided activities. However, with advancements in AI, robotics, tactile interaction, virtual reality, and machine learning, there is an increasing interest in leveraging technology to make rehabilitation more personalized, adaptive, and efficient.
Human-Robot Interaction (HRI) platforms present a promising solution to address these challenges by enhancing the interaction between therapeutic platforms and patients during rehabilitation exercises. The integration of haptic robotics and visual feedback offers the potential to provide real-time, precise control over physiological movements, while machine learning allows the system to adapt to individual patient progress and needs.
The study builds on these technological advancements to develop a platform that can improve the hospital occupational therapy environment. By addressing four primary objectives – a) creating a haptic 3D control platform, b) verifying system data, c) analyzing occupational therapy students' perceptions of HRI, and d) proposing human-centric rehabilitation tasks – the research seeks to establish a clinically validated, innovative tool for upper limb rehabilitation. The Wolf Motor Function Test (WMFT) (Wolf 2001) is also incorporated as a benchmark for assessing the system’s effectiveness in rehabilitation tasks.
Overview of the presentation:
This study focuses on developing and validating a novel Human-Robot Interaction (HRI) platform for upper limb rehabilitation, integrating haptic robotics, vision feedback, and machine learning to enhance the hospital occupational therapy environment. We have four primary objectives, and achieved the first three. First, it seeks to develop a haptic 3D control platform that can precisely manage the movement of a robotic arm for upper limb rehabilitation. This platform allows therapists to define rehabilitation tasks that the robotic system will execute, making the therapy more adaptive and responsive to patient needs. Second, the study aims to verify the data generated by the 3D platform, ensuring accuracy and reliability in the system's output. The third objective focuses on analyzing the perceptions of Doctoral students in Occupational Therapy Department (SJSU) regarding the use of HRI interventions in hospital or clinic settings. This analysis provides insights into the acceptance and potential impact of these technologies in clinicals or general hospitals. Finally, the study proposes a series of human-centric rehabilitation tasks, including those modeled after the Wolf Motor Function Test (WMFT), that can be performed using the novel system. These tasks are tailored to assess and enhance upper limb function, providing a comprehensive approach to patient rehabilitation.
To validate clinical impact, a pilot experiment will be conducted. The experiment will involve generating and executing upper limb rehabilitation tasks under WMFT guidance (Woodbury 2010) using the 3D control platform to drive the haptic robotic arm. Data verification will occur after the pilot study, ensuring that the platform's movements and tasks align with the intended therapeutic goals. Simultaneously, feedback from the occupational therapist will be collected and analyzed to inform future iterations of the platform and further research on HRI for rehabilitation.
In summary, this research aims to create a transformative tool that combines cutting-edge technology with clinical expertise to improve rehabilitation outcomes and the overall patient experience in occupational therapy settings.
Takeaway points:
This study underscores the importance of integrating cutting-edge technology with clinical expertise to improve rehabilitation outcomes. By combining haptic robotics, visual feedback, and machine learning, the developed platform represents a significant step toward more personalized, efficient, and effective rehabilitation in hospital occupational therapy settings.
References:
Wolf, S. L., Catlin, P. A., Ellis, M., Archer, A. L., Morgan, B., & Piacentino, A. (2001). Assessing Wolf Motor Function Test as outcome measure for research in patients after stroke. Stroke, 32(7), 1635–1639. https://doi.org/10.1161/01.STR.32.7.1635
Woodbury M, Velozo CA, Thompson PA, Light K, Uswatte G, Taub E, Winstein CJ, Morris D, Blanton S, Nichols-Larsen DS, Wolf SL. Measurement structure of the Wolf Motor Function Test: implications for motor control theory. Neurorehabil Neural Repair. 2010 Nov-Dec;24(9):791-801. doi: 10.1177/1545968310370749. Epub 2010 Jul 8. PMID: 20616302; PMCID: PMC3731767.
This study aims to advance Human-Robot Interaction (HRI) for upper limb rehabilitation in general hospital settings by developing and validating an innovative platform that integrates haptic robotics, visual feedback, and machine learning.
Application:
The primary goal is to enhance the hospital occupational therapy environment by improving interactions between upper limb impaired patients and rehabilitation exercises. Machine learning techniques will be employed to identify and adapt to predefined tasks recommended by occupational therapists as patients engage with the robotic system. By integrating haptic feedback and visual interventions, the platform aims to provide more efficient, personalized rehabilitation options that prioritize human-centered care.
Background:
Upper limb rehabilitation is a critical component of recovery for patients with impairments resulting from conditions such as stroke or injury. Traditionally, occupational therapy in hospital environments relies on manual exercises and therapist-guided activities. However, with advancements in AI, robotics, tactile interaction, virtual reality, and machine learning, there is an increasing interest in leveraging technology to make rehabilitation more personalized, adaptive, and efficient.
Human-Robot Interaction (HRI) platforms present a promising solution to address these challenges by enhancing the interaction between therapeutic platforms and patients during rehabilitation exercises. The integration of haptic robotics and visual feedback offers the potential to provide real-time, precise control over physiological movements, while machine learning allows the system to adapt to individual patient progress and needs.
The study builds on these technological advancements to develop a platform that can improve the hospital occupational therapy environment. By addressing four primary objectives – a) creating a haptic 3D control platform, b) verifying system data, c) analyzing occupational therapy students' perceptions of HRI, and d) proposing human-centric rehabilitation tasks – the research seeks to establish a clinically validated, innovative tool for upper limb rehabilitation. The Wolf Motor Function Test (WMFT) (Wolf 2001) is also incorporated as a benchmark for assessing the system’s effectiveness in rehabilitation tasks.
Overview of the presentation:
This study focuses on developing and validating a novel Human-Robot Interaction (HRI) platform for upper limb rehabilitation, integrating haptic robotics, vision feedback, and machine learning to enhance the hospital occupational therapy environment. We have four primary objectives, and achieved the first three. First, it seeks to develop a haptic 3D control platform that can precisely manage the movement of a robotic arm for upper limb rehabilitation. This platform allows therapists to define rehabilitation tasks that the robotic system will execute, making the therapy more adaptive and responsive to patient needs. Second, the study aims to verify the data generated by the 3D platform, ensuring accuracy and reliability in the system's output. The third objective focuses on analyzing the perceptions of Doctoral students in Occupational Therapy Department (SJSU) regarding the use of HRI interventions in hospital or clinic settings. This analysis provides insights into the acceptance and potential impact of these technologies in clinicals or general hospitals. Finally, the study proposes a series of human-centric rehabilitation tasks, including those modeled after the Wolf Motor Function Test (WMFT), that can be performed using the novel system. These tasks are tailored to assess and enhance upper limb function, providing a comprehensive approach to patient rehabilitation.
To validate clinical impact, a pilot experiment will be conducted. The experiment will involve generating and executing upper limb rehabilitation tasks under WMFT guidance (Woodbury 2010) using the 3D control platform to drive the haptic robotic arm. Data verification will occur after the pilot study, ensuring that the platform's movements and tasks align with the intended therapeutic goals. Simultaneously, feedback from the occupational therapist will be collected and analyzed to inform future iterations of the platform and further research on HRI for rehabilitation.
In summary, this research aims to create a transformative tool that combines cutting-edge technology with clinical expertise to improve rehabilitation outcomes and the overall patient experience in occupational therapy settings.
Takeaway points:
This study underscores the importance of integrating cutting-edge technology with clinical expertise to improve rehabilitation outcomes. By combining haptic robotics, visual feedback, and machine learning, the developed platform represents a significant step toward more personalized, efficient, and effective rehabilitation in hospital occupational therapy settings.
References:
Wolf, S. L., Catlin, P. A., Ellis, M., Archer, A. L., Morgan, B., & Piacentino, A. (2001). Assessing Wolf Motor Function Test as outcome measure for research in patients after stroke. Stroke, 32(7), 1635–1639. https://doi.org/10.1161/01.STR.32.7.1635
Woodbury M, Velozo CA, Thompson PA, Light K, Uswatte G, Taub E, Winstein CJ, Morris D, Blanton S, Nichols-Larsen DS, Wolf SL. Measurement structure of the Wolf Motor Function Test: implications for motor control theory. Neurorehabil Neural Repair. 2010 Nov-Dec;24(9):791-801. doi: 10.1177/1545968310370749. Epub 2010 Jul 8. PMID: 20616302; PMCID: PMC3731767.
Authors
Assistant Professor
Event Type
Poster Presentation
TimeTuesday, April 14:45pm - 6:15pm EDT
LocationFrontenac Foyer