Assistant Professor, UCSF
Email: [email protected]
Mailing: 95 Kirkham St., Building 101, San Francisco, CA 94122
- MEng, Mechanical Engineering, Imperial College London, London, UK
- MS, Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA
- PhD, Electrical Engineering and Computer Science, UC Berkeley, Berkeley, CA
- Postdoctoral Fellow, Biomechanics and Robotics, UC Berkeley/UCSF, CA
Dr. Robert Matthew is an Assistant Professor in the Department of Physical Therapy and Rehabilitation Science and Department of Orthopaedic Surgery and is an Affiliate Faculty Member in the joint UC Berkeley/UCSF graduate program in Bioengineering. Their research focuses on the development and use of clinically deployable systems to assess function and augment the recovery process.
CLINICAL & RESEARCH INTERESTS
- Wearable / Depth Sensors
- Neuromusculoskeletal Biomechanics
- Motion decomposition
- Rehabilitation Robotics
Acute stroke assessment and rehabilitation
Despite decades of research into post-stroke recovery, there is little evidence that current protocols for upper extremity rehabilitation provide a distinct benefit to motor recovery, with long-term impairment present in two-thirds of stroke survivors. This has been attributed in part to a lack of clear functional assessments and heterogeneity in study participant selection. My work focuses on the use of scalable methods for assessing upper body function in the clinic and home. These measures can be used to assess the effectiveness of different interventions and guide the development of assistive robotic systems.
In collaboration with the UCSF Neurorecovery Clinic (link).
The direct connection of a residual limb to a prosthetic through an osseointegrated interface bypasses the skin breakdown typically seen in conventional socket prosthetics and has been associated with improvements in activity levels and a reduction in long term pain. My work examines the effects of these systems on changes in activity level, compensation, and accumulated loading on the implant.
As part of the UCSF Core Centre for Phenotyping Chronic Low Back Pain (UCSF REACH, link), we are creating a database of functional movement of individuals. We expect to collect kinematic data on over five-hundred people, with associated imagining, pain, and standard clinical functional measures. This dataset provides an un-precedented ability to identify sub-groups of patients based on their observed movements and to correlate them with longitudinal changes in pain, quality of life, and musculoskeletal health. This study provides baseline datasets for developing trajectory-based clustering algorithms and assesses their clinical utility for characterising patient function.