Matthias Schindelholz

ETH Zurich, Reha Rheinfelden, Bern University of Applied Sciences

Papers

10

Total Citations

144

H-Index

9

About

Matthias Schindelholz is a biomedical engineer and rehabilitation scientist whose research sits at the intersection of robotics, cardiovascular physiology, and neurological recovery. He has made pioneering contributions to the development and clinical validation of feedback-controlled robotics-assisted treadmill exercise (RATE) as a means of delivering cardiovascular rehabilitation to stroke survivors — particularly those with severe motor impairments who cannot benefit from conventional exercise interventions. His work demonstrated that robotic systems could be intelligently adapted to monitor and regulate exercise intensity in real time, using physiological signals such as heart rate and oxygen uptake to guide therapeutic sessions safely and effectively. Schindelholz's most influential study (2015, 36 citations) provided clinical evidence that feedback-controlled RATE can meaningfully improve cardiovascular fitness in the early post-stroke period, addressing a critical gap in rehabilitation practice. His earlier proof-of-concept studies established the methodological foundation for cardiopulmonary exercise testing in severely impaired populations using robotic platforms, and subsequent work extended these innovations to robotic end-effector systems simulating walking and stair climbing. Across more than a dozen publications, his research has collectively shaped how the rehabilitation community conceptualises and implements cardiovascular care for neurologically impaired patients, offering new hope for individuals previously excluded from structured aerobic training programs.

Research Focus

Key Achievements

9
H-Index
10
Papers
144
Total Citations
14
Avg Citations/Paper
🏆 Most Cited Paper
Efficacy of Feedback-Controlled Robotics-Assisted Treadmill Exercise to Improve Cardiovascular Fitness Early After Stroke
36 citations · 2015
📈 Most Prolific Year: 2013 (4 Papers)
🤝 Key Collaborators: 6
🏛 Institutions: ETH Zurich, Reha Rheinfelden, Bern University of Applied Sciences

Top Papers

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago