| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | September 14, 2015 |
| Latest Amendment Date: | September 14, 2015 |
| Award Number: | 1544702 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2015 |
| End Date: | December 31, 2020 (Estimated) |
| Total Intended Award Amount: | $799,233.00 |
| Total Awarded Amount to Date: | $799,233.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2121 EUCLID AVE CLEVELAND OH US 44115-2226 (216)687-3630 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2121 Euclid Avenue Cleveland OH US 44115-2214 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CPS-Cyber-Physical Systems |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The project will produce breakthroughs in the science of human-machine interaction and will produce lasting impacts on exercise machine technologies. The proposed Cyber-Enabled Exercise Machines (CEEMs) adapt to their users, seeking to maximize the effectiveness of exercise while guaranteeing safety. CEEMs measure and process biomechanical variables and generate adjustments to its own resistance, and generate cues to be followed by the exerciser. CEEMs are reconfigurable by software, which permits a wide range of exercises with the same hardware. Two prototype machines will be field-tested with the student-athlete population and used to validate project goals. The prototypes will be a valuable instrument for dissemination and outreach, as well as for student engagement. The outcomes of this research have repercussions beyond athletic conditioning: the same foundations and methodologies can be followed to design machines for rehabilitation, exercise countermeasure devices for astronauts, and custom exercise devices for the elderly and persons with disabilities. Thus, the project has the potential to improve health of society members at various levels.
This research will contribute to the foundations of cyber-physical system science in the following aspects: biomechanical modeling and real-time musculoskeletal state estimation; estimation theory and unscented H-infinity estimation; control theory and human-machine interaction dynamics, and micro-evolutionary optimization for real-time systems. The proposed Cyber-Enabled Exercise Machines (CEEMs) are highly reconfigurable devices which adapt to the user in pursuit of an optimization objective, namely maximal activation of target muscle groups. Machine adaptation occurs through port impedance modulation, and optimal cues are generated for the exerciser to follow. The goals of the project are threefold: i) development of foundational cyber-physical science and technology in the ๏ฌeld of human-machine systems; ii) development of new approaches to modeling, design, control and optimization of advanced exercise machines, and iii) application of the above results to develop two custom-built CEEMs: a rowing ergometer and a 2-degree-of-freedom resistance machine.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The last decades have seen a proliferation of robot technologies that involve interaction with humans. Frequently, information rather than physical contact is recalled when discussing these exchanges. In our project, we have focused on physical human-robot interactions, where significant forces and speeds are involved, as demanded by rehabilitation and exercise robots.Our work was centered on the notion of cyber-enabled exercise machines, which should feature autonomy, adaptability to the user and intrinsic safety. These characteristics were to be coordinated to create exercise and rehabilitation systems that are more effective at achieving performance and recovery goals, while being more responsive to the needs of different users.
To this end, a team was formed by a mechanical engineer, an electrical engineer, a human performance scientist and a biomechanics expert. Several scientific and engineering challenges associated with the development of such machines were undertaken, producing new knowledge in the respective fields. As originally planned, two machine prototypes were built that incorporated the principles and methodologies contributed by the project. The systems were tested with human subjects and their capabilities demonstrated through measurable outcomes.
In the field of human performance, we demonstrated the efficacy of introducing eccentric regimes in the rowing exercise. Eccentric phases occur when a muscle stretches under load. Eccentric regimes are of importance in strength building and recovery from injuries. Our powered rowing machine prototype and control systems introduced this form of loading, which is absent from conventional rowing machines.
The team contributed new methods to estimate muscle activation patterns, with unprecented accuracy. We constructed mathematical models of human motion as a basis for these estimations.
Advances in the understanding of human movement control were produced through collaboration across the fields of biomechanics and control theory. We contributed new methods to represent human movement and control in through fast simulations. These methods are instrumental to the development of advanced human-machine interaction systems.
Last Modified: 03/01/2021
Modified by: Hanz Richter
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