Machines. Design, Modeling and Real-Time Control of Advanced Exercise
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1 Design, Modeling and Real-Time Control of Advanced Exercise Machines Hanz Richter, PhD, Professor of Mechanical Engineering Control, Robotics and Mechatronics Lab Cleveland State University
2 Background 4-yr collaborative research project funded by NSF Cyber-Physical Systems Hanz Richter (PI) (Machine Design and Control) Antonie (Ton) van den Bogert (Biomechanical Modeling, Predictive Simulation) Ken Sparks (Human Performance, Human Subjects Research) Dan Simon (Optimization, Estimation, Control) Collaboration with NASA Glenn: exercise in microgravity. Aim: compact, programmable and adaptive machines to optimize exercise and rehabilitation
3 Motivation Exercise machines: many advances in haptics, but inner mechanisms remain essentially the same. Conventional machines offer fixed-path motions and a few discrete resistance level settings from a few kinds: spring-like, weight-like, damping-like, inertia-like.
4 Motivation These fixed paths and finite settings result in a restricted set of muscle activation patterns Can we achieve specific muscle activation patterns (spatially and in magnitude) by designing more universal, programmable-resistance machines?
5 Exercise Machines as Cyber-Physical Systems These machines will autonomously vary mechanical resistance at the user port, seeking to maximize an optimality criterion related to exercise/rehab performance. We have chosen an aerobic machine (a rower) and a resistance machine (a custom 4 dof exercise robot) to develop and demonstrate basic research in biomechanics, control, estimation, optimization and human performance science.
6 Rowing Ergometers and Rowing Exercise Pull phase: overcome flywheel inertia + air damping. Return phase: decouple 1-way clutch, small force recoil due to bungee spring Main limitations: Can only vary damping characteristics in 10 discrete settings (vent opening). Inertial and spring characteristic fixed. Not capable of significant eccentric loading: power flows only from human to machine (pull phase).
7 Eccentric vs. Concentric Exercise Concentric: Muscle shortening under load (lift the weight): human adds energy Eccentric: Muscle lengthening under load (lower the weight): human absorbs energy Eccentric exercise has been widely recognized as essential for Athletic conditioning Rehabilitation Countermeasures for prolonged stays in microgravity (NASA)
8 Rowing: Baseline Human and Machine Data A comprehensive series of tests was conducted 2016, where machine velocities and forces and extensive physiological data (16-point EMG, oxygen, heart rate and motion marker data at 28 locations) were recorded in 9 different workout levels. dspace MicroLabBox greatly facilitated data collection! Largest rowing dataset available (GitHub)
9 Rowing: Sample Human and Machine Data
10 Powered Rowing Machine Prototype BELT DRIVE clutch not used dspace + MOTOR SERVO 1 kw AC SERVOMOTOR LOAD CELL Patent pending for machine + control system
11 Control System: Programmable Impedance with Virtual Flywheel and Clutch Flywheel, damper and clutch operation emulated by control Key advantage: can set parameters to anything desired, continuous range : enabler of optimal exercise Can program impedances outside classical mass-spring-damper (settings are lines of code) Controller developed in Simulink, deployed to MicroLabBox at 1kHz sample rate. ControlDesk interface.
12 Trials: Measurement and Control Setup
13 Post-Development Trials: September 2017 (Video) Using the machine for human performance science studies: Parker-Hannifin Human Motion and Control Lab - CSU Examine effects of high eccentric/concentric power ratios on metabolic expenditure and muscle activation (through EMG) Experienced rower Low cadence, very high force
14 Sample Data: EMG at high ecc/conc ratio
15 One conclusion The functionality of fixed hardware can be emulated and made much more versatile and flexible by means of control. This is possible only if a high-performance real-time control prototyping resource is available, such as the dspace MicroLabBox: High channel count and fast data rate High-level programmability through Simulink, streamlined transition between control algorithm study and simulation and real-time deployment Convenient operator interface: ControlDesk
16 Acknowledgements Support: National Science Foundation Cyber-Physical Systems Program Collaborators: Prof. Dan Simon (EE) Prof. Antonie van den Bogert (ME) Prof. Ken Sparks and Dr. Doug Wajda (Human Performance) Students: Humberto de las Casas: Machine design and control, dspace wizard Farbod Rohani, Huawei Wang: Human motion capture Matt Ivers, Kevin Kleis: Metabolic/EMG analysis Rowers: Matt Ivers, Lou Dregely
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