The effects of oar-shaft stiffness and length on rowing biomechanics

This work investigates the effects of oar-shaft stiffness and length on rowing biomechanics. The mechanical properties of the oar-shafts were examined using an end-loaded cantilever system, and theoretical relations were proposed between the mechanics of the oar-shafts and rowing performance. On-water experiments were subsequently conducted and rowing biomechanics measured via the PowerLine Rowing Instrumentation System. The PowerLine system measures force and oar angle on the oarlock, as well as proper boat acceleration. The convergent validity and test-retest reliability of the PowerLine force measurements were determined prior to the on-water experiments. Thereafter, rowers were tested over a set distance using oar-shafts of different stiffness and length. There were slight differences in the biomechanics between rowing with the different oar configurations. However, the measured differences in the biomechanical parameters were on the same order of magnitude as the rower’s inter-stroke inconsistencies

Inverse Dynamics Modelling of Paralympic Wheelchair Curling

Accepted author manuscript version reprinted, by permission, from Journal of Applied Biomechanics, 2017 (ahead of print) 1-19, http://dx.doi.org/10.1123/jab.2016-0143. © Human Kinetics, Inc.Paralympic wheelchair curling is an adapted version of Olympic curling played by individuals with spinal cord injuries, cerebral palsy, multiple sclerosis, and lower extremity amputations. To the best of the authors’ knowledge, there has been no experimental or computational research published regarding the biomechanics of wheelchair curling. Accordingly, the objective of this research was to quantify the angular joint kinematics and dynamics of a Paralympic wheelchair curler throughout the delivery. The angular joint kinematics of the upper extremity were experimentally measured using an inertial measurement unit system; the translational kinematics of the curling stone were additionally evaluated with optical motion capture. The experimental kinematics were optimized to satisfy the kinematic constraints of a subject-specific multibody biomechanical model. The optimized kinematics were subsequently used to compute the resultant joint moments via inverse dynamics analysis. The main biomechanical demands throughout the delivery (i.e., in terms of both kinematic and dynamic variables) were about the hip and shoulder joints, followed sequentially by the elbow and wrist. The implications of these findings are discussed in relation to wheelchair curling delivery technique, musculoskeletal modelling, and forward dynamic simulations.This research was funded by Dr. John McPhee’s Tier I Canada Research Chair in Biomechatronic System Dynamics

Energy Regeneration and Environment Sensing for Robotic Leg Prostheses and Exoskeletons

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, limitations in automated control and energy-efficient actuation have impeded their transition from research laboratories to real-world environments. With regards to control, the current automated locomotion mode recognition systems being developed rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, here a multi-generation environment sensing and classification system powered by computer vision and deep learning was developed to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. To support this initiative, the “ExoNet” database was developed – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a novel hierarchical labelling architecture. Over a dozen state-of-the-art deep convolutional neural networks were trained and tested on ExoNet for large-scale image classification and automatic feature engineering. The benchmarked CNN architectures and their environment classification predictions were then quantitatively evaluated and compared using an operational metric called “NetScore”, which balances the classification accuracy with the architectural and computational complexities (i.e., important for onboard real-time inference with mobile computing devices). Of the benchmarked CNN architectures, the EfficientNetB0 network achieved the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore. These comparative results can inform the optimal architecture design or selection depending on the desired performance of an environment classification system. With regards to energetics, backdriveable actuators with energy regeneration can improve the energy efficiency and extend the battery-powered operating durations by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, the evaluation and control of these regenerative actuators has focused on steady-state level-ground walking. To encompass real-world community mobility more broadly, here an energy regeneration system, featuring mathematical and computational models of human and wearable robotic systems, was developed to simulate energy regeneration and storage during other locomotor activities of daily living, specifically stand-to-sit movements. Parameter identification and inverse dynamic simulations of subject-specific optimized biomechanical models were used to calculate the negative joint mechanical work and power while sitting down (i.e., the mechanical energy theoretically available for electrical energy regeneration). These joint mechanical energetics were then used to simulate a robotic exoskeleton being backdriven and regenerating energy. An empirical characterization of an exoskeleton was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration and storage with the exoskeleton parameters. The performance calculations showed that regenerating electrical energy during stand-to-sit movements provide small improvements in energy efficiency and battery-powered operating durations. In summary, this research involved the development and evaluation of environment classification and energy regeneration systems to improve the automated control and energy-efficient actuation of next-generation robotic leg prostheses and exoskeletons for real-world locomotor assistance

Biomechanical Modelling of Paralympic Wheelchair Curling

This research represents the first documented investigation into the biomechanics and neural motor control of Paralympic wheelchair curling. A multibody biomechanical model of the wheelchair curling delivery was developed in reference to a Team Canada Paralympic athlete with a spinal cord injury. Subject-specific body segment parameters were quantified via dual-energy x-ray absorptiometry. The angular joint kinematics throughout the wheelchair curling delivery were experimentally measured using an inertial measurement unit system; the translational kinematics of the curling stone were additionally evaluated with optical motion capture. The experimental kinematics were optimized to satisfy the kinematic constraints of the multibody biomechanical model. The optimized kinematics were subsequently used to compute the resultant joint moments through inverse dynamics analysis. The neural motor control of the Paralympic athlete was modeled using forward dynamic optimization. The predicted kinematics from different optimization objective functions were compared with those experimentally measured throughout the wheelchair curling delivery. Of the optimization objective functions under consideration, minimizing angular joint accelerations resulted in the most accurate predictions of the kinematic trajectories and the shortest optimization computation time. The implications of these findings are discussed in relation to musculoskeletal modeling and optimal equipment design through predictive simulation

StairNet: Visual Recognition of Stairs for Human-Robot Locomotion

Human-robot walking with prosthetic legs and exoskeletons, especially over complex terrains such as stairs, remains a significant challenge. Egocentric vision has the unique potential to detect the walking environment prior to physical interactions, which can improve transitions to and from stairs. This motivated us to create the StairNet initiative to support the development of new deep learning models for visual sensing and recognition of stairs, with an emphasis on lightweight and efficient neural networks for onboard real-time inference. In this study, we present an overview of the development of our large-scale dataset with over 515,000 manually labeled images, as well as our development of different deep learning models (e.g., 2D and 3D CNN, hybrid CNN and LSTM, and ViT networks) and training methods (e.g., supervised learning with temporal data and semi-supervised learning with unlabeled images) using our new dataset. We consistently achieved high classification accuracy (i.e., up to 98.8%) with different designs, offering trade-offs between model accuracy and size. When deployed on mobile devices with GPU and NPU accelerators, our deep learning models achieved inference speeds up to 2.8 ms. We also deployed our models on custom-designed CPU-powered smart glasses. However, limitations in the embedded hardware yielded slower inference speeds of 1.5 seconds, presenting a trade-off between human-centered design and performance. Overall, we showed that StairNet can be an effective platform to develop and study new visual perception systems for human-robot locomotion with applications in exoskeleton and prosthetic leg control

Body segment parameters of Paralympic athletes from dual-energy X-ray absorptiometry

The final publication is available at Springer via http://dx.doi.org/10.1007/s12283-016-0200-3This research represents the first documented investigation into the body segment parameters of Paralympic athletes (e.g., individuals with spinal cord injuries and lower extremity amputations). Two-dimensional body segment parameters (i.e., mass, length, position vector of the center of mass, and principal mass moment of inertia about the center of mass) were quantified from dual-energy X-ray absorptiometry (DXA). In addition to establishing a body segment parameter database of Paralympic athletes for prospective biomechanists and engineers, the mass of each body segment as experimentally measured via the DXA imaging was compared with that reported by previous research of able-bodied cadavers. In general, there were significant differences in the body segment masses between the different methods. These findings support the implementation of the proposed database for developing valid multibody biomechanical models of Paralympic athletes with distinct physical disabilities.This research was funded by Dr. John McPhee’s Tier I Canada Research Chair in Biomechatronic System Dynamics

Inverse Dynamics Modeling of Paralympic Wheelchair Curling

Accepted author manuscript version reprinted, by permission, from Journal of Applied Biomechanics, 2017 (ahead of print) 1-19, http://dx.doi.org/10.1123/jab.2016-0143. © Human Kinetics, Inc.Paralympic wheelchair curling is an adapted version of Olympic curling played by individuals with spinal cord injuries, cerebral palsy, multiple sclerosis, and lower extremity amputations. To the best of the authors’ knowledge, there has been no experimental or computational research published regarding the biomechanics of wheelchair curling. Accordingly, the objective of this research was to quantify the angular joint kinematics and dynamics of a Paralympic wheelchair curler throughout the delivery. The angular joint kinematics of the upper extremity were experimentally measured using an inertial measurement unit system; the translational kinematics of the curling stone were additionally evaluated with optical motion capture. The experimental kinematics were optimized to satisfy the kinematic constraints of a subject-specific multibody biomechanical model. The optimized kinematics were subsequently used to compute the resultant joint moments via inverse dynamics analysis. The main biomechanical demands throughout the delivery (i.e., in terms of both kinematic and dynamic variables) were about the hip and shoulder joints, followed sequentially by the elbow and wrist. The implications of these findings are discussed in relation to wheelchair curling delivery technique, musculoskeletal modelling, and forward dynamic simulations.This research was funded by Dr. John McPhee’s Tier I Canada Research Chair in Biomechatronic System Dynamics