Biology subjects

Hicks, J. L.

Publications and source records attributed to Hicks, J. L..

24 records · Page 2Linked to original sources

bioRxiv2021

An open-source and wearable system for measuring 3D human motion in real-time

Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation interventions for conditions such as osteoarthritis, stroke, and Parkinsons disease. Optical motion capture systems are the current standard for estimating kinematics but require expensive equipment located in a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Further, many wearable sensor systems require a computer in close proximity and rely on proprietary software, making it difficult for researchers to reproduce experimental findings. Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller. We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking (n = 5) and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task (n = 5). The open-source software and hardware are scalable, tracking between 1 and 14 body segments, with one sensor per segment. Kinematics are estimated in real-time using a musculoskeletal model and inverse kinematics solver. The computation frequency, depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track up to 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment. The OpenSenseRT system is accurate, low-cost, and simple to replicate, enabling movement analysis in labs, clinics, homes, and free-living settings.

bioengineering↗

bioRxiv2020

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Despite advances in neuroscience techniques, it is still difficult to measure and interpret the activity of the millions of neurons involved in motor control. Thus, researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This "Learn to Move" competition, which we have run annually since 2017 at the NeurIPS conference, has attracted over 1300 teams from around the world. Top teams adapted state-of-art deep reinforcement learning techniques to produce complex motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.

bioengineering↗

bioRxiv2019

An in situ atlas of mitochondrial DNA in mammalian tissues reveals high content in stem/progenitor cells

Mitochondria regulate ATP production, metabolism and cell death. Alterations in mitochondrial DNA (mtDNA) sequence and copy number are implicated in aging and organ dysfunction in diverse inherited and sporadic diseases. Since most measurements of mtDNA use homogenates of complex tissues, little is known about cell type-specific mtDNA copy number heterogeneity in normal physiology, aging and disease. Thus, the precise cell types whose loss of mitochondrial activity and altered mtDNA copy number that result in organ dysfunction in aging and disease have often not been clarified. Here, we validated an in situ hybridization approach to generate a single cell resolution atlas of mtDNA content in mammalian tissues. In hierarchically organized self-renewing tissues, higher levels of mtDNA were observed in stem/proliferative compartments compared to differentiated compartments. Striking zonal patterns of mtDNA levels in the liver reflected the known oxygen tension gradient. In the kidney, proximal and distal tubules had markedly higher mtDNA levels compared to cells within glomeruli and collecting duct epithelial cells. Decreased mtDNA levels were visualized in renal tubules as a function of aging, which was prevented by calorie restriction. We provide a novel approach for quantifying species- and cell type-specific mtDNA copy number and dynamics in any normal or diseased tissue and can be used for monitoring the effects of interventions in animal and human studies.

pathology↗

bioRxiv2019

OpenSim Moco: Musculoskeletal optimal control

Musculoskeletal simulations of movement can provide insights needed to help humans regain mobility after injuries and design robots that interact with humans. Here, we introduce Open-Sim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation but requires extensive technical expertise to implement. Moco frees researchers from implementing direct collocation themselves, allowing them to focus on their scientific questions. The software can handle the wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which are common in musculoskeletal models. To show Mocos abilities, we first solve for muscle activity that produces an observed walking motion while minimizing muscle excitations and knee joint loading. Then, we predict a squat-to-stand motion and optimize the stiffness of a passive assistive knee device. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand human and animal movement.

bioengineering↗

bioRxiv2019

The Effects of Motor Modularity on Performance, Generalizability and Learning in Upper-Extremity Reaching: a Computational Analysis

It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviors seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks-such as reaching to targets on a higher or lower plane, and starting from alternate initial poses-with average errors similar to a full-dimensional controller.

physiology↗

bioRxiv2019

Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations

Deficits in the ankle plantarflexor muscles, such as weakness and contracture, occur commonly in conditions such as cerebral palsy, stroke, muscular dystrophy, and Charcot-Marie-Tooth disease. While these deficits likely contribute to observed gait pathologies, elucidating cause-effect relationships is difficult due to the often co-occurring biomechanical and neural deficits. To elucidate the effects of weakness and contracture, we systematically introduced isolated deficits into a musculoskeletal model and generated simulations of walking to predict gait adaptations due to these deficits. We developed a planar model containing 9 degrees of freedom and 18 musculotendon actuators, and an optimization framework through which we imposed simple objectives, such as minimizing cost of transport while avoiding falling and injury, and maintaining head stability. We first validated that our model could generate gaits that reproduced experimentally observed kinematic, kinetic, and metabolic trends for two cases: 1) walking at prescribed speeds between 0.50 m/s and 2.00 m/s and 2) walking at self-selected speed. We then applied mild, moderate, and severe levels of muscle weakness or contracture to either the soleus (SOL) or gastrocnemius (GAS) or both of these major plantarflexors (PF) and retrained the model to walk at a self-selected speed. The model was robust to all deficits, finding a stable gait in all cases. Severe PF weakness caused the model to adopt a slower, \"heel-walking\" gait. Severe contracture of only SOL or both PF yielded similar results: the model adopted a \"toe-walking\" gait with excessive hip and knee flexion during stance. These results highlight how plantarflexor weakness and contracture may contribute to observed gait patterns. Our model, simulation and optimization framework, and results are freely shared so that others can reproduce and build upon our work.\n\nAuthor summaryDeficits in the muscles that extend the ankle are thought to contribute to abnormal walking patterns in conditions such as cerebral palsy, stroke, muscular dystrophy, and Charcot-Marie-Tooth disease. To study how deficits in these muscles contribute to abnormal walking patterns, we used computer simulations to systematically introduce muscle deficits into a biomechanically accurate model. We first showed that our model could discover realistic walking patterns over a wide range of speeds when we posed a simple objective: walking while consuming a minimum amount of energy per distance, maintaining head stability, and avoiding injury. We then used the model to study the effect of two commonly observed problems: muscle weakness and muscle tightness. We found that severe weakness of the ankle extensors caused the model to adopt a slower, \"heel-walking\" gait, and severe tightness caused the model to adopt a crouched, \"toe-walking\" gait. These results highlight how deficits in the ankle extensor muscles may contribute to abnormal walking patterns commonly seen in pathological populations.

bioengineering↗