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THE BIG PICTURE

Firstly, we want to understand how animal limbs (arms or legs) are able to extend rapidly and accurately to accomplish tasks such as reaching, jumping, running or swimming.  In an evolutionary context, we are interested in the underlying musculoskeletal and neuromuscular features that contribute to effective limb motion.  Secondly, in a biomedical context, we want to understand how musculoskeletal ageing affects those neuromuscular features to better understand how age affects limb control in humans.

Our work is funded by the European Research Council and the Wellcome Trust

PROJECTS

Project 1: Effects of muscular ageing on neuromuscular coordination
Funded by Wellcome Trust
Aim: To determine how ageing influences neuromuscular coordination.
Background: The neuromuscular systems of limbed vertebrates, including humans, solve a profoundly difficult task – they can control multi-jointed limbs with accuracy, robustness and speed.  Such impressive control owes to visuomotor, sensorimotor and muscle-dynamical processes that appear to be “mechanically tuned” for effective limb movement.  Elderly adults suffer from deficits in all three processes, putting them at grave risk of falling.  Our work focuses on the changing muscular dynamics due to aged connective tissue.  Specifically, we hope to link connective tissue stiffening with a change in the nervous system’s ability to control limb movement.
We ask:
a) How do muscle intrinsic properties help confer speed, accuracy and robustness in a healthy limb?
b) How does ageing influence the above?
c) Can we develop an in vitro assay which mimics the effects of age such that we can test treatments/therapies?  

Project 2:  Evolution of Amphibian Locomotion
Funded by European Research Council
Aim: To isolate, measure and predict how variations in limb musculoskeletal architecture drive locomotor diversification.
Background:  All frogs “look like frogs”, yet they exhibit rich locomotor diversity over thousands of species.  Depending on their environment, they can either climb, jump, run, swim and dig.  Even a single frog species is able to perform varied tasks with their limbs as they climb, jump, swim or run through their environment.  Because of their impressive attributes, frogs are exquisite models to understand both the neuro-muscular function and evolution of vertebrate limbs, in general.
We ask:
a) What are the lower-body anatomical features that confer limb “multi-functionality” enabling certain species to walk, jump and climb?
b) When did limb multifunctionality evolve and what role did it have in shaping early frog evolution?

METHODOLOGY
The questions we ask often demand novel experimental approaches as well as computational modelling and simulation.  To make progress, we must consider several aspects of neuromuscular function including: a) muscle properties (e.g. speed, strength), b) skeletal morphology, c) limb architecture (how muscles connect to bones) and d) sensorimotor feedback.  Although traditional experimental approaches address these properties in isolation of one another, they are all interdependent. For example over the course of evolution, if an animal transitions from water to land, we expect that the muscle properties must also change in concert with morphological shifts.  Furthermore, we expect that the internal skeletal structure (e.g. the length of bones or bony processes) would also adapt. Hence, we attempt to understand how muscle properties, limb internal anatomy and external anatomy are 'tuned' to one another to confer effective locomotion.  Additionally, as an animal (or person) ages, there are changes to the musculoskeletal system; how, then does the nervous system adjust or adapt to maintain control?  We are interested in developing novel robotic, physiological and computational tools to understand how various properties interact.

Musculo-robotics:  We have developed a technique for measuring a muscle’s response to varying environmental conditions.  For example, a foot pushing against solid ground behaves differently than in water.  This is the case because physical forces profoundly influence muscle contractile behaviour.  Even given identical motor stimulation from the nervous system, a muscle’s mechanical contribution to limb movement varies depending on locomotor terrain (or substrate).  Studying muscle-limb-environment interactions is challenging because when terrain changes (e.g. gravel versus sand), both the intrinsic muscle response and motor stimulation may change simultaneously.  How can we isolate the effects of muscle-load interactions from sensorimotor modulation?  Our approach is to build a robotic limb model which is in contact with an arbitrary surface (e.g. water, sand, solid ground).  The robot is electronically coupled to an in vitro muscle via closed-loop communication.  We can stimulate the muscle to contract and measure its force in vitro.  The force data is instantaneously transmitted to a robotic limb model which exerts the same force on the substrate.  Following a small delay (<1 ms), the robot will move an amount depending on the physical resistance; slow movement for a large resistance (e.g. mud) versus fast for a small resistance (e.g. air).  The movement is measured and transmitted instantaneously back to the muscle to update its length, thus closing the loop.  Consequently, the muscle “feels” the resistance encountered by the robot as if it were directly attached.  From this technique we can measure how complex locomotor terrains influence limb kinematics and locomotor performance.

Reverse haptics: A flight simulator is an example of a haptic device where a pilot, in the real world, flies an aeroplane which is a physics simulation in the virtual world.  As the pilot moves a motorized joystick, the simulated rudder of the virtual aeroplane also moves.  Computed wind resistance on the aeroplane are then played back into the motors of the joystick allowing the pilot to “feel” the forces on the aircraft as if it were real.  In general, a biological entity in the real world (e.g. pilot) interacts with a physics simulation in the virtual world via computed forces.  In our lab, we created “reverse haptics” where the biological entity is in simulation whereas the physical forces are in the real world.  Specifically, we use a real-time multibody dynamics simulation of a biological limb.  Simulated muscles initiate movement which is mirrored exactly by a physical robotic limb.  When the robot contacts a surface in the real world it senses these forces with a load cell.  These measured forces are transmitted instantaneously back to the simulation such that the simulation “feels” the same physical forces that the robot feels.  In traditional haptics, the physics of limb interaction are computed whereas in reverse haptics, forces are measured.  This is a tremendously useful technique allowing us to understand limb-environment interactions that would be very difficult to simulate.  Using this tool we can manipulate internal musculoskeletal anatomy in software as well as alter substrate and foot/hand morphology in hardware. 

TEAM MEMBERS
Undergraduates:
Matthew Ronnenbergh
PhD Students:
Alice Leavey
Vittorio LaBarbera (co-supervised by John Hutchinson, Monica Daley & Yuval Tassa)
Vittorio La Barbera (co-supervisors Dr. John Hutchinson; Dr. Monica Daley; Dr. Yuval Tassa)
Postdocs:
Dr. Tiina Murtola
We are currently advertising, apply here to join the team!

Former Team members:
Former Undergraduates:
Caoimhe Abdul-Wahab
Charlotte Nwanodi
Abbie Osborn
Mathilde Vilcot
Former PhD students:

Dr. Eva Herbst (co-supervisor Dr. John Hutchinson)
Dr. Amber Collings ()
Dr. Enrico Eberhard ()
Former postdocs (RVC):
Dr. Laura Porro ()
Dr. Chris Basu ()

Former postdocs (Harvard):
Dr. Angie R. Rivera ()
Dr. Christofer Clemente ()

Please see my google scholar for the most recent activity.

Murtola, T., & Richards, C. (2022). The impact of intrinsic muscle properties on simulated reaching performance. Computer Methods in Biomechanics and Biomedical Engineering, 1-12.

Herbst, E. C., Eberhard, E. A., Hutchinson, J. R., & Richards, C. T. (2022). Spherical frame projections for visualising joint range of motion, and a complementary method to capture mobility data. Journal of Anatomy.

Herbst, E. C., Eberhard, E. A., Richards, C. T., & Hutchinson, J. R. (2022). In vivo and ex vivo range of motion in the fire salamander Salamandra salamandra. Journal of anatomy.

Collings, A. J., Eberhard, E. A., Basu, C., & Richards, C. T. (2022). Functional Analysis of Anuran Pelvic and Thigh Anatomy Using Musculoskeletal Modelling of Phlyctimantis maculatus. Frontiers in Bioengineering and Biotechnology, 10.

Richards, C T; Eberhard, E A (2020)
In vitro Virtual Reality: An Anatomically Explicit Musculoskeletal Simulation Powered by in vitro Muscle Using Closed-Loop Tissue-Software Interaction 
J Exp Biol 2020 May 18;223(Pt 10):jeb210054.  doi: 10.1242/jeb.210054.

Porro, L.B.*, Richards, C.T.* (2017) Digital dissection of the model organism Xenopus laevis using contrast-enhanced computed tomography, Journal of Anatomy. DOI 10.1111/joa.12625

Richards, C. T.*, Porro, L. B.*, Collings, A. J.* (2017). Kinematic control of extreme jump angles in the red-legged running frog, Kassina maculata. Journal of Experimental Biology, 220(10), 1894-1904.

Porro, L. B.*, Collings, A. J.*, Eberhard, E. A.*, Chadwick, K. P., & Richards, C. T.* (2017). Inverse dynamic modelling of jumping in the red-legged running frog Kassina maculata. Journal of Experimental Biology, 220(10), 1882-1893.

Clemente, Christofer J., and Christopher Richards. (2013) "Muscle function and hydrodynamics limit power and speed in swimming frogs." Nature communications 4.

Richards, C.T., Clemente, C.J. (2013). Built for rowing: frog muscle is tuned to limb morphology to power swimming. Journal of the Royal Society Interface, 10, 20130236.

Richards, C.T., Sawicki, G.S. (2012). Elastic recoil can either amplify or attenuate muscle–tendon power, depending on inertial vs. ?uid dynamic loading. Journal of Theoretical biology, 313, 68-78 (11p).

Clemente, C.J., Richards, C.T. (2012). Determining the in?uence of muscle operating length on muscle performance during frog swimming using a bio-robotic model. Bioinspiration & Biomimetics, 7, 036018 (12pp).

Richards, C.T., Clemente, C.J. (2012). A bio-robotic platform for integrating internal and external mechanics during muscle-powered swimming. Bioinspiration & Biomimetics, 7, 016010 (15pp).

Richards, C.T., (2011). Building a robotic link between muscle dynamics and hydrodynamics. Journal of Experimental Biology, 214, 2381-2389.

Richards, C.T., (2010). Kinematics and hydrodynamics analysis of swimming anurans reveals striking inter-specific differences in the mechanism for producing thrust. Journal of Experimental Biology, 213, 621-634.

Richards, C.T., (2008). The kinematic determinants of anuran swimming performance:
an inverse and forward dynamics approach. Journal of
Experimental Biology, 211, 3181-3194.

Richards, C.T., Biewener, A.A. (2007). Modulation of in vivo muscle power output
during swimming in the African clawed frog (Xenopus laevis). Journal of
Experimental Biology, 210, 3147-3159.

I teach biomechanics, animal locomotion and comparative anatomy in the BSC programme

I currently work with Tottenham Summer School (LAE Tottenham in London) to contribute biomechanics-related activities.