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1. Rabbit hindlimb kinematics and ground contact kinetics during the stance phase of gait

   https://doi.org/10.7717/peerj.13611  

   Hall, Patrick ; Stubbs, Caleb ; Anderson, David E. ; Greenacre, Cheryl ; Crouch, Dustin L. ( January 2022 , PeerJ)

   Though the rabbit is a common animal model in musculoskeletal research, there are very limited data reported on healthy rabbit biomechanics. Our objective was to quantify the normative hindlimb biomechanics (kinematics and kinetics) of six New Zealand White rabbits (three male, three female) during the stance phase of gait. We measured biomechanics by synchronously recording sagittal plane motion and ground contact pressure using a video camera and pressure-sensitive mat, respectively. Both foot angle ( i.e ., angle between foot and ground) and ankle angle curves were unimodal. The maximum ankle dorsiflexion angle was 66.4 ± 13.4° (mean ± standard deviation across rabbits) and occurred at 38% stance, while the maximum ankle plantarflexion angle was 137.2 ± 4.8° at toe-off (neutral ankle angle = 90 degrees). Minimum and maximum foot angles were 17.2 ± 6.3° at 10% stance and 123.3 ± 3.6° at toe-off, respectively. The maximum peak plantar pressure and plantar contact area were 21.7 ± 4.6% BW/cm 2 and 7.4 ± 0.8 cm 2 respectively. The maximum net vertical ground reaction force and vertical impulse, averaged across rabbits, were 44.0 ± 10.6% BW and 10.9 ± 3.7% BW∙s, respectively. Stance duration (0.40 ± 0.15 s) was statistically significantly correlatedmore »( p < 0.05) with vertical impulse (Spearman’s ρ = 0.76), minimum foot angle ( ρ = −0.58), plantar contact length ( ρ = 0.52), maximum foot angle ( ρ = 0.41), and minimum foot angle ( ρ = −0.30). Our study confirmed that rabbits exhibit a digitigrade gait pattern during locomotion. Future studies can reference our data to quantify the extent to which clinical interventions affect rabbit biomechanics.« less
2. Efficient Digital Modeling and Fabrication Workflow for Individualized Ankle Exoskeletons

   https://doi.org/10.1115/IMECE2021-70603  

   Gebre, Biruk A. ; Nogueira, Rodrigo ; Patidar, Shubham ; Belle-Isle, Robert ; Nolan, Karen J. ; Pochiraju, Kishore ; Zanotto, Damiano ( January 2022 , Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition)

   We introduce a new design method to tailor the physical structure of a powered ankle-foot orthosis to the wearer’s leg morphology and improve fit. We present a digital modeling and fabrication workflow that combines scan-based design, parametric configurable modeling, and additive manufacturing (AM) to enable the efficient creation of personalized ankle-foot orthoses with minimal lead-time and explicit inputs. The workflow consists of an initial one-time generic modeling step to generate a parameterized design that can be rapidly configured to customizable shapes and sizes using a design table. This step is then followed by a wearer-specific personalization step that consists of performing a 3D scan of the wearer’s leg, extracting key parameters of the wearer’s leg morphology, generating a personalized design using the configurable parametric design, and digital fabrication of the individualized ankle-foot orthosis using additive manufacturing. The paper builds upon the design of the Stevens Ankle-Foot Electromechanical (SAFE) orthosis presented in prior work and introduces a new, individualized structural design (SAFE II orthosis) that is modeled and fabricated using the presented digital workflow. The workflow is demonstrated by designing a personalized ankle-foot orthosis for an individual based on 3D scan data and printing a personalized design to perform preliminary fitmore »testing. Implications of the presented methodology for the design and fabrication of future personalized powered orthoses are discussed, along with avenues for future work.« less
3. Reinforcement Learning Assist-as-needed Control for Robot Assisted Gait Training

   https://doi.org/10.1109/BioRob49111.2020.9224392  

   Zhang, Yufeng ; Li, Shuai ; Nolan, Karen J. ; Zanotto, Damiano ( October 2020 , 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob))

   The primary goal of an assist-as-needed (AAN) controller is to maximize subjects' active participation during motor training tasks while allowing moderate tracking errors to encourage human learning of a target movement. Impedance control is typically employed by AAN controllers to create a compliant force-field around the desired motion trajectory. To accommodate different individuals with varying motor abilities, most of the existing AAN controllers require extensive manual tuning of the control parameters, resulting in a tedious and time-consuming process. In this paper, we propose a reinforcement learning AAN controller that can autonomously reshape the force-field in real-time based on subjects' training performances. The use of action-dependent heuristic dynamic programming enables a model-free implementation of the proposed controller. To experimentally validate the controller, a group of healthy individuals participated in a gait training session wherein they were asked to learn a modified gait pattern with the help of a powered ankle-foot orthosis. Results indicated the potential of the proposed control strategy for robot-assisted gait training.
4. Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation

   Zhang, Q. ( October 2022 , Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation)

   Functional electrical stimulation (FES) is a potential technique for reanimating paralyzed muscles post neurological injury/disease. Several technical challenges including difficulty in measuring and compensating for delayed muscle activation levels inhibit its satisfactory control performance. In this paper, an ultrasound (US) imaging approach is proposed to measure delayed muscle activation levels under the implementation of FES. Due to low sampling rates of US imaging, a sampled data observer (SDO) is designed to estimate the muscle activation in a continuous manner. The SDO is combined with continuous-time dynamic surface control (DSC) approach that compensates for the electromechanical delay (EMD) in the tibialis anterior (TA) activation dynamics. The stability analysis based on the Lyapunov-Krasovskii function proves that the SDO-based DSC plus delay compensation (SDO-DSC-DC) approach achieves semi-global uniformly ultimately bounded (SGUUB) tracking performance. Simulation results on an ankle dorsiflexion neuromusculoskeletal system show the root mean square error (RMSE) of desired trajectory tracking is reduced by 19.77 % by using the proposed SDO-DSC-DC compared to the DSC-DC without the SDO. The findings provide potentials for rehabilitative devices, like powered exoskeleton and FES, to assist or enhance human limb movement based on the corresponding muscle activities in real-time.
5. Closing the Wearable Gap—Part III: Use of Stretch Sensors in Detecting Ankle Joint Kinematics During Unexpected and Expected Slip and Trip Perturbations

   https://doi.org/10.3390/electronics8101083  

   Chander, Harish ; Stewart, Ethan ; Saucier, David ; Nguyen, Phuoc ; Luczak, Tony ; Ball, John E. ; Knight, Adam C. ; Smith, Brian K. ; V, Reuben F. ; Prabhu, R. K. ( October 2019 , Electronics)

   Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0more »degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips.« less