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1. Incorporation of a smart sock with the virtual immersive test for postural stability

   Burch, RF ; Chander, H ; Saucier, D ; Ball, J ( January 2023 , The American Journal of the Medical Sciences 2023 Southern Regional Meeting)

   Purpose of Study Assessment of an individual's postural stability serves as an indirect measure for both physiological and biomechanical stresses placed on an individual. More recently, some individuals after COVID-19 (SARS-CoV-2) infection have been identified with neurological complaints (Post-Acute Sequelae of Covid - PASC). These individuals can also be predisposed to decreased postural stability and an increased risk for falls. The purpose of the project was to incorporate two different wearable technology (virtual reality (VR) based virtual immersive sensorimotor test - VIST and pressure senor-based smart sock) to assess postural stability among healthy and individuals with PASC to quantify the overall status of the postural control system. Methods Used All methods were conducted based on the University's Institutional Review Board (IRB# 21-296) with informed consent. A total of 12 males and females (six healthy and six with self-reported complaints of PASC) have completed the study so far. All participants were tested using the VIST, while standing on a force platform and wearing the smart sock simultaneously. The (VIST uses a VR headset and proprietary software to test an individual's integrated sensory, motor, and cognitive processes through eight unique tests (smooth pursuits, saccades, convergence, peripheral vision, object discrimination, gaze stability, head-eye coordination, cervical neuromotor control). Center of pressure (COP) data from force platform and pressure sensor data from the smart socks were used to calculate anterior-posterior and medial-lateral postural sway variables. These postural sway variables were analyzed using an independent samples t-test between the healthy and PASC groups at an alpha set at 0.05. Summary of 

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2. Robust Torque Predictions From Electromyography Across Multiple Levels of Active Exoskeleton Assistance Despite Non-linear Reorganization of Locomotor Output

   https://doi.org/10.3389/fnbot.2021.700823  

   George, Jacob A. ; Gunnell, Andrew J. ; Archangeli, Dante ; Hunt, Grace ; Ishmael, Marshall ; Foreman, K. Bo ; Lenzi, Tommaso ( November 2021 , Frontiers in Neurorobotics)

   Robotic exoskeletons can assist humans with walking by providing supplemental torque in proportion to the user's joint torque. Electromyographic (EMG) control algorithms can estimate a user's joint torque directly using real-time EMG recordings from the muscles that generate the torque. However, EMG signals change as a result of supplemental torque from an exoskeleton, resulting in unreliable estimates of the user's joint torque during active exoskeleton assistance. Here, we present an EMG control framework for robotic exoskeletons that provides consistent joint torque predictions across varying levels of assistance. Experiments with three healthy human participants showed that using diverse training data (from different levels of assistance) enables robust torque predictions, and that a convolutional neural network (CNN), but not a Kalman filter (KF), can capture the non-linear transformations in EMG due to exoskeleton assistance. With diverse training, the CNN could reliably predict joint torque from EMG during zero, low, medium, and high levels of exoskeleton assistance [root mean squared error (RMSE) below 0.096 N-m/kg]. In contrast, without diverse training, RMSE of the CNN ranged from 0.106 to 0.144 N-m/kg. RMSE of the KF ranged from 0.137 to 0.182 N-m/kg without diverse training, and did not improve with diverse training. When participant time is limited, training data should emphasize the highest levels of assistance first and utilize at least 35 full gait cycles for the CNN. The results presented here constitute an important step toward adaptive and robust human augmentation via robotic exoskeletons. This work also highlights the non-linear reorganization of locomotor output when using assistive exoskeletons; significant reductions in EMG activity were observed for the soleus and gastrocnemius, and a significant increase in EMG activity was observed for the erector spinae. Control algorithms that can accommodate spatiotemporal changes in muscle activity have broad implications for exoskeleton-based assistance and rehabilitation following neuromuscular injury. 

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3. Electromyography (EMG) Signal Contributions in Speed and Slope Estimation Using Robotic Exoskeletons

   https://doi.org/10.1109/ICORR.2019.8779433  

   Kang, Inseung ; Kunapuli, Pratik ; Hsu, Hsiang ; Young, Aaron J. ( June 2019 , 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR))

   Robotic exoskeletons have the capability to improve community ambulation in aging individuals. These exoskeleton controllers utilize different environmental information such as walking speeds and slope inclines to provide corresponding assistance. Several numerical approaches for estimating this environmental information have been implemented; however, they tend to be limited during dynamic changes. A possible solution is a machine learning model utilizing the user's electromyography (EMG) signals along with mechanical sensor data. We developed a neural network-based walking speed and slope estimator for a powered hip exoskeleton and explored the EMG signal contributions in both static and dynamic settings while wearing the device. We also analyzed the performance of different EMG electrode placements. The resulting machine learning model achieved error rates below 0.08 m/s RMSE and 1.3 RMSE. Our study findings from four able-bodied and two elderly subjects indicate that EMG can improve the performance by reducing the error rate by 14.8% compared to the model using only mechanical sensors. Additionally, results show that using EMG electrode configuration within the exoskeleton interface region is sufficient for the EMG model performance. 

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4. Assessment of Task and Joint-Based Exoskeleton Designs for Elbow Joint Rehabilitation

   https://doi.org/10.1115/DMD2022-1034  

   Delgado, Pablo ; Jaradat, Lieth ; Yihun, Yimesker ( April 2022 , Frontiers in Biomedical Devices)

   Exoskeletons and robots have been used as a common practice to assist and automate rehabilitation exercises. Exoskeleton fitting and alignments are important factors and challenges that need to be addressed for smooth and safe operations and better outcomes. Such challenges often dictate the exoskeleton design approaches. Some focus on simplifying and mimicking human joints (joint-based) while others have a focus on a specific task (task-based), which does not need to align with the corresponding limb joint/s to generate the desired anatomical motion. In this study, the two design approaches are assessed in an elbow flexion-extension task. The muscle responses have been collected and compared with and without the exoskeletons. Based on 6 with no disability participants, the normalized Electromyography (EMG) RMS values are plotted. The plot profiles and magnitudes are used as a base to assess the exoskeleton alignment. For this specific task, the task-based exoskeleton has shown a profile closer to the one without exoskeleton with a relatively identical support as the joint-based one; the latter is evidenced through most subjects’ muscle response magnitudes. This preliminary data has shown a good methodology and insight towards the assessment of exoskeletons, but more human subject data is needed with different task combinations to further strengthen the findings. 

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5. Postural mechanisms in moderate-to-severe cerebral palsy

   https://doi.org/10.1152/jn.00549.2020  

   Goodworth, Adam ; Saavedra, Sandra ( May 2021 , Journal of Neurophysiology)

   null (Ed.)

   People with moderate-to-severe cerebral palsy (CP) have the greatest need for postural control research yet are usually excluded from research due to deficits in sitting ability. We use a support system that allows us to quantify and model postural mechanisms in nonambulatory children with CP. A continuous external bench tilt stimulus was used to evoke trunk postural responses in seven sitting children with CP (ages 2.5 to 13 yr) in several test sessions. Eight healthy adults were also included. Postural sway was analyzed with root mean square (RMS) sway and RMS sway velocity, along with frequency response functions (FRF, gain and phase) and coherence functions across two different stimulus amplitudes. A feedback model (including sensorimotor noise, passive, reflexive, and sensory integration mechanisms) was developed to hypothesize how postural control mechanisms are organized and function. Experimental results showed large RMS sway, FRF gains, and variability compared with adults. Modeling suggested that many subjects with CP adopted “simple” control with major contributions from a passive and reflexive mechanism and only a small contribution from active sensory integration. In contrast, mature trunk postural control includes major contributions from sensory integration and sensory reweighting. Relative to their body size, subjects with CP showed significantly lower damping, three to five times larger corrective torque, and much higher sensorimotor noise compared with the healthy mature system. Results are the first characterization of trunk postural responses and the first attempt at system identification in moderate-to-severe CP, an important step toward developing and evaluating more targeted interventions. NEW & NOTEWORTHY Cerebral palsy (CP) is the most common cause of motor disability in children. People with moderate-to-severe CP are typically nonambulatory and have major impairments in trunk postural control. We present the first systems identification study to investigate postural responses to external stimulus in this population and hypothesize at how the atypical postural control system functions with use of a feedback model. People with moderate-to-severe CP may use a simple control system with significant sensorimotor noise. 

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