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1. Restoring arm function with a soft robotic wearable for individuals with amyotrophic lateral sclerosis

   https://doi.org/10.1126/scitranslmed.add1504  

   Proietti, Tommaso ; O’Neill, Ciaran ; Gerez, Lucas ; Cole, Tazzy ; Mendelowitz, Sarah ; Nuckols, Kristin ; Hohimer, Cameron ; Lin, David ; Paganoni, Sabrina ; Walsh, Conor ( February 2023 , Science Translational Medicine)

   Despite promising results in the rehabilitation field, it remains unclear whether upper limb robotic wearables, e.g., for people with physical impairments resulting from neurodegenerative disease, can be made portable and suitable for everyday use. We present a lightweight, fully portable, textile-based, soft inflatable wearable robot for shoulder elevation assistance that provides dynamic active support to the upper limbs. The technology is mechanically transparent when unpowered, can quantitatively assess free movement of the user, and adds only 150 grams of weight to each upper limb. In 10 individuals with amyotrophic lateral sclerosis (ALS) with different degrees of neuromuscular impairment, we demonstrated immediate improvement in the active range of motion and compensation for continuing physical deterioration in two individuals with ALS over 6 months. Along with improvements in movement, we show that this robotic wearable can improve functional activity without any training, restoring performance of basic activities of daily living. In addition, a reduction in shoulder muscle activity and perceived muscular exertion, coupled with increased endurance for holding objects, highlight the potential of this device to mitigate the impact of muscular fatigue for patients with ALS. These results represent a further step toward everyday use of assistive, soft, robotic wearables for the upper limbs.

    

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2. Design of a Hybrid SMA-Pneumatic based Wearable Upper Limb Exoskeleton

   Alireza Golgouneh, Eric Beaudette ( September 2021 , International Symposium on Wearable Computers Design Exhibition)

   null (Ed.)

   Upper limb mobility impairments affect individuals at all life stages. Exoskeletons can assist in rehabilitation as well as performing Activities of Daily Living (ADL). Most commercial assistive devices still rely on rigid robotics with constrained biomechanical degrees of freedom that may even increase user exertion. Therefore, this paper discusses the iterative design and development of a novel hybrid pneumatic actuation and Shape Memory Alloy (SMA) based wearable soft exoskeleton to assist in shoulder abduction and horizontal flexion/extension movements, with integrated soft strain sensing to track shoulder joint motion. The garment development was done in two stages which involved creating (1) SMA actuators integrated with soft sensing, and (2) integrating pneumatic actuation. The final soft exoskeleton design was developed based on the insights gained from two prior prototypes in terms of wearability, usability, comfort, and functional specifications (i.e., placement and number) of the sensors and actuators. The final exoskeleton is a modular, multilayer garment which uses a hybrid and customizable actuation strategy (SMA and inflatable pneumatic bladder). 

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3. A six degrees-of-freedom cable-driven robotic platform for head–neck movement

   https://doi.org/10.1038/s41598-024-59349-0  

   Bales, Ian ; Zhang, Haohan ( April 2024 , Scientific Reports)

   This paper introduces a novel cable-driven robotic platform that enables six degrees-of-freedom (DoF) natural head–neck movements. Poor postural control of the head–neck can be a debilitating symptom of neurological disorders such as amyotrophic lateral sclerosis and cerebral palsy. Current treatments using static neck collars are inadequate, and there is a need to develop new devices to empower movements and facilitate physical rehabilitation of the head–neck. State-of-the-art neck exoskeletons using lower DoF mechanisms with rigid linkages are limited by their hard motion constraints imposed on head–neck movements. By contrast, the cable-driven robot presented in this paper does not constrain motion and enables wide-range, 6-DoF control of the head–neck. We present the mechatronic design, validation, and control implementations of this robot, as well as a human experiment to demonstrate a potential use case of this versatile robot for rehabilitation. Participants were engaged in a target reaching task while the robot applied both assistive and resistive moments on the head during the task. Our results show that neck muscle activation increased by 19% when moving the head against resistance and decreased by 28–43% when assisted by the robot. Overall, these results provide a scientific justification for further research in enabling movement and identifying personalized rehabilitation for motor training. Beyond rehabilitation, other applications such as applying force perturbations on the head to study sensory integration and applying traction to achieve pain relief may benefit from the innovation of this robotic platform which is capable of applying controlled 6-DoF forces/moments on the head.

    

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4. A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control

   https://doi.org/10.1017/wtc.2022.18  

   Zhang, Qiang ; Fragnito, Natalie ; Bao, Xuefeng ; Sharma, Nitin ( January 2022 , Wearable Technologies)

   Abstract Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging’s region of interest (ROI) on the net plantarflexion moment prediction performance. We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging + deep learning approach for net joint moment prediction. With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% ( $ p $  < .001) and increased the prediction coefficient of determination by 20.13% ( $ p $  < .001). The findings show that the US imaging + deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assist-as-needed control strategy. 

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5. Transmission Comparison for Cooperative Robotic Applications

   https://doi.org/10.3390/act10090203  

   Nandor, Mark J. ; Heebner, Maryellen ; Quinn, Roger ; Triolo, Ronald J. ; Makowski, Nathaniel S. ( September 2021 , Actuators)

   null (Ed.)

   The development of powered assistive devices that integrate exoskeletal motors and muscle activation for gait restoration benefits from actuators with low backdrive torque. Such an approach enables motors to assist as needed while maximizing the joint torque muscles, contributing to movement, and facilitating ballistic motions instead of overcoming passive dynamics. Two electromechanical actuators were developed to determine the effect of two candidate transmission implementations for an exoskeletal joint. To differentiate the transmission effects, the devices utilized the same motor and similar gearing. One actuator included a commercially available harmonic drive transmission while the other incorporated a custom designed two-stage planetary transmission. Passive resistance and mechanical efficiency were determined based on isometric torque and passive resistance. The planetary-based actuator outperformed the harmonic-based actuator in all tests and would be more suitable for hybrid exoskeletons. 

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