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1. A Compliant, Underactuated Finger for Anthropomorphic Hands

   https://doi.org/10.1109/ICORR.2019.8779435  

   Kontoudis, George P. ; Liarokapis, Minas ; Vamvoudakis, Kyriakos G. ( June 2019 , 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR))

   This paper presents a compliant, underactuated finger for the development of anthropomorphic robotic and prosthetic hands. The finger achieves both flexion/extension and adduction/abduction on the metacarpophalangeal joint, by using two actuators. The design employs moment arm pulleys to drive the tendon laterally and amplify the abduction motion, while also maintaining the flexion motion. Particular emphasis has been given to the analysis of the mechanism. The proposed finger has been fabricated with the hybrid deposition manufacturing technique and the actuation mechanism's efficiency has been validated with experiments that include the computation of the reachable workspace, the assessment of the exerted forces at the fingertip, the demonstration of the feasible motions, and the presentation of the grasping and manipulation capabilities. The proposed mechanism facilitates the collaboration of the two actuators to increase the exerted finger forces. Moreover, the extended workspace allows the execution of dexterous manipulation tasks. 

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2. Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information

   https://doi.org/10.1109/EMBC44109.2020.9175236  

   Zheng, Yang ; Hu, Xiaogang ( July 2020 , Proceedings of IEEE Engineering in Medicine and Biology Society Annual Meeting)

   null (Ed.)

   With the development of advanced robotic hands, a reliable neural-machine interface is essential to take full advantage of the functional dexterity of the robots. In this preliminary study, we developed a novel method to estimate isometric forces of individual fingers continuously and concurrently during dexterous finger flexion and extension. Specifically, motor unit (MU) discharge activity was extracted from the surface high-density electromyogram (EMG) signals recorded from the finger extensors and flexors, respectively. The MU information was separated into different groups to be associated with the flexion or extension of individual fingers and was then used to predict individual finger forces during multi-finger flexion and extension tasks. Compared with the conventional EMG amplitude-based method, our method can obtain a better force estimation performance (a higher correlation and a smaller estimation error between the predicted and the measured force) when a linear regression model was used. Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands. 

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3. Activation of Superficial and Deep Finger Flexors through Transcutaneous Nerve Stimulation

   https://doi.org/10.1109/JBHI.2020.3041669  

   Shin, Henry ; Hawari, Marwan A. ; Hu, Xiaogang ( October 2020 , IEEE Journal of Biomedical and Health Informatics)

   null (Ed.)

   Objective: Functional electrical stimulation (FES) is a common technique to elicit muscle contraction and help improve muscle strength. Traditional FES over the muscle belly typically only activates superficial muscle regions. In the case of hand FES, this prevents the activation of the deeper flexor muscles which control the distal finger joints. Here, we evaluated whether an alternative transcutaneous nerve-bundle stimulation approach can activate both superficial and deep extrinsic finger flexors using a high-density stimulation grid. Methods: Transverse ultrasound of the forearm muscles was used to obtain cross-sectional images of the underlying finger flexors during stimulated finger flexions and kinematically-matched voluntary motions. Finger kinematics were recorded, and an image registration method was used to capture the large deformation of the muscle regions during each flexion. This deformation was used as a surrogate measure of the contraction of muscle tissue, and the regions of expanding tissue can identify activated muscles. Results: The nerve-bundle stimulation elicited contractions in the superficial and deep finger flexors. Both separate and concurrent activation of these two muscles were observed. Joint kinematics of the fingers also matched the expected regions of muscle contractions. Conclusions: Our results showed that the nerve-bundle stimulation technique can activate the deep extrinsic finger flexors, which are typically not accessible via traditional surface FES. Significance: Our nerve-bundle stimulation method enables us to produce the full range of motion of different joints necessary for various functional grasps, which could benefit future neuroprosthetic applications. 

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4. Concurrent Estimation of Finger Flexion and Extension Forces using Motoneuron Discharge Information

   https://doi.org/10.1109/TBME.2021.3056930  

   Zheng, Yang ; Hu, Xiaogang ( April 2021 , IEEE Transactions on Biomedical Engineering)

   null (Ed.)

   Objective: A reliable neural-machine interface offers the possibility of controlling advanced robotic hands with high dexterity. The objective of this study was to develop a decoding method to estimate flexion and extension forces of individual fingers concurrently. Methods: First, motor units (MUs) firing information were identified through surface electromyogram (EMG) decomposition, and the MUs were further categorized into different pools for the flexion and extension of individual fingers via a refinement procedure. MU firing rate at the populational level was calculated, and the individual finger forces were then estimated via a bivariate linear regression model (neural-drive method). Conventional EMG amplitude-based method was used as a comparison. Results: Our results showed that the neural-drive method had a significantly better performance (lower estimation error and higher correlation) compared with the conventional method. Conclusion: Our approach provides a reliable neural decoding method for dexterous finger movements. Significance: Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands. 

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5. Knee Acoustic Emissions as a Digital Biomarker of Disease Status in Juvenile Idiopathic Arthritis

   https://doi.org/10.3389/fdgth.2020.571839  

   Whittingslow, Daniel C. ; Zia, Jonathan ; Gharehbaghi, Sevda ; Gergely, Talia ; Ponder, Lori A. ; Prahalad, Sampath ; Inan, Omer T. ( November 2020 , Frontiers in Digital Health)

   null (Ed.)

   In this paper, we quantify the joint acoustic emissions (JAEs) from the knees of children with juvenile idiopathic arthritis (JIA) and support their use as a novel biomarker of the disease. JIA is the most common rheumatic disease of childhood; it has a highly variable presentation, and few reliable biomarkers which makes diagnosis and personalization of care difficult. The knee is the most commonly affected joint with hallmark synovitis and inflammation that can extend to damage the underlying cartilage and bone. During movement of the knee, internal friction creates JAEs that can be non-invasively measured. We hypothesize that these JAEs contain clinically relevant information that could be used for the diagnosis and personalization of treatment of JIA. In this study, we record and compare the JAEs from 25 patients with JIA−10 of whom were recorded a second time 3–6 months later—and 18 healthy age- and sex-matched controls. We compute signal features from each of those record cycles of flexion/extension and train a logistic regression classification model. The model classified each cycle as having JIA or being healthy with 84.4% accuracy using leave-one-subject-out cross validation (LOSO-CV). When assessing the full JAE recording of a subject (which contained at least 8 cycles of flexion/extension), a majority vote of the cycle labels accurately classified the subjects as having JIA or being healthy 100% of the time. Using the output probabilities of a JIA class as a basis for a joint health score and test it on the follow-up patient recordings. In all 10 of our 6-week follow-up recordings, the score accurately tracked with successful treatment of the condition. Our proposed JAE-based classification model of JIA presents a compelling case for incorporating this novel joint health assessment technique into the clinical work-up and monitoring of JIA. 

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