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1. Neuromorphic vision and tactile fusion for upper limb prosthesis control

   https://doi.org/10.1109/NER.2019.8716890  

   Hays, Mark; Osborn, Luke; Ghosh, Rohan; Iskarous, Mark; Hunt, Christopher; Thakor, Nitish V. (March 2019, IEEE Transactions on Biomedical Circuits and Systems)

   null (Ed.)

   A major issue with upper limb prostheses is the disconnect between sensory information perceived by the user and the information perceived by the prosthesis. Advances in prosthetic technology introduced tactile information for monitoring grasping activity, but visual information, a vital component in the human sensory system, is still not fully utilized as a form of feedback to the prosthesis. For able-bodied individuals, many of the decisions for grasping or manipulating an object, such as hand orientation and aperture, are made based on visual information before contact with the object. We show that inclusion of neuromorphic visual information, combined with tactile feedback, improves the ability and efficiency of both able-bodied and amputee subjects to pick up and manipulate everyday objects.We discovered that combining both visual and tactile information in a real-time closed loop feedback strategy generally decreased the completion time of a task involving picking up and manipulating objects compared to using a single modality for feedback. While the full benefit of the combined feedback was partially obscured by experimental inaccuracies of the visual classification system, we demonstrate that this fusion of neuromorphic signals from visual and tactile sensors can provide valuable feedback to a prosthetic arm for enhancing real-time function and usability. 

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2. First Demonstration of Functional Task Performance Using a Sonomyographic Prosthesis: A Case Study

   https://doi.org/10.3389/fbioe.2022.876836  

   Engdahl, Susannah M.; Acuña, Samuel A.; King, Erica L.; Bashatah, Ahmed; Sikdar, Siddhartha (May 2022, Frontiers in Bioengineering and Biotechnology)

   Ultrasound-based sensing of muscle deformation, known as sonomyography, has shown promise for accurately classifying the intended hand grasps of individuals with upper limb loss in offline settings. Building upon this previous work, we present the first demonstration of real-time prosthetic hand control using sonomyography to perform functional tasks. An individual with congenital bilateral limb absence was fitted with sockets containing a low-profile ultrasound transducer placed over forearm muscle tissue in the residual limbs. A classifier was trained using linear discriminant analysis to recognize ultrasound images of muscle contractions for three discrete hand configurations (rest, tripod grasp, index finger point) under a variety of arm positions designed to cover the reachable workspace. A prosthetic hand mounted to the socket was then controlled using this classifier. Using this real-time sonomyographic control, the participant was able to complete three functional tasks that required selecting different hand grasps in order to grasp and move one-inch wooden blocks over a broad range of arm positions. Additionally, these tests were successfully repeated without retraining the classifier across 3 hours of prosthesis use and following simulated donning and doffing of the socket. This study supports the feasibility of using sonomyography to control upper limb prostheses in real-world applications. 

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3. Texture Discrimination with a Soft Biomimetic Finger Using a Flexible Neuromorphic Tactile Sensor Array That Provides Sensory Feedback

   https://doi.org/10.1089/soro.2020.0016  

   Sankar, Sriramana; Balamurugan, Darshini; Brown, Alisa; Ding, Keqin; Xu, Xingyuan; Low, Jin Huat; Yeow, Chen Hua; Thakor, Nitish (September 2020, Soft Robotics)

   null (Ed.)

   The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over 16 independent parameters when tested on 13 standardized textured surfaces. The 16 parameters were the combination of 4 angles of flexion of the soft finger and 4 speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provide sensory feedback; furthermore, texture feedback has the potential to enhance user experience when interacting with their surroundings. 

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4. Stiffness Perception using Transcutaneous Electrical Stimulation during Active and Passive Prosthetic Control

   https://doi.org/10.1109/EMBC44109.2020.9176078  

   Vargas, Luis; Huang, Helen; Zhu, Yong; Hu, Xiaogang (July 2020, International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   null (Ed.)

   Haptic feedback allows an individual to identify various object properties. In this preliminary study, we determined the performance of stiffness recognition using transcutaneous nerve stimulation when a prosthetic hand was moved passively or was controlled actively by the subjects. Using a 2×8 electrode grid placed along the subject's upper arm, electrical stimulation was delivered to evoke somatotopic sensation along their index finger. Stimulation intensity, i.e. sensation strength, was modulated using the fingertip forces from a sensorized prosthetic hand. Object stiffness was encoded based on the rate of change of the evoked sensation as the prosthesis grasped one of three objects of different stiffness levels. During active control, sensation was modulated in real time as recorded forces were converted to stimulation amplitudes. During passive control, prerecorded force traces were randomly selected from a pool. Our results showed that the accuracy of object stiffness recognition was similar in both active and passive conditions. A slightly lower accuracy was observed during active control in one subject, which indicated that the sensorimotor integration processes could affect haptic perception for some users. 

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5. Functional evaluation of a real-time EMG controlled prosthetic hand

   https://doi.org/10.1017/wtc.2025.7  

   Kalita, Amlan Jyoti; Chanu, Maibam Pooya; Kakoty, Nayan M; Vinjamuri, Ramana Kumar; Borah, Satyajit (January 2025, Wearable Technologies)

   Abstract Electromyogram (EMG)-controlled prosthetic hands have advanced significantly during the past two decades. However, most of the currently available prosthetic hands fail to replicate human hand functionality and controllability. To measure the emulation of the human hand by a prosthetic hand, it is important to evaluate the functional characteristics. Moreover, incorporating feedback from end users during clinical testing is crucial for the precise assessment of a prosthetic hand. The work reported in this manuscript unfolds the functional characteristics of an EMG-CoNtrolled PRosthetIC Hand called ENRICH. ENRICH is a real-time EMG controlled prosthetic hand that can grasp objects in 250.8$$ \pm $$1.1 ms, fulfilling the neuromuscular constraint of a human hand. ENRICH is evaluated in comparison to 26 laboratory prototypes and 10 commercial variants of prosthetic hands. The hand was evaluated in terms of size, weight, operation time, weight lifting capacity, finger joint range of motion, control strategy, degrees of freedom, grasp force, and clinical testing. The box and block test and pick and place test showed ENRICH’s functionality and controllability. The functional evaluation reveals that ENRICH has the potential to restore functionality to hand amputees, improving their quality of life. 

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