Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand

abstract: The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional single-gain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pick-and-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands.View the article as published at https://www.frontiersin.org/articles/10.3389/fnbot.2017.00071/ful

Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand

The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional single-gain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pick-and-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands

Improving Fine Control of Grasping Force during Hand–Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand

The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional singlegain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pickand-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands

Improving Fine Control Of Grasping Force During Hand-Object Interactions For A Soft Synergy-Inspired Myoelectric Prosthetic Hand

The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional singlegain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pickand-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands

Understanding Forearm Muscle Coordination in Children

A combination of surface electromyography (EMG) and pattern recognition algorithms have led to improvements in the functionality of upper limb prosthetics. This method of control relies on user\u27s ability to repeatedly generate consistent muscle contractions. Research in EMG based control of prosthesis has mainly utilized adult subjects who have fully developed neuromuscular control. Little is known about children\u27s ability to generate consistent EMG signals necessary to control artificial limbs with multiple degrees of freedom. To address this gap, two experiments were designed to validate and benchmark an experimental protocol that quantifies the ability to coordinate forearm muscle contractions in able-bodied children across adolescent ages. Able-bodied, healthy adults (n = 8) and children (n = 9) participated in the first experiment that aimed to measure the subject\u27s ability to produce distinguishable EMG signals. Each subject performed 8 repetitions of 16 different hand/wrist movements. We quantify the number of movement types that can be classified by Support Vector Machine with \u3e 90% accuracy. Additional adults (n=8) and children (n=12) were recruited for the second experiment which measured the subjects\u27 ability to control the position of a virtual cursor on a 1-DoF slide using proportional EMG control under three different gain levels. We demonstrated that children had a smaller number of highly independent movements than adults did, due to higher variability. Furthermore, we found that children had higher failure rates and slower average target acquisitions due to increased time-to-target and follow-up correction time. We also found significant correlation between forearm circumference/age and performance. The results of this study provide novel insights into the technical and empirical basis to better understand neuromuscular development in pediatric upper-limb amputees

Evaluation of Individual Finger Forces During Activities of Daily Living In Healthy Individuals and Those with Hand Arthritis

Hand-Osteoarthritis (H-OA) leads to pain, loss of grip strength, and decreased hand function. Current treatment for H-OA involves joint protection programs (JPP) which seek to reduce joint loading during activity. The use of wearable technology to measure hand forces during activity has the potential to determine the effectiveness of JPP. The objective of this thesis was to develop and validate a method of directly measuring finger forces during the performance of activities of daily living, and then use that system to measure the envelope of hand forces during activity in healthy individuals and in patients with H-OA. A commercially-available capacitive sensor system was validated for use in this application and found an envelope of applied forces consistent with previous literature. Using the measurement system and protocols presented in this thesis, the effectiveness of JPP at reducing hand forces can, for the first time, be objectively quantified

The "Federica" hand: a simple, very efficient prothesis

Hand prostheses partially restore hand appearance and functionalities. Not everyone can afford expensive prostheses and many low-cost prostheses have been proposed. In particular, 3D printers have provided great opportunities by simplifying the manufacturing process and reducing costs. Generally, active prostheses use multiple motors for fingers movement and are controlled by electromyographic (EMG) signals. The "Federica" hand is a single motor prosthesis, equipped with an adaptive grasp and controlled by a force-myographic signal. The "Federica" hand is 3D printed and has an anthropomorphic morphology with five fingers, each consisting of three phalanges. The movement generated by a single servomotor is transmitted to the fingers by inextensible tendons that form a closed chain; practically, no springs are used for passive hand opening. A differential mechanical system simultaneously distributes the motor force in predefined portions on each finger, regardless of their actual positions. Proportional control of hand closure is achieved by measuring the contraction of residual limb muscles by means of a force sensor, replacing the EMG. The electrical current of the servomotor is monitored to provide the user with a sensory feedback of the grip force, through a small vibration motor. A simple Arduino board was adopted as processing unit. The differential mechanism guarantees an efficient transfer of mechanical energy from the motor to the fingers and a secure grasp of any object, regardless of its shape and deformability. The force sensor, being extremely thin, can be easily embedded into the prosthesis socket and positioned on both muscles and tendons; it offers some advantages over the EMG as it does not require any electrical contact or signal processing to extract information about the muscle contraction intensity. The grip speed is high enough to allow the user to grab objects on the fly: from the muscle trigger until to the complete hand closure, "Federica" takes about half a second. The cost of the device is about 100 US$. Preliminary tests carried out on a patient with transcarpal amputation, showed high performances in controlling the prosthesis, after a very rapid training session. The "Federica" hand turned out to be a lightweight, low-cost and extremely efficient prosthesis. The project is intended to be open-source: all the information needed to produce the prosthesis (e.g. CAD files, circuit schematics, software) can be downloaded from a public repository. Thus, allowing everyone to use the "Federica" hand and customize or improve it

Evoked Somatosensory Feedback for Closed-Loop Control of Prosthetic Hand

Somatosensory feedback, such as tactile and proprioceptive feedback, is essential to our daily sensorimotor tasks. A lack of sensory information limits meaningful human-machine interactions. Different somatosensory feedback strategies have been developed in recent years. Non-invasive sensory substitutional approaches often evoke sensations that are unintuitive, requiring extensive sensory training. Alternatively, invasive neural stimulation can elicit intuitive percepts that are interpretable readily by prosthetic hand users; however, the invasive nature of the procedure limits wide clinical applications. To overcome these issues, we developed a multimodal sensory feedback approach that delivers tactile and proprioceptive information non-invasively. We used a skin-surface nerve stimulation array to target afferent fibers in the peripheral nerves, which can elicit intuitive tactile feedback at the fingertips. We used a vibrotactile array to deliver proprioceptive percepts encoding kinematic information of prosthetic joints. First, we evaluated whether the peripheral nerve stimulation technique could be used for the recognition of object properties. Evoked tactile sensations were modulated using forces recorded by a sensorized prosthesis not actively controlled by the users. We demonstrated that the elicited tactile sensation at the fingertips can enable recognition of object shape and surface topology. Second, we evaluated how evoked tactile feedback can be integrated into the functional utility of a prosthetic hand. We quantified the benefits of tactile feedback under different myoelectric control strategies, when participants performed an object manipulation task. We showed an improved task success rate and reduced muscle activation effort when tactile feedback was provided. Finally, we investigated whether multimodal (tactile and proprioceptive) feedback can enable the recognition of more complex object properties during active control of a prosthetics hand. We found that integrated tactile and proprioceptive feedback allowed for simultaneous recognition of multiple object properties (size and stiffness) in individuals with and without an arm amputation. Overall, this work demonstrates that artificially evoked somatosensory feedback can be utilized effectively to improve the closed-loop control of prostheses. These outcomes highlight the critical role of somatosensory feedback during human-machine interactions, which can enhance functional utility of prosthetic devices and promote user experience and confidence.Doctor of Philosoph